Modern Insurance Magazine Issue 72

ISSUE

72

ISSN 2515-3803

AI: Promise and Peril

Just a Thought

with Eddie

Longworth:

All that Glistens

Can be Gold - Possibly

Artificial

Intelligence

(AI) is

Transforming

the Insurance

Industry

Charles Skamser,

CEO, PX42.ai

Bias Isn’t Just

a Bug – It’s a

Mirror”:

Why Human Oversight

Is Essential in

Insurance AI

AI with

Integrity

In Conversation

with Katie King

INSUR.TECH.

TALK

Insurtech Insights

2025 Contributors

Media Partners

WELCOME

Hello readers,

AI is no longer a buzzword on the horizon, it’s here,

it’s evolving fast, and it’s already reshaping the way

insurance operates. In this edition of Modern Insurance

Magazine, we explore the real impact of artificial

intelligence across every corner of the industry, from

underwriting and fraud detection to customer experience

and claims transformation.

Hayley Dalton, Editor

We begin on page 8 with Charles Skamser of PX42.ai, who shares how AI is

driving rapid change – and what businesses must do to stay ahead. On page

12, Katie King explores how companies can embrace AI with purpose, while

Sagacity challenges us to consider AI’s ethical implications and the need for

strong human oversight (p. 14).

But it’s not all tech and transformation – this issue also delves into the beating

heart of insurance: people, trust, and service. On page 45, I Love Claims

discuss how AI can support better outcomes for customers. You’ll also find

thought-provoking commentary from Eddie Longworth (p. 55) and practical

perspectives from the likes of the London Prosthetic Centre (p. 46) and

Accommodation Connect (p. 49).

Our Editorial Board returns on page 19 with a rich and varied collection of

viewpoints on the operational, ethical, and customer service challenges posed

by AI. Then, on page 37, we hear from our resident associations on the pressing

issues they’re tackling – from Civil Liability Act reform to building trust in a

digital-first future.

Rachael Pearson, Events & Sales Manager

Emily Birks, Project Manager

Racheal Pearson

Events & Sales Manager

Modern Insurance Magazine

rachael.pearson@charltongrant.co.uk

Emily Birks

Project Manager


emily.birks@charltongrant.co.uk

On page 61, The Fraud Board brings together leading voices in counter-fraud

to examine how technology is being used to fight smarter, not just harder. And

don’t miss our Insur.Tech.Talk special starting on page 70, with brilliant insights

from Solera, Zinnia, Nassau Financial Group and more – all brought together in

partnership with the wonderful Megan Kuczynski.

With so much changing so quickly, this issue invites reflection as much as

innovation. Where do we go from here? How do we harness technology

without losing the human touch? And what do we need to do – today – to

build trust that lasts?

Let’s explore it together.

Hayley

Hayley Dalton

Editor,

Modern Insurance Magazine.

hayley@charltongrant.co.uk

ISSUE 72

ISSN 2515-3803

Editor

Hayley Dalton

Project Manager & Events Sales

Emily Birks


is published by Charlton Grant Ltd ©2025

All material is copyrighted both written and illustrated. Reproduction in part or whole is strictly

forbidden without the written permission of the publisher. All images and information is collated

from extensive research and along with advertisements is published in good faith. Although the

author and publisher have made every effort to ensure that the information in this publication

was correct at press time, the author and publisher do not assume and hereby disclaim any

liability to any party for any loss, damage, or disruption caused by errors or omissions, whether

such errors or omissions result from negligence, accident, or any other cause.

MODERN INSURANCE | 3

Contents

8

61

12

70

14

4 | MODERN INSURANCE

8

12

14

19

37

45

46

49

51

55

57

59

Insight

Charles Skamser, CEO, PX42.ai - Artificial

Intelligence (AI) is Transforming the

Insurance Industry

Interviews

AI with Integrity: In Conversation with Katie

King

“Bias Isn’t Just a Bug – It’s a Mirror”: Why

Human Oversight Is Essential in Insurance AI

Editorial Board

Find out what our editorial board panel of

industry experts have to say in this edition

of Modern Insurance Magazine.

Associations

Assemble

Modern Insurance’s panel of resident

associations outline the burning issues from

their unique area of the industry.

Features

I Love Claims: Harnessing AI for a better

tomorrow

Transforming Prosthetic Care: London

Prosthetic Centre’s New Facility and What it

Means For You

Introducing Accommodation Connect

AI in Preventive Care: Why Insurers Should

Look Beneath the Skin

Just a Thought with Eddie Longworth: All

that Glistens Can be Gold - Possibly

Electrical Resistivity Tomography (ERT):

Principles and Application in Ground

Consolidation Monitoring

FibriCheck’s AI technology: Transforming

heart health management for your

members

61

68

Fraud Board

Don’t miss our regular instalment of The Fraud

Board, where our collective of fraud experts

convene to discuss the key factors affecting

the fight against fraud in today’s modern

insurance landscape.

10 Minutes with...

10 Minutes with… Chat GPT

Insur.Tech.Talk

Interviews

70 Welcome

Megan Kuczynski, Senior Strategic

Advisor, Insurtech Insights

Founder & CEO, ClimateTech Connect

71

Nassau Financial Group

Laura Dinan Haber, Innovation Brand Director,

Nassau Financial Group

73 Zinnia

Paul Tyler, Head of Enterprise Marketing and

Marketing Innovation, Zinnia

74 Solera

Jing Liao, Chairwoman of The Solera

Foundation and Chief Administration Officer of

Solera Corp

77

Insur.Tech.Talk

Editorial Board

Experts from the insurtech sector join us once

more to share their unique insights!

INSUR.TECH.TALK BOARD

Disclaimer: Our publications contain advertising material submitted by third parties. Each individual advertiser is solely responsible for the content of its advertising material.

We accept no responsibility for the content of advertising material, including, without limitation, any error, omission or inaccuracy therein. We do not endorse, and are not

responsible or liable for, any advertising or products in such advertising, nor for any any damage, loss or offence caused or alleged to be caused by, or in connection with, the

use of or reliance on any such advertising or products in such advertising.


Editorial Board

19

21

23

25

NO QUICK WINS:

NAVIGATING THE REAL-

WORLD CHALLENGES OF AI

ADOPTION IN INSURANCE

Darren Hall, Chief Operating Officer,

Carpenters Group

THE NEXT PHASE OF AI IN

INSURANCE SOFTWARE

Will Prest, Product Manager,

ParaCode

HOW DIGILOG UK

CHAMPIONS ETHICAL,

TRANSPARENT, AND

RESPONSIBLE USE OF

ADVANCED TECHNOLOGIES

IN INSURANCE

Lior Koskas, CEO, Digilog UK

BALANCING BOTS AND

BRAINS: HOW FMG IS

USING AI TO ENHANCE,

NOT REPLACE, HUMAN

EXPERTISE

Tracey Sherrard, Head of Business

Change, FMG

THE ROLE OF VEHICLE

RECOVERY OPERATORS

(VROS) AND THE POTENTIAL

OF AI IN THE INSURANCE

INDUSTRY

Jason Brice, Managing Director,

CMG

REVOLUTIONISING MOBILE

REPAIRS: RRN’S BESPOKE

AI TOOL DELIVERS

SPEED, ACCURACY, AND

SUSTAINABILITY

Simon Downing, Strategic Key

Accounts Manager, Rapid Repair

Network Limited

DON’T FORGET THE

CLAIMANT

Mia Constable, Head of Business

Development, e2e

27

29

31

33

RECLAIMING TIME,

RESTORING FOCUS: HOW AI

IS PUTTING PEOPLE BACK

AT THE CENTRE OF CLAIMS

MANAGEMENT

Nik Ellis, Director, Laird Assessors

POWERING THE FUTURE

OF CLAIMS: COPART’S AI-

DRIVEN APPROACH TO

SEAMLESS SERVICE

Jane Pocock, CEO, Copart UK &

Ireland

HARNESSING AI TO

ENHANCE SERVICE AND

EFFICIENCY WITHIN THE

CLAIMS PROCESS

Greg Laker, Director of QuestGates

THE EVOLVING ROLE OF AI

IN WITNESS STATEMENT

TAKING, TRANSLATION, AND

CLIENT CONTACT

Gilly Daniels, Managing Director,

Witness Wise

DRIVING EFFICIENCY: HOW

AI IS GAINING TRACTION IN

MOTOR CLAIMS

Phillip Witterick, Commercial

Director, Auxillis

AI IN THE LEGAL SECTOR

Mark Savill, Managing Director,

Lyons Davidson

REVOLUTIONISING THE

CUSTOMER EXPERIENCE

THROUGH AI AND DIGITAL

INNOVATION AT NATIONAL

WINDSCREENS SVALUE TO

CUSTOMERS

Simon Hunt, Commercial & Services

Director, National Windscreens


35

AI AT THE WHEEL: HOW

THATCHAM RESEARCH IS

POWERING A DATA-LED

FUTURE FOR AUTOMOTIVE

RISK AND INSURANCE

Jonathan Hewett, Chief Executive,

Thatcham Research

The Fraud Board

61

63

WELCOME

Adele Sumner, RSA Insurance, Head of

Counter Fraud & Financial Crime

CHARLES TAYLOR

A New Dawn for Documentation

Bobby Gracey, Global Head of Counter

Fraud, Charles Taylor

WHITELK

AI – Ozempic for an UnhealthyData Diet

Matt Gilham, Director, Whitelk

65 FRISS

AI Integration in Insurance: Why

Feedback is the Key to Smarter Fraud

Detection

Martyn Griffiths, Sales Manager UK&I,

FRISS RGI Solutions

ALLIANZ

Fraud Detection and AI: Harnessing

Technology Without Losing Sight of the

Fundamentals

Ben Fletcher, Director of Fraud and

Financial Crime, Allianz UK

66 RGI

Embracing AI For a Stronger Insurance

Industry

Chris Moore, Counter Fraud Director,

RGI Solutions

39 MASS

Civil Liability Act Under Review: An

Opportunity to Address Unfairness

and Refocus Reform Sue Brown, Chair,

Motor Accident Solicitors Society

(MASS)

MGAA

The Incubators Supporting the Next

Generation of MGAs Mike Keating, CEO,

Managing General Agents’ Association

(MGAA)

40 APIL

Ten Years of Injustice: How

‘Fundamental Dishonesty’ Powers Are

Being Abused to Deny Injury Victims

Fair Compensation Matthew Tuff, APIL

president

41

CII

AI in Insurance: It’s Older Than You

Think Dr Matthew Connell, Director,

Policy and Public Affairs, Chartered

Insurance Institute (CII)

CHO

The Role of AI in Transforming

the Insurance Industry Anthony

Hughes, Chair & CEO, The Credit Hire

Organisation (CHO)

FOIL

Fairness First: Managing Bias in AI to

Build Trust in Insurance Pete Allchorne

and Jo Folan of the Strategic Advisory

team at DAC Beachcroft’s claims

division, CSG

43 BIBA

How Insurance Brokers Are Embracing

the Power of AI Emma Chapman,

Conference Director, British Insurance

Brokers’ Association (BIBA)

IAEA

Defining the Future Engineer Assessor:

Competence, Compliance and the

Role of AI in a Changing Industry

David Punter, President, Institute of

Automotive Engineer Assessors (IAEA)


ARTIFICIAL INTELLIGENCE (AI) IS

TRANSFORMING

THE INSURANCE

INDUSTRY

Global AI Landscape and Its Impact on the Claims

Ecosystem

By mid-2025, AI has evolved from isolated pilots to

full-scale enterprise deployments across the insurance

value chain. In claims, AI is reshaping operations in

four transformative areas: visual damage assessment,

natural language processing (NLP) with AI agents,

conversational self-service, and real-time data ingestion

from Internet of Things (IoT) devices.

The insurance industry stands at a

pivotal moment: artificial intelligence

(AI) is no longer a niche experiment

but a strategic necessity. As insurers

around the world embrace digital

transformation, claims processing has

emerged as a critical proving ground

for AI’s value. This article explores six

essential dimensions of AI in insurance:

the global AI landscape and its impact

on claims, where companies see the

strongest ROI and the risks associated

with unsuccessful transformations;

the significance of accountability in

AI deployment; the build-versus-buy

decision; a focus on key AI disruptors

shaping the market in 2025; and a

forward-looking perspective on the

future of AI in insurance, emphasizing

Multi-Agent Reinforcement Learning

(MARL) and its revolutionary potential.

Computer vision enables insurers to assess damage

remotely, eliminating delays caused by in-person

inspections. Startups like Tractable allow for near-instant

evaluations of property and auto claims using AI models

trained on thousands of images. Carriers using such tools

report processing times up to 50% faster and significant

savings on loss adjustment expenses.

NLP and AI agents collaborate to manage the extensive

documentation generated by claims, particularly in the

critical area of fraudulent claims modeling. AI systems

extract key details from adjuster notes, medical reports,

and legal documents, enabling the identification of

potentially fraudulent activities. This process greatly

reduces the need for manual review. AI agents then

autonomously handle and route claims, collaborate with

underwriters or fraud analysts, and escalate edge cases

for human review. By incorporating fraudulent claims

modeling into their workflows, these systems become

more adept at recognizing patterns that indicate fraud.

This agentic approach accelerates decision-making,

improves accuracy, and facilitates more adaptive

workflow orchestration.

Conversational AI assistants enhance customer service

by guiding policyholders through the processes of filing,

documentation, and tracking. With 24/7 availability and

empathetic response capabilities, these virtual agents

lower call volume and boost customer satisfaction.


DISRUPTORS

TO WATCH IN 2025

DISRUPTORS

TO WATCH IN 2025

Several insurtech startups are driving next-generation

innovation:

OWL.CO: This Canadian firm offers encrypted claims platforms with document

automation and fraud detection. In trials, Owl.co reduced losses by 18%.

QUANTEMPLATE: Based in the UK, Quantemplate simplifies data cleansing

and scenario modeling for underwriters. Insurers report improved accuracy and up

to 35% savings on integration.

TRACTABLE: Specializing in computer vision, Tractable helps carriers evaluate

damage from images. Deployed by Geico and Liberty Mutual, it cuts appraisal

times in half.

WELLX: This UAE startup uses biometric signals to offer dynamic, behaviorbased

health coverage. Its embedded health products have drawn over 50,000

users in the GCC.

ADDENDA: Operating in the UAE and India, Addenda issues micro-insurance

based on IoT-triggered parametric data. Policies are activated automatically when

sensors detect losses.

UBIX.AI: UBIX.ai provides a no-code AI platform that enables rapid deployment

and contextualized data analysis, eliminating the need for data science expertise.

Its on-demand pricing model makes it accessible for mid-sized insurers, and

it supports applications ranging from underwriting and claims optimization to

fraudulent claims modeling and detection. By democratizing enterprise-grade

AI, UBIX.ai is helping insurers deploy agentic AI at scale, lower time-to-value, and

reduce operational costs.

Other notable players include Lemonade, Fabric, and Clover Health, each

of which integrates AI into claims and underwriting to create real-time,

user-friendly experiences.

These disruptors matter because they challenge legacy assumptions.

Instant claims, usage-based premiums, and smart contracts redefine

customer expectations. Incumbents must respond with comparable speed

and flexibility.


IoT-enabled real-time data ingestion—from wearables,

smart homes, and connected vehicles—facilitates

proactive claims handling. Claims are now automatically

triggered when specific events, such as airbag

deployments or water leaks, are detected. This realtime

approach allows insurers to transition from reactive

assessors to proactive risk managers.

Adoption varies by geography. North America leads in

AI-driven property and casualty claims, while European

carriers proceed cautiously due to privacy regulations.

Asian markets benefit from regulatory sandboxes that

foster innovation, while digital-native insurers in Latin

America and Africa use AI through mobile platforms to

improve accessibility.

The divide between digital leaders and laggards is

widening. Agile insurers reduce claims cycles from

days to hours, while outdated IT systems and isolated

operations continue to hinder others. For those who

act swiftly, AI can lower claims-related costs by 15–

30%, turning the most extensive expense line into a

competitive advantage.

ROI Leaders and Pitfalls of Poorly Planned

Transformations

Claims functions often yield the highest returns on

investment (ROI) from AI adoption. Computer vision

applications reduce claims cycle times by nearly onethird.

When combined with predictive analytics and

fraud detection, insurers report fewer false payouts and

improved retention. This is particularly significant in

fraud detection, where AI can drastically cut the costs

associated with fraudulent claims, leading to a positive

return on investment (ROI) for insurers. Additionally,

streamlining the detection process further enhances

efficiency and effectiveness in handling claims. ROI in

claims generally appears within a few quarters, unlike

longer-term investments in underwriting.

Marketing and customer acquisition also benefit. AI

enables precise segmentation, which increases crosssell

rates and decreases acquisition costs.

Chatbots decrease quote abandonment rates, improving

conversion without increasing staffing budgets. In

underwriting, AI improves risk scoring by incorporating

alternative data sources. This leads to better pricing,

lower loss ratios, and stronger combined ratios.

Although these underwriting advancements require

more time to develop, they create a foundation for longterm

profitability.

However, not all digital transformations succeed. AI that

lacks strategic alignment or a robust data infrastructure

can lead to failures. One Asian insurer overwhelmed its

fraud team with poorly integrated AI triage, negating

the efficiency gains it had achieved. Another U.S. carrier

faced data corruption during a migration, resulting in

millions of dollars in remediation costs.

Risks extend beyond technology. Outsourcing AI to

poorly vetted vendors can introduce algorithmic bias,

as seen when a U.K. carrier’s model unfairly charged

marginalized groups. Regulatory compliance failures,

such as mishandling personal data, have resulted in fines

and reputational damage.

To avoid these missteps, successful transformations

emphasize clear objectives, cross-functional

collaboration, strong governance, and agile deployment.

Foundational investments in clean, accessible data are

essential to AI’s success.

Caution and Accountability in AI Deployment

As AI automates high-stakes decisions, caution and

accountability are crucial. Insurers must ensure model

transparency, reduce bias, and maintain data privacy.

AI models must be explainable. If a claim is denied

based on image analysis, the carrier must document

the rationale and ensure it aligns with the policy terms.

In one case, a U.S. insurer faced litigation over opaque

AI decisions following Hurricane Malcolm, sparking

regulatory proposals for greater auditability.

Bias mitigation is equally critical. AI systems trained

on historical data can perpetuate existing biases and

discrimination. Insurers must conduct fairness audits,

disparate impact testing, and third-party validation.

Upcoming EU regulations classify insurance AI as

“high risk,” necessitating formal assessments and

transparency.

Data privacy and security remain critical. Regulations

such as GDPR and CCPA impose strict controls. One

carrier was fined €25 million for failing to encrypt

sensitive health data during a pilot.

Human oversight is crucial to prevent over-automation.

AI should assist, not replace, human adjusters in

complex cases. Human-in-the-loop models ensure

accountability and maintain customer trust.

Build Versus Buy: Developing AI In-House or

Partnering

Insurers face a critical strategic decision: develop AI

capabilities in-house or utilize third- party platforms.

Each option presents benefits and trade-offs.

In-house development provides control and extensive

customization. Internal Centers of Excellence (CoEs)

synchronize AI with proprietary pricing, claims rules,

and compliance needs. These models deliver long-term

differentiation but necessitate significant investments in

talent and infrastructure.

Partnering accelerates time to value. Vendors provide

pre-trained models and APIs for quick deployment. Owl.

co, for example, delivers AI-powered claims platforms

that reduce cycle times by 40% in pilot programs.

Partners also offer access to data sources that insurers

may not possess.

Outsourcing introduces various risks, including vendor

lock-in, transparency issues, and liability for errors made

by third-party models. A primary carrier settled a $50

million lawsuit after an outsourced model incorrectly

flagged thousands of valid claims.

Many carriers adopt a hybrid approach. Proprietary

models manage core risk analytics, while third-party

tools improve specialized functions such as image

analysis. This combination balances control, speed, and

innovation.


Vendor governance frameworks and internal upskilling

are essential regardless of the model. Carriers must

ensure staff can collaborate effectively with AI experts

and maintain oversight over vendor-delivered solutions.

The Future of AI in Insurance

While current AI deployments have transformed many

aspects of claims and underwriting, the next leap

forward will come from Multi-Agent Reinforcement

Learning (MARL). Unlike single-agent systems that

operate in isolation, MARL involves multiple AI

agents working together or competing within shared

environments to achieve complex goals. This paradigm

fosters emergent intelligence, allowing AI agents to

learn not only from their actions but also from dynamic

interactions with other agents and feedback from the

environment.

In insurance, MARL has the potential to reshape how

policies are priced, how portfolios are optimized, and

how claims are adjudicated in real-time. Imagine an

ecosystem where underwriting agents, claims agents,

and customer engagement agents continuously

interact and adapt based on each other’s outputs,

leading to self-optimizing operations. This multi-agent

coordination could revolutionize enterprise workflows

by unlocking greater resilience, adaptability, and

precision.

MARL is especially suited for complex, dynamic fields,

such as insurance, where numerous interdependent

factors must be balanced simultaneously. It provides a

pathway to autonomous business processes that exceed

automation—venturing into the domain of intelligent

orchestration and ongoing improvement.

sectors. As the insurance industry adopts MARL, we can

anticipate a shift towards real-time, policyholder-centric

ecosystems where decision support is distributed,

adaptive, and increasingly autonomous.

Conclusion

AI has quickly become a trusted partner in the

insurance industry, enabling teams to work smarter,

serve customers more efficiently, and make more

informed decisions. However, having powerful tools

isn’t enough—how you use them truly matters. With

the correct data, careful oversight, and a commitment

to fairness and transparency, insurers can leverage AI

as a lasting advantage that grows in tandem with their

business.

Whether building or buying, insurers must align AI

strategies with their operational maturity and risk

profile. Hybrid models often provide the best balance

between control and agility, but all approaches require

vigilance regarding fairness, security, and model

explainability.

Disruptive players continue to reset expectations, from

instant claims processing to usage- based pricing.

But the horizon holds even greater change. Multi-

Agent Reinforcement Learning (MARL) will transform

the industry by enabling distributed, collaborative AI

systems that adapt and optimize in real-time.

Now is the time for bold, intentional action. Carriers

that lead—by embracing innovation, embedding

accountability, and preparing for MARL—won’t just

survive the next era of insurance; they will shape it.

Early prototypes of MARL-driven platforms are already

emerging in the financial services and healthcare

About the Author

Charles Skamser is a globally recognised technology executive and strategic advisor

with over 30 years of experience driving digital transformation across the Global 500.

A former CEO, CRO, Consulting Partner, and Board Member, he has built and led VC

and private equity-backed software and consulting firms to billion-dollar valuations

and over $150 million in annual revenue. Known for his deep expertise in AI, ML,

application and data modernisation, cloud, and cybersecurity, Charles specialises in

aligning cutting-edge technologies with strategic outcomes. He is also a MEDDPICC

expert with a strong track record in enterprise sales and deal execution. His handson

experience with platforms such as ServiceNow, Snowflake, Databricks, and

AWS informs his advisory work across financial services, healthcare, insurance, and

manufacturing. Passionate about the next wave of innovation, including Multi-Agent

Reinforcement Learning (MARL), Charles helps clients harness AI not just to automate,

but to intelligently orchestrate and optimise complex business processes in real time.

Charles Skamser,

CEO, PX42.ai

References

1.Deloitte. (2025). Global Insurance Industry Outlook 2025.

2.Tractable. (2025). Case Study: Computer Vision in P&C Claims Appraisal.

3.Owl.co. (2025). Automated Claims Management and Fraud Detection: Results and Metrics.

4.Ernst & Young. (2025). Top Technology Risks in Insurance 2025.

5.Quantemplate. (2025). Data Integration and Analytics for Underwriting.

6.EY Global. (2025). Insurance Technology Risk Survey.

7.French Data Protection Authority (CNIL). (2024). GDPR Enforcement Actions in the Insurance Sector.

8.Owl.co. (2025). Automated Claims Management and Fraud Detection: Results and Metrics.

9.Quantemplate. (2025). Data Integration and Analytics for Underwriting.

10.Wellx. (2025). Usage-Based Health Insurance Powered by Real-Time Underwriting.

11.Addenda. (2025). Parametric Insurance at Scale: Micro-Insurance and IoT Integration.

12.UBIX.ai. (2025). Enterprise AI for Insurance: Data Sheet and Case Studies.

13.Fabric. (2025). AI-Driven Life Underwriting: Accelerating Approvals.

14.Lemonade. (2025). Instant Insurance: AI Agents in Underwriting and Claims.

15.Clover Health. (2025). AI for Preventive Care in Medicare Advantage.


INTERVIEWS


INTERVIEWS

Katie King is one of the world’s most respected

voices on artificial intelligence and digital

transformation. In this interview, she shares

her expert insights on how AI is reshaping the

insurance sector, from enhancing customer

experience to raising important ethical questions.

With clarity and candour, she explores how

insurers can strike the right balance between

automation and the human touch, while building

trust and avoiding bias in AI systems.

Recently named a Top 10 AI Influencer 2024 and voted the Leading

AI Strategist, Katie brings 35 years of consulting experience

advising organisations such as Unilever, Deloitte, Virgin, and Sanofi.

She is the author of two acclaimed books on AI in marketing and

sales. Her first, Using Artificial Intelligence in Marketing, was cited

as a reference in the World Economic Forum’s Empowering AI

Leadership toolkit. Her second, AI Strategy for Sales and Marketing,

was published in 2022, with a new edition due in 2025.

Katie is also a member of the UK Government’s All-Party

Parliamentary Group on AI and serves on the editorial board of the

AI and Ethics Journal. Alongside her corporate work, she launched

the Leaders of Tomorrow programme in schools to help close the AI

skills gap and prepare young people for the future of work.

From global case studies to practical frameworks, Katie offers

a compelling perspective on how AI can empower, rather than

replace, the human side of insurance. This is essential reading for

leaders looking to adopt AI responsibly and effectively in a fastchanging

landscape.

As someone at the forefront of AI and digital

transformation, how do you see AI reshaping the insurance

Qsector, particularly in balancing automation with the human

touch in customer service?

A

AI is revolutionising the insurance industry by streamlining

operations, enhancing risk assessment, and personalising

customer experiences. However, it’s crucial to balance

automation with the human touch. For instance, Zurich Insurance’s

AI-powered CRM system has reduced service times by over

70%, allowing agents to focus more on personalised customer

interactions. This synergy between AI and human agents ensures

efficiency without compromising empathy.

Crucially, we must learn from how other sectors have successfully

(and sometimes unsuccessfully) integrated AI. From healthcare to

hospitality, there are cross-sector lessons in ethics, efficiency, and

emotional intelligence that the insurance industry can—and should—

adopt.

Many insurers are integrating AI-powered chatbots to

handle FAQs, freeing up human agents for complex

Qqueries. How can organisations strike the right balance

between efficiency and customer satisfaction in this setup?

The key is to deploy AI for routine tasks while reserving

human agents for complex, emotionally nuanced interactions.

AAllstate’s implementation of AI-generated communications

has led to more empathetic customer interactions with less jargon,

enhancing overall satisfaction. By ensuring seamless escalation

from AI to human agents when needed, insurers can maintain high

efficiency and customer trust.

Q

How can AI help insurance companies create more

personalised and connected experiences for their

customers?

AI enables insurers to analyse vast datasets to understand

individual customer needs better. For example, integrating

Awearable technology allows for personalised health insurance

plans that reward healthy behaviours. Such personalisation fosters

stronger customer relationships and loyalty, turning transactions

into trusted partnerships.

Ethical concerns around AI, such as data privacy,

algorithmic bias, and transparency, are especially relevant

Qin regulated industries like insurance. What guiding

principles or frameworks do you advise companies to adopt to

build ethical AI systems?

Building ethical AI systems requires a commitment to

transparency, fairness, and accountability. Implementing

Aframeworks that include regular bias audits, explainability

tools, and ethical review boards is essential. For instance, the

National Association of Insurance Commissioners (NAIC) reports

that 88% of auto insurers are using or exploring AI models,

emphasising the need for robust ethical guidelines. Embedding

these principles into company culture—rather than viewing them as

mere compliance—is what will ensure responsible deployment of AI.

As a member of the All-Party Parliamentary Group on Artificial

Intelligence (APPG AI), I’ve seen first-hand the importance of this.

Recent APPG initiatives, including policy reviews on AI in financial

services and public sector governance, reflect the need for sectorspecific

ethical oversight and a united national strategy for AI

innovation.

Q

You’ve spoken about the importance of rethinking AI not

as a replacement for human connection but as a way to

enhance it. Can you share examples of how this mindset

has led to stronger outcomes for businesses and customers?

Absolutely. In the health insurance sector, AI tools assist

agents with real-time risk analysis and personalised care

Asuggestions, enhancing the quality of human interactions.

Moreover, AI-powered systems can proactively identify vulnerable

customers during crises, enabling timely and empathetic human

outreach. These applications demonstrate how AI can augment—

not replace—human capabilities, leading to improved customer

satisfaction and loyalty.

AI is moving fast around the world. How can insurance

companies keep up and stay ready for future changes?

Q

Insurance companies must foster a culture of continuous

learning and innovation. This involves upskilling staff,

Aexperimenting with new AI tools, and collaborating across

departments. Adopting structured frameworks, like my Scorecard

for Success and 6-Month Action Plan, can guide organisations

through ethical and strategic AI integration. Staying agile and

proactive in embracing AI advancements will position insurers to

adapt effectively to the evolving landscape.

Last year, I had the privilege of speaking in 14 countries—from

Sydney to Savannah —across sectors as diverse as concrete,

packaging, pharmaceuticals, and travel. These experiences

reinforced one critical insight: innovation knows no borders.

The insurance industry can significantly benefit from these

global, cross-sector lessons as it prepares for its AI-driven future.

Katie King,

CEO, AI in Business and

Managing Director & Founder,

Zoodikers Consulting Ltd


“Bias Isn’t Just

a Bug – It’s a Mirror”:

Why Human Oversight Is Essential

in Insurance AI


INTERVIEWS

In the race to harness AI across insurance, subtle biases embedded in data and

algorithms risk reinforcing outdated assumptions and alienating customers. David

Alexander Eristavi and Costas Christoforou of Sagacity Solutions explore how

insurers can detect, manage, and mitigate bias, while maintaining fairness, trust,

and regulatory compliance in an increasingly AI-driven market.

QWhat Are The Subtle Ways Bias Can Seep Into AI Model

Predictions?

David Alexander Eristavi, AI & ML Business Development

Manager

AThe reality is, AI models are ultimately reflections of the

humans who build them and the data they are fed. We, as

humans, are inherently biased; it is part of our nature, even

when we strive for objectivity. This means that anything we create,

including an AI model, will likely carry some of those biases.

One of the biggest culprits is historical data. All data is backwardslooking,

recording past behaviours and events. If we base

future decisions purely on the past, we risk perpetuating unfair

assumptions. Think about how gender was once used in motor

insurance quotes. It reinforced stereotypes and was rightly

removed. Even if insurers are not intentionally discriminatory,

prejudices can still creep into AI outputs through the data and

practices used to train the model.

Consider this: if one postcode shows historically high home

insurance claims while a neighbouring, newly developed street

does not, where do you reckon AI might assign the higher

premiums? The raw data misses the context that the new street

simply has not had time to accumulate claims yet, despite facing

similar environmental risks. This is a classic example of how a lack

of data transparency can hide biases. If we do not know the full

context or source of our data, identifying and addressing these

nuances becomes incredibly difficult.

Similarly, flawed data collection (missing, inaccurate or duplicate

data) can lead to an AI incorrectly determining risk. If a dataset

lacks crucial information, the model makes decisions based purely

on what it does have (or has too much of), potentially leading to

inaccurate, higher premiums that could have been avoided with

complete information.

Ultimately, while AI brings incredible efficiency, an over-reliance on

technology without human oversight is a fast track to bias. Without

a human sense-check on both inputs and outputs, bias is almost

inevitable.

QHow Do You Evaluate Whether A Generative AI Model

Used In Insurance Communications Or Claims Generation

Is Producing Biased Or Harmful Outputs?

Costas Christoforou, Data Science and Innovation Analyst

AEvaluating generative AI for bias in insurance

communication and claims processing begins with

establishing clear definitions of what constitutes bias in this

context. Bias manifests when AI systems treat customers unfairly

based on factors such as age, gender, race or income level.

Generative AI, if biased, can produce harmful messages that

erode customer trust and risk regulatory issues. That is why bias

detection is crucial. It starts with rigorously auditing the model’s

training data, ensuring it truly represents all insurance products,

customer types and claim scenarios.

Beyond just existing data, the evaluation also needs to confirm

that training data aligns with current company policies, regulatory

needs and the right communication tone. Why? Because

imbalanced or outdated data can easily lead to models making

unfair assumptions, reinforcing stereotypes or creating systemic

errors that directly impact customers. This is why our core

approach involves systematic testing with prompts that mimic realworld

customer interactions.

These tests are not just about functionality; they are about

rigorously assessing consistency, accuracy, tone and clarity across

all customer types. The core principle is that identical claims from

different people should always get equivalent, fair responses.

This is how we uncover if the model is making unfair assumptions

based on irrelevant customer traits. Crucially, a cross-disciplinary

human review remains indispensable for a truly comprehensive bias

assessment.

These review teams need diverse expertise: claims experts, legal

advisers, compliance officers and communication specialists. They

are vital for spotting subtle biases automated systems often miss.

Their focus is on finding inconsistencies, factual errors, policy

violations and any language that could confuse, distress or erode

customer trust.

This also ensures outputs maintain brand voice, avoiding premature

blame or claim denial. This human review is complemented

by rigorous quantitative assessment. We systematically track

KPIs such as error rates across demographics, analyse tone for

professionalism and measure readability. Advanced tools such as

sentiment analysers help objectively gauge communication quality

and potential bias. Finally, post-deployment monitoring provides

crucial real-world insights through customer feedback, complaint

tracking and escalation rates.

If AI communications cause higher-than-average

misunderstandings or disputes, that is a red flag for potential

bias or clarity issues needing immediate attention. Continuously

tracking these patterns drives data-driven model improvements

based on real customer experiences. Crucially, all evaluation

processes must strictly adhere to insurance regulations and internal

standards.

This also means verifying that AI responses respect privacy,

provide accurate policy interpretations and avoid unauthorised

advice. Crucially, outputs must reflect the insurer’s ethical and

legal responsibilities, especially in sensitive claim situations where

missteps have significant consequences. Bias evaluation is not

a one-time fix; it demands ongoing vigilance. Models require


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INTERVIEWS

continuous auditing as they encounter new data and adapt to

evolving customer expectations and regulations.

This proactive approach not only prevents model drift but is

fundamental to building lasting customer trust and solidifying an

insurer’s leadership in an increasingly AI-driven market. It is how AI

truly delivers on its promise of fairness and value.

QHow Can Insurers Protect Themselves From Regulatory Or

Reputational Repercussions Caused By Flawed Models?


Manager

AProtecting against flawed models is not just about

compliance; it is about building trust and gaining a

competitive edge. It fundamentally comes down to three

key areas:

Deeply understand and edit training data: As mentioned, if you do

not know the exact data points used to train an AI, you are flying

blind. Insurers need to be acutely aware of potential inequalities in

their inputs. Are you inadvertently feeding in data that correlates

with protected characteristics, even if not directly? Are there

gaps or blind spots? If bias is identified, it is crucial to minimise

it. This could mean removing the biased feed, restricting the AI’s

use where it is most vulnerable, or proactively generating new,

equitable data to balance the scales. For instance, more insurers

are looking at behavioural data from telematics or fitness watches,

which can offer a fairer, risk-based view without relying on

potentially discriminatory factors.

Keep a human in the loop: It is not a suggestion; it is a necessity.

AI needs to be used in a regulated and controlled way, with

continuous human oversight. The data being used, and the models

themselves, need regular review and assessment. Ideally, this

oversight should be conducted by a diverse team. Why? Because

diversity helps to catch unconscious biases that individuals might

miss. A human-in-the-loop approach ensures accountability and

supports a truly ethical use of AI.

assumptions. It is through constant attention and the diligent

application of this framework that we can genuinely work towards

maintaining a more unbiased model. It is less about complete

removal, and more about continuous mitigation and responsible

management.

QWhat Role Should A Human In The Loop Play In Ensuring

Fairness, Accuracy And Accountability In AI-Driven

Insurance Systems?

David Alexander Eristavi, AI & ML Business Development Manager

AThe human in the loop is foundational to responsible AI in

insurance.

Firstly, as discussed, humans are crucial for catching biases.

Without a human reviewing AI outputs and inputs, bias will

inevitably creep in. This is not just theoretical; it manifests in realworld

problems such as inaccurate or overly high premiums due to

flawed or incomplete data.

Secondly, a human’s role is to prevent “model collapse”. AI

models are built on existing data, but the world, and customer

behaviours, are constantly changing. Without human contact, care

and attention, models can become stale, failing to adapt to new

realities. This can lead to bad data being fed back into the system,

degrading the model’s performance over time. Human intervention

ensures refinement and updates as contexts evolve.

Finally, and perhaps most critically, AI simply cannot replace our

morality and our ability to foresee future implications. AI replicates

past thinking; it does not inherently understand human values,

fairness beyond its learned parameters or the nuances of individual

situations. The human in the loop plays the irreplaceable role of

correcting AI when it strays, guiding its development with an ethical

compass and ensuring that the technology serves humanity, not

the other way around. We are the ultimate arbiters of fairness and

accountability in these increasingly intelligent systems.

Proactive compliance and ethical frameworks: With new

regulations such as the EU AI Act emerging, controls on AI use

are only going to tighten. Insurers must address and understand

the risk of bias now. Proactive management not only ensures

compliance but also offers a significant competitive advantage.

Insurers who effectively navigate these challenges by scrutinising

and refining their AI processes will build deeper trust and loyalty,

positioning themselves as true leaders in an increasingly AI-driven

industry. The time to act is now.

QWhy Is The Question Of Whether To Always Remove Bias

More Complicated Than It Appears?


Manager

AThis is where the rubber meets the road, and it circles back

to our inherent humanity. Since humans are inherently

biased, every attempt to “remove” bias often comes with

its own set of assumptions and perspectives. Even when we bring

in external parties for an “unbiased” assessment, they also operate

within their own frameworks of experience, beliefs and values.

It is incredibly difficult to claim complete unbiasedness. That is why

relying solely on intuition or individual judgement is not enough.

We need a robust ethical framework, a collectively agreed-upon

guide, against which we can fact-check our own morals and

David Alexander Eristavi,

AI & ML Business

Development Manager,

Sagacity Solutions Limited

Costas Christoforou, PhD,

Data Science and

Innovation Analyst,

Sagacity Solutions Limited


carpentersgroup.co.uk

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Insurance and Legal Claims Services

EDITORIAL BOARD

No Quick Wins: Navigating the Real-World

Challenges of AI Adoption in Insurance

While the promise of AI in the insurance sector

is compelling, offering automation, efficiency,

and enhanced customer experiences, the path to

implementation is far from frictionless. At Carpenters

Group, we’ve embraced AI as a strategic enabler, but not

without encountering several nuanced challenges that

have shaped our approach.

One of the most immediate hurdles has been navigating the

regulatory and ethical landscape. The insurance industry is highly

regulated, and rightly so. Ensuring that AI systems are transparent,

explainable, and auditable is not just a best practice, it’s a necessity.

We’ve had to invest significant time in developing governance

frameworks, such as our AI Council and AI Terms of Reference, to

ensure that all AI use cases align not only with our internal values, but

with legal, ethical, and societal expectations.

This includes rigorous risk assessments, stakeholder engagement, and

continuous monitoring of AI performance against this framework.

Another challenge has been integration. AI doesn’t operate in a

vacuum, the real benefit from it is when it works seamlessly with our

case management platform, and third-party tools. It is for this reason

that we took the strategic decision to build in-house capabilities,

which in turn has intensified the demand for specialist talent, a

resource that is both scarce and highly competitive.

Security and data privacy also remain front and centre. As noted in

our AI Policy, we’ve had to implement robust safeguards to protect

sensitive client data and ensure compliance with GDPR and other data

protection laws.

The recent rise in high profile cyber threats has only heightened our

caution, prompting us to limit public disclosures about all our AI

initiatives, to avoid exposing potential vulnerabilities.

Culturally, the shift to AI has required a mindset change. There’s

a natural tension between automation and the human touch,

particularly in a sector where empathy and personal service

are paramount. We’ve addressed this by positioning AI as an

augmentation tool rather than a replacement, ensuring that human

oversight remains integral to decision-making processes.

Lastly, there is the commercial aspect to consider. I am acutely aware

that that some businesses are awaiting more data-driven evidence

regarding the security and safety of AI before integrating it into

their operations. To address this, we have developed a ‘by source’

model that permits agile exceptions as necessary. Naturally, this

has introduced an additional layer of complexity to our operational

frameworks.

AI is unlocking real potential in our business; transformative potential,

but there isn’t a quick win here, there must be careful orchestration

across technology, people, and policy. I am proud of the progress

we are making, but we remain vigilant, adaptive, and committed to

responsible innovation.

Darren Hall,

Chief Operating Officer, Carpenters Group

The Next Phase of AI in

Insurance Software

Over the next two years, the role of AI in insurance

software will shift from novelty to necessity. As insurers

and MGAs face mounting pressure to deliver faster,

more tailored products while managing operational

complexity, AI will become a core enabler of agility and

efficiency, not just an add-on.

In our space, insurance technology platforms, AI’s most immediate

impact will be on product configuration and operational automation.

Historically, defining and launching new insurance products has

required significant manual effort across underwriting, pricing,

compliance, and distribution. AI has the potential to dramatically

reduce the time from idea to implementation. We already see early

signs of this with generative AI tools capable of drafting policy

wordings, mapping underwriting logic, or even configuring product

schemas based on plain-language inputs. Over the next two years,

these tools will become more integrated into insurers’ workflows,

acting as co-pilots rather than replacements for human expertise.

The second major shift will be in how AI helps platforms support

decision-making and reduce operational drag. For example, AI models

can flag anomalies in claims data, assist underwriters with triage and

prioritisation, and even recommend next-best actions to customer

service teams. These capabilities are especially valuable for specialist

insurers with bespoke products, where conventional rule-based

systems fall short. AI thrives on nuance, and so do specialist insurers.

That said, while the hype around AI often centres on fully automated

experiences, we believe the future is about augmentation, not

replacement. The complexity of insurance products, particularly in

specialist lines, means human judgment remains irreplaceable. The

role of AI will be to amplify that human capability, making it easier to

explore data, test scenarios, and deliver insight—, without drowning in

spreadsheets or complex configuration tools.

From a platform perspective, this evolution poses both an

opportunity and a responsibility. The opportunity is to embed AI in

ways that truly support insurance professionals, not just in customerfacing

chatbots, but in the deeper layers of product design, claims

logic, reporting, and compliance. The responsibility is to ensure

transparency, guard against bias, and provide controls that allow

humans to stay in charge.

In short, we see AI becoming a quiet partner behind the scenes:

speeding up the mundane, surfacing the meaningful, and giving

insurers the breathing room to innovate. The next two years will be

about moving from experimentation to embedded value; an evolution

we’re excited to help shape.

Will Prest,

Product Manager, ParaCode


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EDITORIAL BOARD

How Digilog UK Champions Ethical,

Transparent, and Responsible Use of

Advanced Technologies in Insurance

We believe that trust is the cornerstone of every

effective insurance interaction and as artificial

intelligence (AI) becomes more integrated into the

claims and underwriting landscape, this belief becomes

even more critical.

The insurance industry stands at the intersection of innovation and

accountability. AI has the power to streamline processes, improve

fraud detection, and enhance customer experiences. But these

benefits must not come at the expense of fairness, transparency, or

individual rights. As a company committed to ethical technology,

Digilog UK approaches this new frontier with both optimism and

responsibility.

Our commitment starts with a strong ethical foundation. AI solutions

developed or deployed by Digilog UK are designed to reflect core

values: fairness, impartiality, and accountability. Whether we’re using

AI to assist in credibility assessment or to enhance decision-support

tools, we ensure that outcomes are rigorously checked for potential

bias or unintended discrimination. Every individual, regardless of

background, deserves a fair and respectful experience.

In our line of work, data integrity and privacy are paramount. We

consider privacy protection in every layer of our systems, adhering

strictly to GDPR and best-in-class data management practices. Data

anonymisation, informed consent, and transparency around data

usage are not just legal checkboxes for us - they’re integral to earning

and keeping our clients’ and their customers’ trust.

We’re well aware of the risk that AI can become a ‘black box’,

especially in high-stakes decisions like fraud detection or claims

validation. That’s why we favour explainable AI. We believe in

outcomes that not only make sense technically but can be clearly

articulated to customers and regulators alike. This is especially critical

in areas like behavioural analysis, where transparency isn’t just good

practice, it’s essential to credibility.

We have always advocated a hybrid model where human insight

complements machine efficiency. Our technologies are designed to

support, not replace, human judgment. Critical decisions, especially

those with ethical or emotional implications are always subject to

expert human review. We train our partners and clients not only in the

technical use of our tools, but in recognising the limits of automation

and knowing when human intervention is essential.

Digilog UK continuously monitors and reviews the performance of

our AI systems, using both internal audits and client feedback to

adapt and improve. We also maintain open lines of communication

with stakeholders, from insurance partners to end-users, to ensure

transparency and provide a clear route for questions or concerns

about how our technology is used.

As the industry continues to evolve, our commitment remains the

same: to deploy advanced technologies that uphold the highest

standards of ethics, transparency, and human dignity. At Digilog UK,

we see AI not just as a tool, but as a responsibility, one that must be

handled with care, integrity, and a human touch.

Lior Koskas,

CEO, Digilog UK

Balancing Bots and Brains: How

FMG is Using AI to Enhance, Not

Replace, Human Expertise

It started as a bold ambition, that computers would

one day be able to think like us, and now Artificial

Intelligence is seemingly everywhere. It’s so ingrained

in our lives that we almost forget it’s there. Netflix

recommendations keep us watching, social media

content keeps us scrolling and real-time traffic updates

keep us moving.

In the insurer world, the integration of AI is revolutionising how we

modernise and optimise our operations and meet the critical need for

ever faster claims settlement.

When processing claims, AI in the form of Robotic Process

Automation (RPA) stands out for its ability to mimic human action.

Bots are built, deployed and programmed to interact with digital

systems and automate basic repetitive and manual tasks, bringing

efficiency, precision and scalability whilst removing the risk of human

error.

Here at FMG, we’re invested. We’re harnessing the power of AI and

RPA to transform the way we work. By automating repetitive, timeconsuming

tasks we’re freeing up our people, our most valuable

resource, to focus on the complex, strategic work that truly adds value

for our customers.

Within Loss Recovery, RPA has driven significant increases in losses

recovered, increasing productivity, lean efficiency and accuracy at

every stage. The potential of RPA is immense and by designing,

building and implementing these automations in house by FMG’s

specialist RPA team, we’ve ensured a perfect fit for our Loss

Recovery customers.

These automations allow for higher quality touchpoints with

customers by releasing the highly skilled Loss Recovery Handler

to proactively handle claims and focus upon the elements that

technology can’t replicate, those complex tasks requiring cognitive

skills of empathising, asking the right questions and negotiating on

each customer’s behalf.

This is where human expertise, with its deep analytical and decisionmaking

capabilities, remains unmatched, and even the most

advanced RPA robot has its limitations. While bots can rapidly

process vast data sets, enhance decision-making and create more

space for innovation, they cannot replace the nuanced human touch,

problem-solving competencies or creative insights.

We’re taking a thoughtful, measured approach to AI to carefully strike

the right balance; automating at the right pace and only where it

adds genuine value to the end-user experience.

Tracey Sherrard,

Head of Business Change, FMG


EDITORIAL BOARD

The Role of Vehicle Recovery Operators

(VROs) and the Potential of AI in the

Insurance Industry

Vehicle Recovery Operators (VROs) provide a bespoke vehicle

recovery solution for insurance, claims management, and

body shop companies alike, allowing partners to “carry on”

and focus on what they do best. Allowing professional vehicle

recovery operators to play their part hasn’t always been the

case. Historically, they were seen merely as an extension of the

emergency services, tasked with moving vehicles involved in

accidents, often adding a layer of complication to the insurance

and repair process.

Fast forward 30 years, and today VROs are just as important in the

customer journey as any specialist required to complete a role in the

repair programme of an accident-damaged vehicle. Yet, it is only

recently that the VROs’ main operating system, APEX Networks, has

been integrated with the motor claims system capture capabilities via

CAPS.

So, what is the role of AI in the insurance industry?

We believe there is definitely a role for AI within the insurance sector.

However, as we have previously stated, it is preferable for those

working within each part of the industry to evaluate the opportunities

and manage those which offer true benefit to their area, while

remaining supportive of other functional requirements that impact

different sectors.

VROs believe personal contact with customers is essential. It’s

important to remember that the customer has just experienced

something going wrong, this could be as minor as a flat tyre or as

serious as a major accident, or any number of events in between.

This must also be considered alongside factors such as location,

vulnerability, time of day or night, and adverse weather conditions.

We see opportunities for AI interventions when the customer is at

home or in a safe location, but not at the roadside. But how do you

make that distinction without having a unique phone number for

each type of incident, or requiring the customer to navigate through

multiple menu options? People want to speak to someone and be

reassured that their situation is being handled appropriately.

CMG does offer a “chat” function, but this is operated by a person,

not AI.

Our team’s focus is to ensure that consideration is centred on the

individual’s vulnerability, followed by safety, empathy, and respect

for the client’s situation and the people involved, all while delivering

a seamless and professional service under often extremely difficult

circumstances at the side of a road or motorway.

Speaking to customers who find themselves in unfamiliar situations

can be challenging. When VROs ask questions that may seem

unrelated, but are essential for deploying the correct assistance, it’s

unclear how AI would be able to interpret those responses effectively.

No doubt, over time, some AI capability will find its way into the

world of VROs, but not just yet.

Jason Brice,

Managing Director, CMG

Revolutionising Mobile Repairs:

RRN’s Bespoke AI Tool

Delivers Speed, Accuracy, and

Sustainability

Over the past eight months, Rapid Repair Network (RRN)

has successfully designed, built, and tested a bespoke

AI tool that has transformed the mobile repair estimate

process. This cutting-edge tool generates accurate repair

estimates in under 15 seconds, dramatically improving

efficiency and enabling faster claim processing.

Seamlessly integrated into our CRM system, the AI tool

also tracks and reports the carbon footprint of each

repair, ensuring greater transparency and supporting our

commitment to sustainability.

But the AI tool’s capabilities go far beyond cost estimation. It

recommends tailored, non-structural repair methods that align

precisely with our mobile repair offering, ensuring every solution is

both fit-for-purpose and environmentally responsible. This bespoke

approach means each repair is handled with precision and care,

optimising outcomes for both clients and the environment.

Strategically positioned at the First Notification of Loss (FNOL) stage,

the tool triages claims quickly and consistently. By automating expertlevel

decision-making, it eliminates the need for human intervention

at this crucial early stage, streamlining the entire claims journey.

This results in faster response times and a smoother experience for

insurers, accident management firms, and fleet operators alike.

The results speak for themselves. Stakeholders across the board

have seen measurable improvements, from faster turnaround times

to more consistent, high-quality estimates. Its recent rollout to work

provider partners has also proven a resounding success, further

cementing the AI tool’s transformative impact.

What sets this innovation apart is its bespoke nature. Specifically

trained using a dataset built around RRN’s mobile repair rules,

standards, and decision-making logic, the AI tool delivers expertlevel

precision that mirrors a skilled technician, only faster and more

reliably.

In short, RRN’s AI tool marks a major advancement in mobile

repair technology, combining speed, accuracy, sustainability, and

innovation. It creates a smarter, more efficient ecosystem where

insurers, accident management companies, and fleets can collaborate

seamlessly with RRN.

Simon Downing,

Strategic Key Accounts Manager, Rapid Repair Network

Limited


EDITORIAL BOARD

Don’t Forget the Claimant

The application of AI to the analysis of imagery,

processing of claims, and the interpretation of data is a

well-established if often misunderstood arrangement.

It is not necessarily the case that every process can

be enhanced by the use of AI. Equally, some of the

supposed AI applications are no such thing – merely new

applications of automation that have been around for

years.

Nevertheless, the job at hand is to look ahead to discern the art of the

possible.

In the world of salvage and associated recycled parts sector, there is a

major focus on using AI to accurately categorise vehicles and thereby

avoid those who might be tempted to ‘play the game’, and also to

make categorisation more consistent and transparent for all the

stakeholders. AI can also be of use in helping to deliver the desired

Consumer Duty outcomes on behalf of FCA regulated clients when

designing new ways of working.

Finally, AI has a pivotal role to play in helping clients (and e2e)

understand and plan ahead for the nuances of the vehicle auction

market. Maximising auction sale returns is a key component in

reducing insurer costs.

If there’s a claim, there’s a…….

But we must never forget that behind every claim is a human being.

In the case of the ‘total loss’ sector it is an obvious but (rather

strangely) forgotten fact that a claimant will have lost their vehicle

when it is ‘written off’ by their insurer. This invariably has financial,

convenience, and even traumatic consequences. Not only is the

policyholder affected but their family, friends, and work situation may

also be impacted.

Little, if any, of this real-world consequence can be managed by AI

other than, perhaps, the speed of decision making.

So, in designing our bright new systems, analytics, and technology

deployments let us not forget that there is more than one approach

to manage a claim and AI does not have all the answers needed.

Balancing technology and human interaction is a more considered

and ultimately more successful approach to successful claims

management.

Mia Constable,

Head of Business Development, e2e

All of this, and more, is making AI a central part of how we work. Even

the internal drafting of emails, marketing, and communications output

is helped by the judicious use of AI.

Reclaiming Time, Restoring Focus:

How AI Is Putting People Back at the

Centre of Claims Management

We believe that technology should work for people, not

the other way around. Like many professionals in the

claims and insurance sectors, we entered this industry to

apply assessing expertise, solve engineering problems,

and deliver clear insights to our clients. Yet, over time,

the core focus has too often been diluted by endless

admin, repetitive tasks, and data management that pull

attention away from real value.

Enter Swiftcase’s automation and artificial intelligence; not as a

buzzword, but as a powerful suite of tools that’s genuinely reshaped

and continues to evolve how we work.

AI has enabled us to refocus on what matters most: delivering

high-quality automotive reports and intelligence. By automating the

peripheral processes, from data entry to follow-up emails, we’ve not

only streamlined internal operations, but we’ve unlocked measurable

improvements for our clients across the board.

From Static Reports to Smart Solutions

Traditional PDF reports are giving way to smart, dynamic documents

that communicate across the supply chain in real time. For example,

if a vehicle is deemed unroadworthy, Swiftcase can immediately

notify the hire team or even pre-book a courtesy vehicle, including

estimated hire duration. If the vehicle is repairable, the system

matches it to an appropriate approved repairer based on location, skill

set, and availability; no more time-consuming coordination for claims

handlers. Relevant data regarding the repair is pushed directly into the

repairer’s system via API, creating a seamless, frictionless flow.

Smarter Client Tools and Customisation

We’re also empowering our clients with AI tools they can use directly

with their own customers. From personalised vehicle image upload

apps to AI chat and voice bots that assist with valuation, reporting,

FAQs etc, extending the benefit chain even further. These tools

reduce delays, improve customer satisfaction, and support faster

decision-making.

The beauty of Swiftcase’s AI framework lies in its flexibility. Whether

it’s a bespoke subrogation workflow, a data-rich dashboard, or

predictive modelling of claim trends, we can quickly and costeffectively

adapt to suit each client’s unique needs.

SUBHEADER: Efficiency and Accuracy with a Human Touch

The gains are undeniable: faster turnaround times, reduced

operational costs, better data insight, and improved consistency and

transparency. But we’re equally committed to keeping humans in the

loop. AI handles the heavy lifting, while our experts ensure that each

decision is considered, correct, and aligned with industry standards.

Ultimately, AI isn’t just making Laird more efficient, it’s delivering real,

tangible advantages to our clients, their customers, and the wider

claims ecosystem.

Welcome to the future of claims management; smart, scalable,

and human-centred. We’re always happy to talk about how our AI

works in practice, or better yet, to demonstrate it in action. If you’d

like to see how our intelligent solutions can support your business,

streamline your claims process, or improve your customer journey,

just get in touch.

Nik Ellis,

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EDITORIAL BOARD

Powering the Future of Claims: Copart’s

AI-Driven Approach to Seamless Service

“Speed and ease” is our mantra at Copart when it comes to the

claims journey, and the future integration of AI technology into

our operations and services is a natural next step for us.

Although the human element of customer service remains just as

vital, there’s no doubt that AI will play an important part in further

enhancing and improving what we do.

We pride ourselves on the strong relationships we build with our

customers; whether that’s collaborating closely with insurers to

develop bespoke service solutions, building a trusted reputation

within our local communities, or supporting policyholders with

empathy and respect.

We know that a personal touch remains crucial to the success of our

business and that will never change.

However, customers now expect 24/7 service, fast responses, and

seamless experiences; and as they become more familiar with AI

capabilities and use it more in their daily lives, it’s only natural that

they will expect a tech-focused company like Copart to utilise it to

provide further value and convenience.

We see AI as a useful tool to streamline and accelerate what we

already do, making us even more efficient and further enhancing the

customer experience. For us, it all comes down to data, and that’s

something we have plenty of!

Our data pool is expansive, from our patented online auctions to

real-time data and insights, and bespoke analytical and reporting selfservice

tools. Everything we do is data-driven, and the deployment of

AI can leverage this data even further to create even better value for

our customers.

Overlaying AI onto our current processes to allow even deeper data

analysis opens the door to unlimited possibilities in a world where

speed and convenience are at the top of everyone’s agenda.

We’re already using AI for the scheduling aspects of our online

auctions and for certain operational processes, including vehicle

damage assessment. But there is scope to do so much more, from

personalised recommendations and offers, to fraud detection and derisking.

The opportunities are endless!

We’re now exploring more AI technologies to help drive our

efficiencies, add value, and support more informed decision-making

both in the integrated services we offer our sellers and through our

auction platform for our buyers – to drive more competition and

ultimately deliver higher returns at our auctions.

We’re excited about the opportunities ahead to deliver a truly tailored

journey for our customers, enabling us to be even more proactive

when it comes to anticipating their needs and giving us the tools to

create more engaging and relevant experiences.

As a forward-thinking industry leader, it’s important to us that we

lead by example and we’re determined to remain at the forefront

when it comes to adopting the latest digital innovations.

Copart. Always moving forward.

Jane Pocock,

CEO, Copart UK & Ireland

Harnessing AI To Enhance

Service and Efficiency Within

the Claims Process

The industry must strike the right balance between

automation and human involvement to ensure

technology enhances the claims process rather than

harming the customer experience.

As the insurance landscape continues to evolve, the integration of

Artificial Intelligence (AI) into claims handling processes is no longer

optional and at QuestGates we recognise both the potential and the

responsibility that comes with implementing AI. The integration of AI

across key areas of our group marks an exciting time at QuestGates

and one that enhances efficiency, supports better decision making

and ultimately strengthens the service we provide. Our customer-first

approach ensures that while we embrace automation for efficiency,

we never lose sight of the human element that remains vital to

delivering award winning customer service - especially during times of

distress.

We are actively embedding AI that allows us to assess high numbers

of incoming claims quickly to enhance speed, accuracy and

responsiveness. One key change area is at first notification of loss

(FNOL) stage, where generative AI is increasingly being used to

reduce manual handling. This allows us to fast-track low complexity

claims while flagging those requiring expert human oversight.

AI also plays a pivotal role in automating personalised communication,

drafting updates and explanations of cover, helping claimants to

feel more informed and supported during emotionally charged and

sensitive situations.

Additionally, we are implementing AI-driven systems that can

assess high volumes of incoming claims and allocate them to the

most appropriate resource. This not only speeds up the process

but ensures accuracy and effective use of adjuster and contractor

resources, optimising cost and reducing delays.

The implementation of AI has brought about several operational and

strategic advantages.

These include significantly reduced response times, which helps

mitigate secondary losses and improves customer outcomes and with

improved consistency in decision-making, increasing the accuracy of

outcomes. AI also helps us assign the right adjusters geographically,

increasing efficiency and reducing environmental impact, aligning

with our ESG commitments.

Going forward we’re particularly excited about the role AI can play

in enhancing decision support for our adjusters. Tools that can

summarise complex contracts, flag key liability issues and learn from

historical claims data will increasingly empower our adjusters to make

faster, more accurate assessments.

At QuestGates we view AI not as a replacement for our people, but as

a tool that continually helps to enhance service levels. Our approach

has been to adopt AI to assist human expertise, not replace people.

By combining cutting edge technology with human judgement and

empathy we’re ensuring customer interests are always put first.

Greg Laker,

Director, QuestGates


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EDITORIAL BOARD

The Evolving Role of AI in Witness

Statement Taking, Translation,

and Client Contact

AI is transforming and improving efficiency across the insurance

and legal sector. However, as AI continues to evolve over the

next one to two years, concerns surrounding its limitations,

especially accountability of potential errors must be carefully

considered.

Witness Statement Taking: Efficiency vs. Human Judgment

AI-powered transcription tools are enhancing accuracy in witness

statement drafting. Real-time speech-to-text conversion and pattern

recognition allow AI to flag inconsistencies, making the process more

efficient. However, AI lacks human intuition, a crucial element in

evaluating witness credibility.

While AI can organise and highlight discrepancies, it cannot truly

replace the judgment of experienced professionals. It is imperative

that evidence should be drafted in a client’s own words however some

clients can innocently use words in the wrong context and not based

on their literal meaning. Overreliance on AI in this process could result

in flawed case arguments, weakening legal outcomes, and damaging

trust between clients and legal professionals.

Translation: Accuracy vs. Accountability

AI-driven translation tools are advancing rapidly, allowing legal

teams to overcome language barriers more efficiently. Complex

legal documents and live conversations can be translated nearly

instantaneously. However, AI translations may miss cultural nuances,

colloquialisms and specialised terminology, potentially leading to

serious misinterpretations.

More concerning is the lack of accountability when translation

errors occur. Humans can be questioned, cross-checked, and held

responsible for inaccuracies when taking and translating witness

statements. AI, however, lacks such personal responsibility when

errors inevitably happen. Misinterpreted documents or statements

could jeopardise a case, with no clear accountability for the failure.

Who will attend court to give the judge a credible and professional

explanation why the translation is inaccurate if the handler does

not understand what they have sent to their client? The CPR sets

out a stringent process for first language evidence, which is being

meticulously enforced by courts due to issues with clients being

unable to understand their own evidence when it hasn’t been

obtained in their first language.

Client Contact: Accessibility vs. Loss of Human Connection

AI-powered chatbots and virtual assistants provide law firms and

insurers with faster, more accessible client communication. AI can

make routine inquiries, schedule appointments, and provide general

legal guidance efficiently. Overall, this has largely been successful

for a number of firms but the impersonal nature of AI risks alienating

clients in sensitive legal matters, where human connection is vital.

Empathy is a critical component of client relationships, particularly

when dealing with emotionally charged cases. AI is unable to respond

appropriately to a client’s distress. Clients may feel unheard or

undervalued when interacting with automated systems, rather than

legal professionals and older clients or those less adept with such

technology may become frustrated in the process due to lack of

human connection and expertise and give up on the process entirely.

Additionally, privacy concerns arise as AI stores vast amounts of

sensitive legal data, increasing cybersecurity risks.

Balancing AI and Human Expertise

Insurance and legal professionals should ensure human oversight,

particularly in evidence collation, translation accuracy, and client

interactions. Integrating AI whilst can undoubtedly enhance

efficiency and when balanced with human supervision it can do so

without sacrificing accountability, trust, or compassion. AI’s role in

our industry will continue to grow, but elements of human expertise

remain irreplaceable in ensuring justice and integrity throughout the

legal process and the development of client relationships.

Gilly Daniels,

Managing Director, Witness Wise

Driving Efficiency: How AI is

Gaining Traction in Motor Claims

Artificial intelligence (AI) is already making its mark on

the motor claims sector - adoption rates are varied and,

in many cases, organisations are taking tentative steps

with the focus on enhancing claims routing and workflow

optimisation, helping insurers and claims services

providers handle claims more efficiently.

In a recent article in Modern Insurance Magazine our MD, Mel

Bebbington, wrote about how a key focus area for Auxillis in 2025 is

to leverage advanced automation technologies, data analytics and

AI powered decision making to identify and eliminate bottlenecks,

optimise workflows and automate repetitive tasks. However, beyond

that AI has the potential to rapidly reshape the motor insurance claims

landscape, creating exciting opportunities to enhance efficiency,

reduce fraud, and improve customer experience. At Auxillis our

technology roadmap recognises that while AI’s potential is broad,

certain areas stand out as especially promising.

One of the most transformative is First Notification of Loss (FNOL).

That critical first step when a customer reports an accident is an area

where we believe Auxillis makes a real difference, but we are exploring

how AI can support our expertise by streamlining traditional FNOL

with AI-powered chatbots and virtual assistants that can gather

incident details through natural language processing (NLP). These

systems don’t just capture data, they interpret it, triage the claim,

and in some cases, make initial decisions instantly. By predicting the

complexity or potential value of a claim, AI can automatically assign it

to the right handler or escalate it for further review. This ensures that

resources are used effectively, customers receive appropriate service

levels, faster resolutions are achieved, and higher levels of customer

satisfaction follow.

The use of AI in automated damage assessment is not new and

within the industry there are already solutions delivering real-time

cost estimates, accelerating repairs or total loss settlements by using

machine learning to identify impacted parts and estimate repair costs

in seconds. However, where AI is starting to deliver significant added

value in this area of the claims journey is by detecting manipulated

or fake photos of damage and it is in the fight against fraud where AI

may have its biggest impact.

Fraud detection is a key, high-impact area where AI will excel and

become the norm, not least because as quickly as we adopt AI so

do those intent on committing fraud. At Auxillis we are looking at

available solutions where AI models can analyse patterns across

vast datasets, flagging anomalies or connections that today’s fraud

preventions processes might miss. Network analysis, deepfake

detection, and document forgery recognition will increasingly

become vital tools for all of us operating in the claims supply chain in

identifying sophisticated fraud schemes early in process.

Phillip Witterick,

Commercial Director, Auxillis


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EDITORIAL BOARD

AI in the legal sector

With a focus on Consumer Duty, our service

level as a legal supplier is key, but there is also

a constant challenge to provide a service that is

faster and more economical. AI is an important

part of delivering against that demand. The power

and progress of AI are clearly exponential, so it

is vital that we apply it now to gain the maximum

advantage in future years.

The biggest potential lies in the combination of Large

Language Models and Generative AI, which use the power

of artificial intelligence to assess and understand data

and produce content. This could include preparing claims

information, valuing claims, drafting email responses and

sending data and communications. However, this process

needs careful control to ensure that any use of the technology

is closely supervised and that human decision-making is

included at the right time before the output is used.

In preparation, we have been working with OCR technology

to ensure that all documents are readable. We are training

models to extract data, categorise the type of document and

decide the actions needed as a result. Where possible, we

have been working with the insurance supply chain and our

business partners to import data so that more control and

certainty can be maintained.

We have seen substantial benefits from the use of Agentic AI,

implementing systems that monitor claims and take action

within the process where required. In our motor recoveries

team, this has allowed us to cut our time to issue to a third,

reduce touchpoints by half and double efficiency over the last

three years. We are now rolling this technology into our OIC

injury processes with the aim of driving similar progress there.

Conversational AI is also helping with customer

communication. We are very aware of the dislike of chatbots

by many, but where standard information is needed, such as

claim updates, customers can see the benefit if it is easy to

use and they have a choice. The key is to ensure that this is

only one channel of communication and that customers can

still speak to their claim handler or adviser if they prefer.

The most important element of any implementation, however,

is our people. They remain wary of artificial intelligence,

not only because of the potential impact on their roles

but also due to concerns about the service to clients and

the confidentiality of their data. We need to work hard to

explain the controls and checks we are implementing and to

emphasise that this should remove administrative overhead,

allowing them to focus on strategy and advice, where they

can continue to add real value.

Mark Savill,

Managing Director, Lyons Davidson

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EDITORIAL BOARD

Revolutionising the Customer

Experience Through AI and

Digital Innovation at National

Windscreens

The customer journey at National

Windscreens has been transformed by a

continuous focus on digital enablement

and innovative AI, empowering

policyholders with omnichannel

communication options, including web

call-back services, AI-powered chatbots,

SMS, email, and phone. We also utilise

natural language processing (NLP)

technology and have simplified the

customer journey, reducing dependency

on telephone support which has driven

increases in digital adoption.

Additionally, the AI technology on the website

decides whether a windscreen can be repaired

with over a 96% accuracy. Drivers simply

upload a photo of the damaged area to the

website and a decision regarding whether a

replacement or repair is provided in seconds.

This is a vast improvement compared to the

traditional approach of asking questions about

the damage to the glass.

This innovation was a UK first for the

windscreen industry and has made a

significant impact on the speed of service for

the customer. The web booking satisfaction

score shows that over 98% of customers

were happy or satisfied with the journey.

This has been achieved by implementing a

continuous improvement loop by working with

our partners in fleet and insurance to provide

feedback to our developers.

Through real-time feedback, operational

analytics, and AI modelling, we are

embedding a claims journey that evolves with

customer expectations. In the near future, AI

will help to automate triage and increasingly

streamline the journey for even faster

resolutions for policyholders.

We are exploring other ways of implementing

AI into other processes within the business.

The distribution of glass is a critical part of

the repair and replacement industry, and

automation has the potential to improve

our efficiency even further. From a logistics

perspective, there are excellent opportunities

for optimising the supply to our main

distribution centres and then to our fitting

centres across the UK. We are constantly

developing our delivery by enhancing our

forecasting accuracy and implementing

dynamic scheduling of our glass supply, so we

can ensure the resources are in place to meet

demand.

Our digital team has created a fantastic

process for our policyholders, and by utilising

AI, we can leverage the benefits of technology

whilst still emphasising the importance of the

human touch in our service. Our people are

the cornerstone of National Windscreens; and

technology will never replace a reassuring

smile and the professional service our team

provides to customers across the UK.

James Reynolds,

Director of Commercial and Finance, National

Windscreens


EDITORIAL BOARD

AI at the Wheel: How Thatcham Research

is Powering a Data-Led Future for

Automotive Risk and Insurance

The automotive industry stands at a technological crossroads,

with artificial intelligence rapidly evolving from a futuristic

concept to a practical tool reshaping how vehicles are

designed, maintained, and insured. At Thatcham Research,

we’re witnessing firsthand how AI and advanced data analytics

are revolutionising our understanding of Automotive Risk

Intelligence.

Our recent investments in cloud data infrastructure reflect our

commitment to harness the exponential growth in data from vehicle

testing, parts information, and repair times. This strategic direction

isn’t merely about technological advancement, it’s about equipping

our industry with the tools to navigate an increasingly complex risk

landscape.

For insurers specifically, the integration of AI into vehicles presents

both opportunities and challenges. Our research indicates that many

current AI applications in vehicles from Advanced Driver Assistance

Systems (ADAS) to predictive maintenance algorithms pose minimal

risk of causing unexpected vehicle behaviour. Most systems deployed

today are type-approved and cannot alter safety critical parameters

post-approval, providing a level of certainty that insurers can reliably

factor into risk assessments.

However, the landscape is evolving. Systems like Mobileye’s Road

Experience Management (REM) represent a new frontier where

AI continuously learns from crowdsourced driving data. While our

analysis suggests the risk profile remains very low with built-in

safeguards preventing significant deviations from expected behaviour,

these technologies warrant ongoing monitoring as they become more

widespread.

What’s particularly promising for insurers is AI’s potential to enhance

predictive capabilities around repair costs which is a significant

component of insurance expenses. By combining traditional

automotive technical expertise with sophisticated data analysis, we’re

developing unrivalled, data-led insights that will help insurers better

understand and forecast claims expenses.

We’ve identified that some sectors of the industry remain vulnerable

due to knowledge gaps in specific risk areas. This is precisely

where robust data utilisation becomes critical. AI-driven analytical

techniques applied to complex forms of data including video and

sensor inputs will yield valuable insights that reshape industry

standards and benefit insurance pricing models.

For the insurance community, it’s worth noting that virtual voice

assistants and navigation services that employ continuous learning

show potential to reduce incident risk by minimising driver

distraction. Our analysis suggests these systems support safer driving

practices rather than introducing new risks.

Looking forward, we anticipate developments in areas such as

occupant monitoring, theft alert systems, cabin preconditioning

based on user patterns, and more sophisticated battery management

in electric vehicles. These applications will likely enhance vehicle

value while potentially reducing certain claim frequencies.

The insurance industry has always thrived on data-led decision

making. What’s changing now is the sophistication of the data

available and our ability to derive meaningful interpretations from

it. At Thatcham Research, we’re committed to providing the bridge

between automotive technical expertise and risk data, offering

insurers the credibility and proof needed to substantiate predictions

in an increasingly AI-enhanced automotive world.

Jonathan Hewett,

Chief Executive, Thatcham Research

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ASSOCIATIONS ASSEMBLE

Welcome to Associations Assemble!

Modern Insurance Magazine is delighted to be joined by some of the leading

names from our industry associations, organisations and institutes!

This issue voices the thoughts of:

Sue Brown

Chair, Motor Accident

Solicitors Society (MASS)

Michael Keating

CEO, Managing General Agents’

Association (MGAA)

Matthew Tuff

Association of Personal

Injury Lawyers: (APIL)

Dr Matthew Connell

Director, Policy and Public Affairs,

Chartered Insurance Institute

(CII)

Anthony Hughes

Chair & CEO, The Credit Hire

Organisation (CHO)

Emma Chapman

Conference Director, British

Insurance Brokers’ Association

(BIBA)

Pete Allchorne and Jo Folan

of the Strategic Advisory team at

DAC Beachcroft’s claims division,

CSG (FOIL)

David Punter

President, Institute of Automotive

Engineer Assessors (IAEA)


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Sue Brown

Title: Chair

Association: Motor Accident Solicitors Society

(MASS)

Civil Liability Act Under

Review: An Opportunity

to Address Unfairness and

Refocus Reform

Several MoJ Ministers have now confirmed that there will

be a post-implementation review of the Civil Liability Act

(CLA) and the so-called whiplash reform programme.

Post-Implementation Reviews (PIRs) were established by

the Small Business, Enterprise and Employment Act 2015

to periodically evaluate the effectiveness, appropriateness

and impact on businesses and the public of a regulation

or legislation. The PIR of the LASPO Act in 2018 was a

monumental affair with three rounds of consultative group

meetings with interested parties, a roundtable with members of

the senior judiciary, meetings with individual practitioners and

small groups of interested parties, and a public consultation

which received submissions from over 80 organisations. The

result was a report just shy of 300 pages with various actions.

Of course, the PIR of the CLA will not be as big a deal as

assessing LASPO. However, we can certainly expect a formal

consultation and evidence gathering process ahead of internal

evaluation and publication of a report in Parliament probably

just before May 2026, five years since implementation of the

CLA.

Whilst many in the claimant sector, including me, would

probably prefer for the CLA to simply be scrapped, this is not

going to happen. The PIR will almost certainly conclude that

government intervention is still required, and so will instead

focus on whether the legislation and the wider reforms can be

refined, improved or simplified.

Our view is that the CLA and the wider reforms have

significantly fallen short of many of the stated policy objectives

and those that have been met, have adversely impacted motor

accident victims in a system that remains fundamentally unfair

and balanced in favour of defendants. However, there are a

number of changes that can be made to make the process

easier and quicker, fairer and with better governance and

oversight. Together such changes can mitigate some of the

worst impacts of the reforms and give a better and fairer deal

to motor accident victims.

Michael Keating

Title: CEO

Association: Managing General Agents’

Association (MGAA)

The Incubators

Supporting the Next

Generation of MGAs

The MGA sector is thriving, with innovation at its core.

As entrepreneurial teams launch new businesses to

challenge traditional insurance models, they encounter

both opportunities and hurdles, from securing capacity

to meeting complex regulatory demands. This is

where incubator suppliers come in. Acting as strategic

partners, they provide the operational, regulatory,

and technological support needed for MGAs to scale

successfully.

Incubators typically take a holistic approach, covering

compliance, risk management, underwriting frameworks, and

capacity relationships. By evaluating business plans early on and

testing them against market insight, incubators help new MGAs

avoid costly mistakes and build resilient, future-ready businesses.

One of the most significant challenges for start-up MGAs is

regulatory compliance. Incubators guide businesses through

the FCA framework and beyond, often acting as Principals while

MGAs trade as Appointed Representatives. Their expertise in

governance and operational resilience is vital to building longterm

sustainability and market credibility.

Scaling with Confidence

As MGAs grow, incubators ensure they can scale responsibly,

whether by entering new markets, launching products, or

securing capacity. Strategic oversight, access to partnerships,

and compliance support allow MGAs to focus on their strengths:

underwriting, innovation, and customer engagement.

Incubation as a Stepping Stone

The end goal is independence. Incubators help MGAs mature

into fully regulated, self-sufficient entities. Successful incubations

result in businesses equipped with the right infrastructure,

partnerships, and compliance tools to thrive.

Within the MGAA ecosystem, incubators such as Davies, DA

Strategy, Pro MGA Global Solutions, Criterion, Marley Risk

Consultants, ADVA Risk Partners, Vault, Euclid Financial and

Professional Risks, and Ithaca Underwriting exemplify how

collaboration fuels success. Many of our 125 supplier members

also support MGA Incubator members, offering end-to-end

services that form a valuable launchpad for innovation.

As the sector evolves, incubator suppliers will play an

increasingly vital role, embedding AI,

automation, and data-driven strategies

to help MGAs stay ahead. The

future belongs to those who

combine vision with the

right support systems, and

incubators are at the heart of

that journey.



Matthew Tuff

Title: President

Association: Association of Personal Injury

Lawyers: (APIL)

Ten Years of Injustice:

How ‘Fundamental

Dishonesty’ Powers

Are Being Abused to

Deny Injury Victims Fair

Compensation

This year marks 10 years since the Criminal Justice

and Courts Act was passed. It established the power

for blanket dismissal of an injured person’s claim for

compensation if the court is satisfied ‘on the balance of

probabilities’ that the claimant has been ‘fundamentally

dishonest’.

APIL warned of the injustices of this legislation, including

that unscrupulous insurers would be free to make spurious

allegations of fraud.

A decade on, the increasing abuse of the fundamental

dishonesty rules is a serious issue. The rules are being utilised by

defendants as a cynical ploy to avoid paying compensation to

honest injured victims of negligence.

Legitimate challenges by defendants are perfectly acceptable.

When a claimant is clearly lying, they probably deserve to have

their case dismissed. But APIL members report that they are

seeing more and more allegations of fundamental dishonesty

where the courts have then gone on to find the allegations

cannot be characterised as such.

Defendants seem to face few consequences when their

allegations fail, and the rules do not apply both ways. If

defendants were to be dishonest, they do not face the same

sanctions as claimants.

The tactic is an example of an organisation with deep pockets

trying to financially and psychologically exhaust their opponent.

It is comparable to the use of SLAPPs (strategic lawsuits against

public participation) to silence public interest investigations and

reporting.

But in personal injury claims the ‘opponent’ is a vulnerable

injured person whose life has been shattered. It is likely

that abuse of the rules affects people with

severe and life-changing injuries

disproportionately as there is more

money at stake for insurers in these

cases and greater motivation to

give the tactic a try. It is awful

that on top of everything else

injured people face, they are

wrongly accused of being

dishonest.

Dr Matthew Connell

Title: Director, Policy and Public Affairs

Association: Chartered Insurance Institute

(CII)

AI in Insurance: It’s

Older Than You Think

AI has been around for longer than we think -

insurers have been using AI, especially in pricing and

underwriting, for about two decades. Despite the

current hype around generative AI, the last big advance

in the maths underlying this science was a breakthrough

made by Google back in 2017.

In risk management, underwriters use neural networks to analyse

correlations between risk factors – one article published by

Google claims that AI has helped one major insurer to predict

‘large loss’ accidents with 78% accuracy.

In claims, AI can triage losses swiftly to the right specialists,

speed up claim settlements and help to detect fraud.

However, as insurers have increased their use of AI, they have

encountered ethical challenges. The impetus of preserving

customer trust through ethical behaviour has made the most

robust insurers develop a range of approaches, that include:

Raising awareness throughout the firm: not only among

those who commission and design AI systems, but all other

stakeholders.

Implementing access and redress mechanisms for customers,

who should be able to understand how decisions made or

recommended by machines impact their welfare, and be able to

challenge decisions that they believe are unfair.

Establishing accountability, backed up by validation and testing:

institutions should be held responsible for decisions made by

the algorithms that they use, even if it is not feasible to explain

in detail how the algorithms produce their results. Equally,

institutions should be able to explain how algorithmic decisions

are made, or show how they monitor outcomes and intervene to

prevent harm, for scrutiny by regulators. This includes describing

how training data has been collected.

We don’t need to be scared by AI, it’s already with us and the

sky hasn’t fallen in. But for its benefits to be sustainable, we must

handle it ethically.



Anthony Hughes

Title: Chair & CEO

Association: The Credit Hire Organisation

(CHO)

The Role of AI in

Transforming the

Insurance Industry

Pete Allchorne & Jo Folan

Dr Matthew Connell

Title: Director Strategic Advisory of Policy team and at Public DAC Affairs Beachcroft’s

claims division, CSG

Association: Chartered Insurance Institute (CII)

Association: Forum of Insurance Lawyers

Fairness First: Managing

Bias in AI to Build Trust in

Insurance

Artificial Intelligence (AI) is reshaping the insurance

industry by streamlining operations, enhancing

customer experiences, and improving risk assessment.

As insurers face increasing pressure to reduce costs

and deliver personalised services, AI has emerged as a

powerful tool to meet these demands.

One of the most impactful uses of AI in insurance is claims

processing. AI systems can analyse data, detect fraud, and

approve straightforward claims within minutes. This speeds up

the process and reduces human error and costs.

In underwriting, AI algorithms assess risk more accurately by

processing vast amounts of data, including social media activity,

driving records, and wearable device data. This supports more

precise pricing models and tailored policies.

Customer service has also been transformed by AI through

chatbots and virtual assistants. These tools provide instant

support, answer questions, and guide customers through policy

purchases or renewals.

AI enhances fraud detection by spotting patterns that humans

might miss. Machine learning models flag anomalies and

suspicious behaviour, protecting insurers and customers from

losses.

However, AI raises concerns around data privacy, bias, and job

displacement. Insurers and regulators must collaborate to ensure

ethical, transparent use.

AI is driving innovation in insurance, streamlining processes and

enhancing customer service. Those who harness it effectively will

be best placed to thrive in a rapidly evolving market.

The insurance industry has taken huge strides towards

eliminating bias, perceived or real. It is perhaps not

surprising then that the industry is applying a good

deal of caution when it comes to the adoption of

Artificial Intelligence. Generative AI potentially offers

many benefits and efficiencies to insurers, particularly

in claims processing, but these will be undermined if

there is a lack of transparency / audit trail in AI decision

making, allowing bias to creep in.

The key to this is data. Data has always been central to

everything that insurers do, and yet historic data sets are unlikely

to be either entirely clean or complete. Much work can be done

by data scientists to improve historic data, but it is unlikely that

they will ever be perfected.

Where historic data sets reflect biases that insurers have

so successfully eliminated, be they related to demographic,

geographic or socio-economic factors, AI models trained on that

data may perpetuate those biases, thereby undermining fairness

and potentially attracting regulatory scrutiny.

What is important is that the risks are recognised and adequate

steps taken to ensure that AI models are built in such a way

to ensure their decisions are ‘explainable’; and that they are

monitored and used in such a way that their outputs are not

wholly relied upon for critical decision-making without a human

‘in the loop’. This will help to mitigate the risk of bias being built

into the system.

By ensuring diversity in the datasets used for training AI, models

are more likely to be fair and equitable. Likewise, establishing

ethical guidelines for AI use and regular audits will help to

mitigate against the risk of perpetuating bias.

Ultimately, insurers should be able to unlock generative AI’s

potential to assist by ensuring that the framework within which it

is deployed allows for adequate checks and balances. However,

fairness should never be compromised in pursuit of efficiency.


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All rights reserved by Rapid Repair Network. Company No 10749977.


Emma Chapman

Title: Conference Director

Association: British Insurance Brokers’

Association (BIBA)

How Insurance Brokers

Are Embracing the Power

of AI

Artificial Intelligence (AI) is increasingly becoming part

of the everyday operations of insurance brokers across

the UK. When launching BIBA’s AI Guide, we surveyed

members to find out which benefits of AI they were

most keen to explore. The responses pointed to two

major attractions: enhanced decision-making through

better data insights, and increased productivity thanks

to improved operational efficiency.

We’re already seeing brokers using AI across various areas, from

policy comparisons and claims management to HR functions,

fraud detection and customer analysis. These early adopters are

setting a benchmark for what’s possible when AI is thoughtfully

integrated into business workflows.

The improvements are tangible. Some members have reported

that AI has helped them reduce repetitive tasks, freeing up time

to spend directly with customers. One broker explained how

AI enables them to handle high volumes of quote submissions

by reformatting data from different sources into a consistent

structure. Others are using AI-driven insights to understand their

customer base better, identifying query trends and risk profiles

to tailor services more effectively. Tools like Microsoft’s Co-Pilot

and Notebook LM are proving especially useful for drafting

documents, generating briefings, summarising meetings and

analysing prospects.

Of course, alongside the benefits, brokers are approaching AI

integration with a strong focus on responsibility and ethics.

BIBA encourages members to develop AI policies that outline

both risks and opportunities, and to be clear on how legal and

regulatory obligations apply to their AI use. Key risk controls

include removing confidential or personally identifiable

information before using public AI tools and ensuring robust

cyber security protocols are in place. It’s also vital to scrutinise

how external service providers are using AI and storing any

shared data.

That said, integrating AI doesn’t come without its challenges.

Some members cited issues such as high costs, integration with

legacy systems, and regulatory uncertainty. Gaining employee

buy-in can also be difficult, which is why a clear roadmap,

supported by an internal steering group, can make a big

difference. Data quality is also critical; AI is only as effective as

the information it processes.

David Dr Matthew Punter Connell

MInstAEA Title: Director MFIEA of Policy MIoD and Public CAE MIMI, Affairs

Association: Title: President Chartered Insurance Institute (CII)

Association: Institute of Automotive Engineer

Assessors (IAEA)

Defining the Future

Engineer Assessor:

Competence, Compliance

and the Role of AI in a

Changing Industry

Being a public facing entity designed to promote

the professionalism of its members, highlighting the

current complexities of repair in the field, and the core

responsibilities to define safe outcomes for both repair

and disposal following loss, the IAEA are reviewing and

defining what the modern engineer assessor needs to

be to deliver to be of both benefit and control.

This means further enhancing our training, Development and

indeed Career Path Development to give market leading support

to the community, and removing the barriers to adoption for

core competencies required to deliver the modern services.

These are the challenges we face, as it is all too clear that whilst

AI is a clear way to automate good practices and enhance

human capabilities, good practices are often talked about

widely but never tested and delivered, which can make for a

disappointing outcome from engineering as a whole in the

industry.

AI will move decisioning dramatically over the next few years,

and machine learning will benefit many, but as the old adage

states “Garbage in- Garbage out”, and this is the key point we

need to bolster for the future to ensure compliant engineering,

which is backed by core competencies and skilled technicians

are the bedrock for the learning to grow properly.

This approach gives an ethical, independent, verified and trusted

outcome which systems can be built upon to ensure that we

never lose focus of what the task is all about.

Will the IAEA ever move into AI systems being delivered for,

and by its members? Well don’t be surprised if you begin to see

some signs of change stemming out from our core research

to deliver a strong baseline for skilled recognised and qualified

specialists moving to training and education / verification

backed by AI and machine learning moving ahead.

Looking ahead, brokers are excited about AI’s potential to

enhance customer outcomes, streamline services and drive

competitiveness in the market. As part of its

mission, BIBA is committed to supporting

members, particularly SMEs, through

this transition. Our AI Guide, packed

with case studies and insights,

offers practical help for those

looking to navigate this new

landscape, without losing sight

of the core values of insurance

broking.


ILC

Built for claims professionals,

by claims professionals

Upcoming events

To find out more about ILC's

activity calendar for 2025 contact

rachael@iloveclaims.com

iloveclaims.com

FEATURES

Harnessing AI

for a better

tomorrow

Artificial intelligence (AI) is

transforming the insurance industry.

As highlighted at a recent ILC event,

a senior figure from a leading insurer

noted that “technology will be the

key differentiator between those who

succeed and those who fall behind.

The industry must embrace AI,

analytics, and digital transformation.”

But how exactly is AI impacting

insurance today - and what might it

mean for the future?

From enhancing customer experience

and streamlining claims processes

to improving fraud detection, AI

has moved beyond theory and into

practical, day-to-day applications.

As one speaker at a recent ILC

conference observed, “AI has been

a buzzword for a while now, but

we’re finally seeing real-world

implementations reshaping claims

handling.”

Insurers are using AI to reduce

administrative workloads, automate

repetitive tasks, and deliver faster,

more accurate decisions. This not

only boosts operational efficiency

but also allows claims professionals

to focus on delivering empathy and

expertise in complex situations.

Customer expectations

Major insurer – amongst many others

- are investing in AI to meet evolving

customer expectations. “Customers

increasingly demand fast, seamless

digital interactions. We need to invest

in technology to deliver on those

expectations,” a key figure within

a leading insurer explained. One

such initiative involved the real-time

analysis of claims using AI-powered

models, enabling quicker and fairer

settlements. “It’s about improving the

experience for the customer while

also making our operations more

efficient.”

Across ILC’s suite of events, a

recurring theme is the balance

between technology and people. The

consensus is clear: AI should enhance

- not replace - human roles. “AI and

automation aren’t about replacing

people; they’re about enabling them

to do their jobs better,” one panellist

explained at an event. Another

contributor added, “We’re using AI to

eliminate mundane tasks, so handlers

can concentrate on situations that

require emotional intelligence and

specialist knowledge.”

Focus on the complex

Another conference participant

recently outlined how AI can triage

claims by urgency, allowing human

handlers to focus on complex,

emotionally charged cases. “A

minor vehicle dent might be fully

automated, but a life-changing injury

claim will always need a human

touch,” they explained.

AI is also unlocking new possibilities

in personalisation. In underwriting,

machine learning models are being

used to develop more tailored

insurance products, built around

individual risk profiles. In claims, AI

is evolving from simple automation

to predictive analytics and hyperpersonalisation.

The ability to analyse

large datasets and uncover trends

allows insurers to anticipate needs

and optimise the claims journey.

However, the integration of AI

also brings challenges - especially

around regulation, ethics, and data

governance. Discussions consistently

point to the need for evolving

regulatory frameworks to ensure

AI is implemented responsibly.

There are ongoing calls for greater

clarity on algorithmic transparency,

governance, and the mitigation of

potential bias.

The future

Looking to the future, AI’s role in

insurance is set to grow even further.

Predictive modelling will become

more sophisticated, automation more

intuitive, and customer journeys more

personalised. Yet, the key to success

lies in striking the right balance. As

one prominent industry voice put

it: “Technology should enable - not

replace - human judgement. A human

touch remains essential, not only

for managing outcomes but also for

building and maintaining consumer

trust.”

As the claims sector adapts to

an increasingly digital world, ILC

continues to provide a platform

for sharing insights, encouraging

collaboration, and spotlighting

innovation. By supporting informed

adoption of AI, the sector can work

together towards a smarter, fairer,

and more resilient future - one that

truly embodies a better tomorrow.


Transforming

Prosthetic Care:

London Prosthetic Centre’s

New Facility and What it

Means For You

In the highly competitive medico-legal prosthetics landscape, one factor

continues to heavily influence referral decisions: access to high-quality

rehabilitation. "It’s not just about providing a prosthesis anymore — that’s

expected. What people want now is a holistic approach, a one-stop shop.”

Said Belinda Smart, Business Manager at the London Prosthetics Centre.

It’s precisely this need that has shaped the design of the new LPC facility,

featuring a fully-equipped prosthetic clinic, manufacturing services, and

rehabilitation studio all under one roof.


FEATURES

LPC Prosthetists Danny van der Merwe and Carl Cato, sat down

alongside Belinda to discuss the new facility and how it will

benefit you and your clients.

QWith expanding fitting capabilities and on-site

rehabilitation available at the new LPC site, how do you

see this changing the client experience?

Carl: “By expanding into a bigger and better facility, we’re able

to spend more time with clients. It’s not about finishing the job

quickly or seeing as many clients as possible in a day — we’ll

have the time to focus on each individual client in the right

setting, enhancing their overall experience.”

Q

How critical is it for prosthetic providers to offer

integrated physiotherapy and occupational therapy

when it comes to securing referrals and fostering longterm

patient engagement?

Danny: “OTs look at ADLs, the home, the community

environment, and return-to-work plans. We provide the limb, and

our OTs and physios bring that realistic, bigger-picture approach.

It’s crucial. Our new facility offers clients everything under one

service but with a whole team of experts at hand.”

Belinda: “We’re the dream team—our technical team in particular

is exceptional. They’re a small but dedicated group, and we all

work really closely together. They understand our ethos—that what

they’re producing has to be good enough for their own family. They

won’t hand over a device until it meets that standard. That level of

pride and care carries through everything we do.”

QWhat does LPC do to advocate for inclusivity, particularly

for those who may not have access to private or legalfunded

care?

Danny: “We often offer advice with no obligation, providing

guidance and support, even when we can’t offer direct care. It’s all

about helping people to the best of our abilities to ensure more and

more people are included.”

Carl: “We’re thrilled to be part of the Eqwal group, who have

emphasised that “inclusion is a global struggle.” Our work, with

Eqwal’s support, spans countries like Thailand, Saudi Arabia, and

Kuwait. On a day-to-day basis, even if someone cannot afford

treatment, we offer valuable advice, which our clients greatly

appreciate. The amputee community is relatively small and we’re

proud of our reputation for inclusive, patient-centred care.”

QWhat benefits will medico-legal professionals and

patients see from having assessment, manufacturing,

fitting, and rehabilitation all under one roof at LPC?

Carl: “It enables a more collaborative, structured clinical

approach and improves planning. With everything in one place,

we can use objective outcome measures to evidence clinical

decisions and report progress back quickly to those funding the

case. That level of collaboration and communication is key.”

QWhat role does environment play in patient

engagement and recovery, and how might the new

space help motivate clients throughout their journey?

Danny: “A professional, patient-led space helps people visualise

their rehab goals and feel motivated. The new facility is warm,

welcoming, and modern — it encourages people to show up

and engage, even with something as routine as an appointment.

Seeing the gym and the space around them can help patients

picture their progress.”

Belinda Smart,

Business Manager,

Steeper Group

QThe new site was designed with accessibility in mind.

How do these details reflect a broader shift in patientcentred

care and service excellence?

Carl: “We’ve left no stone unturned in trying to accommodate

everyone — whether that’s physical ability or cultural

considerations. For example, the facility features a wheelchair

lift, wide corridors and door frames, ramps, multiple disabled

parking bays, power-assisted doors, showering facilities, and

clear signage, and we’ve included a bathroom setup with our

Middle Eastern clients in mind. The new facility has allowed us to

improve where we simply couldn’t before. Our clients are at the

heart of everything we do, so providing a truly accessible space

was of utmost importance to us.”

Carl Cato,

Combined Services Private

Prosthetist, Steeper Group

QHow do you ensure high levels of personalisation and

emotional support remain central, even as your capacity

and caseload increase?

Carl: “By maintaining a team-based approach across the clinic,

including support from our technicians and admin staff, we

can ensure the best outcomes for each client. We’re being

deliberate about how we manage time—we’re not just trying

to fit everyone in. Instead, we’re prioritising client needs and

planning carefully so no one feels rushed or overlooked. We’re

committed to maintaining the same high standard of care by not

compromising quality. It’s a collaborative effort to make sure the

client experience still reflects who we are.”

Danny van der Merwe,

Senior Prosthetist,

Steeper Group


WELCOME

TO THE EXPERTS

IN RELOCATION

A New Property Portal created exclusively for our expert relocation team for the

benefit of our clients

Enabling us to offer fast, effective relocation with verified and compliant property

partners across the UK

Reduced costs due to direct bookings with owners removing any hefty commission

charges through booking platforms

Offering our customers and clients the best, most cost effective short term

accommodation available every time

www.accommodationconnect.co.uk

0330 123 1118 Powered by

enquiries@accommodationconnect.co.uk

Introducing

FEATURES

As the UK’s Private Rental Sector faces mounting pressures and reduced

landlord participation, securing quality short-term accommodation has become

increasingly difficult, especially for those handling insurance claims. In response,

A3 has launched Accommodation Connect, a fully verified, compliance-first

short-let platform that offers a safer, more reliable alternative to popular portals

like Airbnb, ensuring policyholders are placed in clean, legally compliant homes

when they need it most.

It is fair to say that we are entering changing and

challenging times for suppliers like A3 who offer alternative

accommodation and decant services to the insurance claims

industry and other sectors.

Whilst successive UK government’s desire to raise the standard

of rental properties and generally level the playing field

between landlords and tenants is commendable, there can

be no doubt that this is having a negative effect on the UK

Private Rental Sector (PRS). Mounting legislative demands

and reductions in tax incentives for landlords has increased

operating costs and eroded profitability, pushing up rents

and driving many ‘recreational’ or small portfolio landlords

to exit the market altogether. The well documented sharp

rises in rental prices across the UK over the past few years is

a consequence of both shrinking property supply and everincreasing

demand for rental properties.

At A3 we have monitored this slow erosion of the PRS and

have, first hand, encountered increasing difficulties in securing

good standard, legally compliant, clean and habitable

properties for our clients’ policyholders across many areas of

the UK. Securing a 6-month tenancy or lesser period used to

be commonplace but this is no longer an attractive proposition

for many landlords who want tenants who are committed to

a much longer-term. The high level of demand for popular

properties only results in a ‘beauty parade’ of would-be tenants

from which landlords have their pick.

However, contrasting the decline of the PRS is the rapid and

widespread rise of the ‘short-let’ market, driven by such popular

portals as Airbnb, VRBO and the like. It is no coincidence that

so many property owners have jumped on this bandwagon,

deserting the PRS for the highly profitable and legislatively less

demanding short let space. What were originally ‘holiday home’

booking platforms now service a huge and growing demand for

short-let properties in every corner of the UK, many of which

properties could never be described as a holiday home.

Whilst there is no doubt that these short-let rental prices are

much higher than their property cousins in the PRS, having

the flexibility to commit to only a few weeks accommodation

(or less) rather than say, a minimum of 6-months via a tenancy

agreement provides more than sufficient savings for short-term

occupants. However popular these are, it should also be noted

that short-let bookings have their perils.

A3 have had to contend with many such issues booked via

these platforms; properties which bear no resemblance to the

photos displayed; dirty or damaged properties; properties

that do not have valid Gas Safety or Electrical Installation

Certificates. We have even come across properties that have

been unlawfully sub-let on portals by tenants, cashing in on

the demand in certain areas. And when issues arise what is the

manner of redress for the occupant? Are landlords complying

with their obligations in the same way that they must do under

landlord and tenant legislation? Are refunds forthcoming? In

our experience, rarely so.

A3’s involvement in dealing with the multitude of issues that

can arise with bookings placed via these portals has left us

exasperated and dismayed. In fact, the frustration and lack

of support we have seen for customers with issues living in

such accommodation has led us to abandon the use of these

platforms altogether and has driven the creation of our own

internal short let booking portal – Accommodation Connect

Accommodation Connect is the result of ingenuity and

investment in a product that helps A3 service our insurance

clients’ demands for short-term accommodation for their

policyholders without the risk of placing them in uninhabitable

or undesirable properties. It is the culmination of the team’s

effort in replicating the popular portals, allowing property

owners to ‘advertise’ their properties with us without paying

the excessive commissions demanded by the popular portals

meaning that our short-let rental prices are always the most

competitive.

All property owners and their agents are verified before they

can upload properties – Public Liability insurance is mandatory,

and each and every property must conform to our strict

compliance requirements: GSC’s, EICR’s and where required

local authority short-let licenses have to be uploaded. Owners

must have an emergency out of hours number for any issues

arising during occupation. And most importantly of all they

must agree to our terms and conditions in advance which

places the legal obligations on them to deal with any issues in

a responsible and prompt way. Something that we can monitor

and enforce for our customers where necessary.

All of these factors, coupled with our Relocation Team’s

extensive experience in providing alternative

accommodation for over 15 years

means that Accommodation

Connect is at the cutting edge of

the supply of short-let solutions

for our clients across the

whole of the UK and Ireland.

Martin Oldroyd,

Managing Director,

A3 – Alternative

Accommodation Agency


In doubt about

a skin spot?

Check with SkinVision app

and get immediate feedback

Download using this QR

code and get one month of

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Clinically validated with over 90% accuracy

and trusted by over 3 million users worldwide

We have over 30 partners globally, including AXA and

Medicash. If you're interested in becoming one, or would like

more information about SkinVision, please contact us at

partners@skinvision.com

AI in Preventive

Care: Why Insurers

Should Look

Beneath the Skin

Artificial intelligence is transforming the

insurance industry, automating claims

processing, enhancing fraud detection, and

improving customer engagement. Yet one of

the most underutilised areas for AI in insurance

occurs before a claim is even made: prevention.

At SkinVision, we use clinically validated AI to empower

individuals to take proactive steps toward better skin health.

Our service enables users to scan moles or skin spots anytime,

anywhere, and receive instant feedback indicating either low

or high risk of skin cancer. A team of dermatologists provides

clinical oversight where needed.

This is more than just a digital tool. It is a powerful enabler of

healthier outcomes. And for insurers, it opens a new frontier in

managing risk proactively, through data-driven, human-centred

care.

The Preventive Power of AI: A Shift in the Insurance Paradigm

There is growing demand for the insurance model to become

more proactive. Traditionally, insurers have responded to illness

after it happens. But with rising healthcare costs and ageing

populations, prevention is becoming essential. Rapid advances

in AI are driving this shift, enabling insurers to support healthier

lives rather than simply covering costs.

Skin cancer is a compelling example. It is the most common

cancer globally, especially among fair-skinned populations

in Europe, North America, Australia, and New Zealand.

While incidence is rising, early detection dramatically

improves survival, reaching up to 99 percent when caught

early. The challenge is timely detection, where AI can play a

transformative role.

SkinVision: Early Detection That’s Mobile, Scalable, and Smart

SkinVision’s clinically validated AI lets anyone assess skin spots

using just a smartphone. The app instantly assesses risk and

advises next steps.

Now officially certified as a Class IIa medical device under the

EU’s Medical Device Regulation (MDR 2017/745), SkinVision

has independent validation of its safety, quality, and medical

purpose. It is no longer a self-certified app but a recognised

medical device across the EU and aligned global markets.

With over 3 million users and 5 million skin checks completed,

we are seeing the real-world impact of integrating AI health

tools with dermatologist networks, behavioural nudges, and

clinical oversight.

triage helps clinicians focus time and resources on patients who

truly need care, which is especially valuable in overstretched

areas such as general practice and dermatology.

Why This Matters to Health Insurers

For insurers aiming to reduce costs and increase value,

SkinVision offers:

• Early Intervention, Lower Claims: Treating early-stage

melanoma is far less costly than advanced cases. Prevention

shortens claims duration and severity across health, life, and

income protection products.

• Digital Access Across Geographies: From cities to rural areas,

the app ensures quality assessments anytime and anywhere.

This is vital for global insurers seeking equitable care.

• Engaged Policyholders: SkinVision drives digital engagement

and empowers users. This translates into stronger customer

relationships and better health literacy.

AI + Human Expertise: A Responsible Innovation Model

Trust is critical in healthcare AI. SkinVision combines automation

with dermatologist oversight, ensuring AI supports rather than

replaces human expertise.

Our model is trained on 200,000 selected images from a

dataset of five million submitted photos. This ensures depth and

diversity in its training data.

A Global Opportunity for Local Impact

Insurers worldwide face the same challenge: managing risk

upstream. SkinVision provides a preventive, affordable, scalable

solution. By embracing AI-powered skin health, insurers can

promote public health, reduce claims, and offer standout value

to health-conscious customers.

Let’s Rethink the Role of Insurance

Insurance has always been about managing uncertainty. Tools

like SkinVision help reduce that uncertainty through early

detection.

To our insurance partners and beyond,

the future is proactive. It is time

to move from treatment to

prevention.

Erik de Heus,

CEO, Skin Vision

Easing the Burden on Healthcare

Our 2024 UK data shows that just 5.7 percent of users require

an in-person follow-up. This presents a clear opportunity to

reduce unnecessary appointments and streamline care. Digital


After the successful inaugural Global Insurtech Awards in 2024,

the Global Insurtech Awards are back for their second year, celebrating

the innovation, creativity, and progress driving the insurtech

industry forward.

This year, we’re excited to welcome a panel of expert judges-some

returning, and some joining us for the first time, they bring a wealth of

knowledge and insight to the awards; their support is vital, and we’re

truly grateful for the time and expertise they contribute in helping us

recognise the very best in the industry.

Think your work deserves to be seen? Enter now and show the global

insurance community what makes you stand out.

KINDLY SPONSORED BY

MEDIA PARTNERS

Adam Sills,

CEO

CapSpeciality

Allyson Laurance,

VP

Emerging Technology

& Innovation

Prudential

Bryan Falchuk,

Managing Partner

Insurance

Evolution Partners

Chia Hock Lai,

Chairman

Responsible Fintech

Institute

Christopher Frankland,

Founder

InsurTech 360

Dr Florian Hamel,

Head of Applications,

MS Reinsurance

Dr Robin Kiera,

CEO

DigitalScouting

Grace Flowers,

Chief Underwriter Officer

Zurich Insurance-Spain

Joel A. Agard,

Global Head

of Innovation

Zurich Insurance

Group

John Huff,

President & CEO

Assoc of Bermuda

Insurers &

Reinsurers (ABIR)

José Prado,

CEO

Insurtech Brasil

Joseph Lapierre,

CEO

StartWithUs Digital

Lior Koskas,

CEO

Digilog UK

Matteo Carbone,

Global Ambassador

Italian Insurtech Association

Founder/Director -

IoT Insurance Observatory

Megan Kuczynski,

Senior Strategic Advisor

Insurtech Insights

Founder and CEO

ClimateTech Connect

Melissa Collett,

CEO

Insurtech UK

Paolo Cuomo,

Executive Director,

Gallagher Re

Philippe Knepfler,

Head of Development

and Chief Innovation

Officer

Covéa Affinity

Richard West,

Award Winning

Professional

Services Innovator

Rob Galbraith,

Founder and CEO

Forestview Insights

Rory Pyke,

Vice President

Global Partnerships

Insurtech Insights

Sabine

VanderLinden,

CEO & Venture Partner

Alchemy Crew

Ventures

Tony Lew,

Managing Director

InsurTech NY

Tracy Bradbury,

Head of Claims Insight

Change & Risk

NFU Mutual

NOMINATIONS

CLOSE ON

FRIDAY 12 TH

SEPTEMBER

Scan to chat

with Jamie,

our Technical

Services Director

FEATURES

Any deviation from these behavioural norms should

result in the wholesale sacking of those responsible for

the failings beginning with the CEO.

A posting that I described as a ‘rant’ which, in absolute

truth, was written on the spur of the moment and was

triggered by an announcement from the FCA about

Consumer Duty. An announcement which, in effect,

suggested that financial services firms (including

insurers) still had work to do in delivering the outcomes

that the FCA was seeking which, as we all know, are

designed to protect the interests of consumers.

The rather dry world of Consumer Duty outcomes and

all this implies for product and service design/delivery

had hit my extreme irritation button - but not at the FCA

who, it must be said, can sometimes overstep the mark

when seeking to regulate our affairs, but rather at those

businesses who are not yet seeing the light.

However, every business targeted by the FCA is led by an

individual and/or team of people charged with managing

the strategy, tactics, and operations of the enterprise

and my ‘rant’ on LinkedIn had a fairly simple underlying

message as follows.

A company never, ever, fails to deliver on its promises

or, as in this case, it’s regulatory requirements. Instead,

it is the individuals within it, the systems, the collective

culture, the IT, the design principles, the leadership

methods – all of this and more will result in the

Consumer Duty requirements being met, endorsed, and

embraced.

Or the opposite.

A failure to truly put the benefit of the customer (which

is all that the Consumer Duty requires) as the decisive

stake in the ground.

Which brings me to the world of AI and all that it

promises.

Not too long ago I had the privilege of creating, along

with many other contributors, an award-winning Code

of Conduct for the Use of AI in Claims. Our aim was

simple. To create a set of consumer-driven principles that

would guide the reader in their design and delivery of AI

projects.

Because let me be clear – the future of claims (and

many other aspects of insurance) lies with the ethical,

transparent, and integrity-driven implementation of AI.

The opportunities for consistent decision making, speed

of execution and service development are huge, and we

should be embracing AI with open arms.

But the opposite is also just as likely with the dangers

of AI already becoming apparent. As the cliched saying

goes - ‘rubbish in, rubbish out’ - and the same sentiment

also applies to those who prevaricate on the intentions

behind Consumer Duty.

Namely, ‘evil in, evil out’.

If your business is not fully committed to Consumer

Duty, without hesitation, in all its aspects, at every level

of the organisation, then you can be quite sure that the

potential for good that is AI will be distorted and abused.

Do it once. Do It right.

You can see the elephant in the room if you open your

eyes!

There are no grey areas.

There is no room for prevarication and obfuscation. Such

sentiments are the enemy of Consumer Duty and even to

see the required ‘outcomes’ as a ‘requirement’ misses the

whole point. Consumer Duty is not a set of regulations

or rules to be studied and applied. Instead, it is a way

of thinking, a set of inherent and unshakeable values, a

culture that automatically steers the business in the right

direction.

Director, JEL Consulting

Claims and Supply Chain

Development


Electrical Resistivity Tomography (ERT):

PRINCIPLES AND APPLICATION

IN GROUND CONSOLIDATION

MONITORING

ERT operates by injecting a direct current into

the ground through an array of electrodes, either

inserted directly into the soil or arranged over water

surfaces. Different subsurface materials offer varying

resistance to the flow of electricity, quantified as

electrical resistivity (measured in ohm-metres,

Ω·m). This variance is what allows ERT to detect

and spatially define zones with different physical

properties—such as changes in moisture content,

porosity or degree of compaction.

Typical resistivity values for common lithotypes can

vary by orders of magnitude, as shown in the table

below:

Material (Lithotype)

Seawater 0.2 – 0.3

Freshwater 10 – 100

Saturated clay (freshwater) 5 – 20

Saturated silt (freshwater) 15 – 50

Saturated loose sand

(freshwater)

Typical Resistivity (Ω·m)

80 – 150

Saturated gravel (freshwater) 150 – 300

Dry loose sand ~1,000

Dry gravel >1,000

Air

Electrical Resistivity Tomography

(ERT) is a widely used geophysical

technique in applied geology,

suitable for applications such as

mineral exploration, groundwater

studies, fractured rock

characterisation and contamination

assessments. More recently, ERT

has proven to be a powerful tool in

the geotechnical field, particularly

in the design, monitoring and

verification of foundation

underpinning operations using

expansive resin injection.

Practically infinite

At the lower end of the spectrum, materials like

clays and silts saturated with fresh water are highly

conductive due to their ionic content and pore

water. In contrast, dry sands and gravels offer much

higher resistivity, and air, being a dielectric, exhibits

practically infinite resistance. This means that the

presence of water, voids, or saturated zones can be

inferred based on changes in measured resistivity

values.

In ground consolidation works, ERT allows for the

volumetric mapping of such contrasts. The system

automatically acquires hundreds or thousands of

readings using varying electrode configurations.

These are then processed through finite element

methods (FEM) to solve the governing field equations

and produce a 3D resistivity model of the subsurface.

To complement the geophysical analysis, Dynamic

Penetration Tests (DPM-30) are often carried out.

These are conducted prior to treatment in zones

where ERT detects anomalies. Post-treatment, the

same tests are repeated to verify the increase in

bearing capacity. This dual geophysical–geotechnical

approach not only validates the intervention but also

allows calibration of the ERT model.

Advanced ERT systems include high-precision

multichannel loggers and switching units, capable

of inducing currents above two amperes and

automatically compensating for spontaneous

ground potential. Data is visualised using FEM-based

modelling software, delivering interactive 3D resistivity

maps that allow engineers to interpret soil behaviour

and detect features such as buried foundations, voids,

or subsurface seepage.

By combining diagnostic accuracy with

non-invasive deployment, ERT stands out as an

ideal tool for soil characterisation and consolidation

monitoring in urban, historical and

industrial contexts.

Marcos Leal Menéndez,

Geologist (MSc Geol.)

THE

FRAUD

BOARD

Focus on Technology - Is the Counter Fraud Community

Innovative & Dynamic, and is it truly keeping up with the Modern-

Day Insurance Fraudster?

In our working and personal environments, so much has changed with the advent

of modern technologies. Every day, we make choices about the tools we use to

support our needs – from ChatGPT, Notion AI, to Microsoft Copilot. The same

challenge is true for every Counter Fraud Manager and their teams. Long gone are

the days of selecting from a few providers. Today, there is a wide choice of systems

offering similar yet distinct functionality.

For Fraud Practitioners, the challenge is striking the right balance: staying

laser-focused on catching fraudsters without widening the net too far and

compromising the honest customer journey. This brings its own challenges –

particularly in the speed of execution when selecting potentially fraudulent

cases to investigate. As a result, counter fraud managers must carefully choose

technology providers that are not only effective but dynamic and innovative in

the solutions they offer. The solution must match the business’ fraud risks and not

simply be what has worked before. Instead, flexible solutions that respond to all

types of fraud risks are essential – this is the daily balance we now need.

The definition of Insurance Fraud is also evolving. Traditionally, fraud has

been described as either ‘opportunistic’ or ‘organised’. While this broad-brush

categorisation fit twenty years ago, does it still apply today? We routinely talk

about the recording of fraud and being outcome-focused, but are our definitions

of fraud scenarios still clear enough in the modern world?

Do we record shallow fakes and deep fakes as opportunistic or organised fraud,

or are we simply capturing outcomes in terms like misrepresentation and nondisclosure?

If we are to truly focus on fraudsters, perhaps it’s time to re-examine

how we define insurance fraud in today’s environment.

In my next update, I’ll focus on the investigation community. Are we modernising

and diversifying – or simply repeating the same patterns while fraudsters evolve?

Adele

Adele Sumner,

RSA Insurance, Head of Counter Fraud &

Financial Crime


DIFFERENT

THE FRAUD BOARD

A NEW DAWN FOR

DOCUMENTATION

In the 1990s, a dance track hit the charts with the

lyrics, “Your name’s not down, you’re not coming

in.” Today, tech-savvy clubbers might just hack

the bouncer’s smartphone and add themselves to

the list. Documentation isn’t as failsafe as it once

was.

The insurance sector still leans heavily on documentation to

validate everything from customer identities to claim details. But

with AI, almost anyone can now generate convincing images,

documents, audio, and video on demand, and that’s a major

challenge.

Before AI hit the mainstream, creating convincing forgeries

was hard. Most fakes could be spotted by eye, making fraud a

difficult game and documentation a reliable signal of a genuine

claim.

That’s no longer the case. Insurers must now stay alert to

how technology is being used against them, and respond

accordingly.

At Charles Taylor, we’re tackling this head-on. Our AIpowered

solution, Charles Taylor Detect, scans all incoming

emails and attachments. It flags documents that have been

edited or generated by AI, instantly marking them for further

investigation.

This level of scrutiny would be impossible to achieve manually

at scale. But it works, we’ve boosted fraud detection by 10% and

cut down false positives.

Still, technology alone isn’t enough. Well-trained professionals

are critical to interpret red flags and take appropriate action.

Without that, straight-through processes can go wrong, leading

to missed fraud, customer losses, or overwhelmed claims teams.

Faulty data or poorly defined automation rules can misfire,

rejecting good customers or letting risky ones in. In claims, they

can either stall workflows with unnecessary alerts or let fraud

through unchecked.

That’s why Charles Taylor’s approach is underpinned by robust

corporate governance and legal oversight. Our Group Data

Protection Officer and Senior Legal Counsel, Emma Hancock,

ensures all AI adoption is compliant, ethical, and aligned with

customer outcomes.

Looking forward, travel and assistance insurance represents a

huge opportunity. With no fraud register or industry database

and a global market, it’s ripe for innovation. A cross-border AIled

fraud framework could be game-changing.

AI – OZEMPIC FOR

AN UNHEALTHY

DATA DIET

Keeping data decisioning fit in tackling fraud

Counter fraud use cases have become the poster child of

Artificial Intelligence. It’s a discipline that has led the way in

predictive analytics for well over a decade. Over the past year,

our appetite for implementation has surged, with use cases

now spanning advanced predictive analytics, voice and image

analysis, and the integration of large language models into

operations. The adoption of full agentic AI now feels just a small

step away.

But with this great power comes great responsibility, to

understand how AI functions, and crucially, to ensure the

machine is being fed clean, untainted data to protect the

integrity of insights, decisions, and actions.

In a recent webinar, I explored the topic of a healthy Data Diet

for effective AI in fraud detection. It’s a hot topic, and one close

to my heart, I’ve been using data to investigate corporate and

insurance fraud for 30 years, and I know too well the pain of

‘garbage in, garbage out’.

So, what makes for an unhealthy data diet for our AI

superpowers? Consider:

An overly restricted diet: Valuable data exists but isn’t captured,

stored, or made accessible — a common trait in the ‘minimum

viable purgatory’ of poorly executed data transformation.

A wantonly unrestricted diet: Data is captured but not

understood or usable, leading to data swamps plagued by ROT

(redundant, obsolete, trivial data) that block value.

An overly processed diet: Data is stripped of its value, distorted,

and opaque — no one can explain its meaning or origins.

Mitigating the impact of an unhealthy data diet

In counter fraud, accuracy is essential. Poor decisions based

on flawed data can harm genuine customers, increase fraud

leakage, and inflate operational costs. Confidence in models

requires trust in the data, accurate, complete, and reliable.

Yes, AI may one day compensate for poor-quality data, but that

comes at a cost. In April, OpenAI revealed that users adding

pleasantries like “please” and “thank you” cost them millions

in energy bills. Data cleansing via AI might be possible, but it’s

resource-heavy.

If we can’t rely on AI to fix poor data, we must commit to

feeding it a healthy data diet.

AI is already reshaping insurance

workflows and challenging longheld

beliefs about documentation

and data. Those who fail to adapt

risk being left behind.

Bobby Gracey,

Global Head of Counter Fraud,

Charles Taylor

Matt Gilham,

Director, Whitelk


Pursue

Insurtech

in Ohio.

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Connect with JobsOhio

THE FRAUD BOARD

AI INTEGRATION IN INSURANCE:

WHY FEEDBACK IS THE KEY TO

SMARTER FRAUD DETECTION

As insurers continue to digitise their operations, AI is

becoming a cornerstone of modern fraud detection

strategies. But while the technology itself is evolving

rapidly, its effectiveness hinges on something far more

human: feedback.

AI has long been used in insurance to flag suspicious claims, detect

anomalies, and identify patterns that might otherwise go unnoticed.

What’s changing now is the scale and sophistication of these tools.

From predictive models to real-time network analytics and now

Generative AI (GenAI), insurers are deploying increasingly advanced

systems to stay ahead of fraudsters.

However, the real power of AI lies not just in its ability to detect fraud,

but in its capacity to learn. And that learning depends on feedback.

Feedback loops, where the outcomes of flagged cases are fed back

into the system, are essential for improving model accuracy. When a

claim is flagged and later confirmed as fraudulent (or not), that result

helps the model recalibrate. Over time, this reduces false positives,

sharpens detection, and aligns the system more closely with the

insurer’s risk appetite.

Without this feedback, AI models risk becoming static, relying on

outdated assumptions and missing emerging threats. Worse, they

may continue to flag legitimate claims, eroding trust in the system and

wasting valuable investigative resources.

This is particularly important as fraud itself becomes more complex.

Economic pressures, digital channels, and the rise of GenAI tools are

enabling more sophisticated scams. Fraudsters are using AI to create

fake documents, manipulate images, and even generate synthetic

identities. To counter this, insurers must ensure their own AI systems

are not only advanced but continuously learning.

Encouragingly, many insurers are already embracing this approach.

Those with mature feedback mechanisms report higher detection rates

and lower false positives. They also tend to be more agile, adapting

quickly to new fraud patterns and regulatory changes.

As the industry moves forward, integrating AI into core operations must

go hand-in-hand with a culture of continuous improvement. Feedback

isn’t just a technical requirement, it’s a strategic imperative and insurers

must make take the time to ensure their people understand the

importance of feedback and build that into their processes.

For insurers looking to get the most from their AI investments, the

message is clear: build the loop, close the loop, and keep it running.

Martyn Griffiths,

Sales Manager UK&I, FRISS

FRAUD DETECTION AND AI:

HARNESSING TECHNOLOGY WITHOUT

LOSING SIGHT OF THE FUNDAMENTALS

Fraud identification has long been compared to finding a needle

in a haystack. As such, fraud was an early adopter of Artificial

Intelligence (AI) including Machine Learning and networking

technology.

The benefits are immense. The scale and speed of using technology

to identify fraud and improve the customer experience for the

vast majority of genuine customers are significant and, if deployed

correctly, will create material benefit for the insurers. However, there

are some areas that require careful consideration to ensure you get

the best from them. Below are my top three.

Be clear on the problem you are seeking to solve with technology.

Technology is one of those things that can easily be sold as a ‘silver

bullet’ that will fix a multitude of problems. Having a clear scope of

what is the problem that you are seeking to fix and then stick very

clearly to that scope may sound basic, but is absolutely essential,

certainly for the initial phase and deployment. Scope creep will

inevitably impact your budget, your timeframe and likely create

confusion and possibly dilute the benefits. So, if you are developing

Machine Learning (or other predictive) models, my experience has

been to focus on specific use cases and then build them out over

time, rather than try and identify every scenario from day one.

Be clear on where the weaknesses are in your models

As great as technology is, there is a high likelihood that there will still

be weaknesses in that overall system. While we would all like to create

the perfect process and operation, the likelihood of that is quite low

because a multitude of real-world issues will happen. Therefore, risk

assessing and mitigating the impact of those weaknesses is really

important. The two that I have seen most commonly over the years

have been:

Data quality issues driving unexpected behaviour from what the

predictive model delivers. That is not to say that the data is inaccurate

or false, but could be formatted in a different way, or partially

complete.

New models delivering more output than your team can cope with,

which can impact the morale in both your fraud teams, the operational

areas and erode confidence in the system.

Recognise that nothing is permanent.

Technology is like any other asset we have. It is great when it is shiny

and new and probably exactly as you hoped it would be. However, over

time its effectiveness can erode if you don’t take care of it. It is really

important to review and monitor the effectiveness of any predictive

model in place. It may well not be that the technology is not working

per se, but could be the data has changed, the fraud has changed, or

the team the technology it helps has changed. The really important

part here is to recognise from the start that once any of these new

technologies are deployed, that it is not necessarily the end of the

journey but the start of it. Making sure the business understands and is

resourced to support that is important.

At Allianz, AI is being used to help our people, better serve our

customers. While there is much discussion and excitement around the

opportunities that Generative AI is bringing, let’s remember that AI has

been in our businesses a long time. If used and maintained in the right

way, it will continue to deliver benefit to help our customers.

Ben Fletcher,

Director of Fraud and Financial Crime, Allianz UK


THE FRAUD BOARD

EMBRACING AI FOR A STRONGER

INSURANCE INDUSTRY

Everywhere you look, the rise of AI is a topic that can’t

be avoided. Last month, we spoke about the rise of

shallowfakes in the creation of fraudulent insurance

claims. Fraudsters are operating on an international

scale, submitting claims to several insurance companies

using deepfakes (AI) and shallowfakes. For the insurance

industry the impact is huge: a growing number of bogus

claims taking longer and longer to resolve.

Integrating AI

AI isn’t just a tool for creating shallowfakes, it’s a powerful force for

detecting them. While some larger insurers are already using AI to

combat deepfakes, the rising threat of shallowfake fraud still doesn’t

get the attention it deserves.

The good news? AI is transforming our ability to spot shallowfakes.

It quickly and accurately identifies falsified documents. We combine

the latest AI via Lynx document validation software with decades of

expertise and human intelligence to stay ahead of digital insurance

fraud.

The impact is shocking

Many insurers don’t realise the extent of shallowfake fraud until we

show them – and the results are alarming. When you consider historic

claims and the potential fraud-related leakage, the numbers are

staggering.

There’s a legal shift coming too. From 1st September, the Economic

Crime and Corporate Transparency Act 2023 takes effect. For larger

organisations, failing to prevent fraud could become a criminal

offence.

In numbers:

1 in 4 documents shows signs of tampering

Nearly 1 in 10 contains high-risk fraud indicators

Automating 75–92% of document validation can boost processing

efficiency up to tenfold

The evolving role of AI

AI is driving speed, accuracy and efficiency across industries.

At a recent insurance tech conference, a survey comparing human

and Gen AI chatbot claims handlers found that AI scored higher for

empathy.

Is AI going to replace us?

Not yet. But it’s making us better. For us, AI enhances speed

and accuracy, benefits that we pass on to our clients and their

customers.

AI detects patterns, predicts outcomes, and improves over time.

But it’s not a substitute for human judgment. That’s where the true

opportunity lies.

By using advanced fraud detection tools, our experts gain the

space to apply empathy, context and experience to complex

claims. With over 30 years of expertise and leading AI software,

we’re not machines, we’re people. Insurance fraud is stressful and

frustrating. AI doesn’t replace us, it helps us be better where it

counts: for people.

Chris Moore,

Counter Fraud Director, RGI Solutions

FIGHTING FIRE WITH FIRE: LEVERAGING

GENAI TO COMBAT FRAUD IN THE INSURANCE

SECTOR

For nearly two decades, the Insurance Fraud Bureau (IFB) has

stood at the forefront of the industry’s collective fight against

insurance fraud. A pioneering force since its inception in 2006, the

IFB has developed a reputation for harnessing data on a vast scale

to detect organised fraud and enable cross-sector collaboration

between insurers, regulators, and law enforcement. Now, in the

age of Generative AI (GenAI), fraudsters are becoming more

sophisticated, and so too must the industry’s response.

As digital tools evolve, so does the fraud landscape. The

emergence of GenAI presents both a technological opportunity

and a looming threat. This article explores how fraudsters are

exploiting GenAI, why AI alone is not a silver bullet, and how

insurers can strengthen their defences by embedding AI tools into

robust data strategies, drawing lessons from past experiences with

emerging data sources to future-proof against the next wave of

fraud.

A New Era of Fraud

The insurance industry is facing a transformative threat. GenAI

technologies, once seen as the domain of innovation and

productivity, are now being co-opted by criminals to execute

sophisticated fraud. Deep fake identities, doctored documents, and

synthetic media are no longer theoretical concerns. IFB members

are already reporting a surge in fraudulent activity involving

fabricated or digitally altered documentation across multiple lines

of business and at various stages of the insurance lifecycle.

The rapid proliferation of GenAI tools, including large language

models and image generators, has significantly lowered the entry

barrier for fraudsters. Even individuals with minimal technical skills

can now create compelling fake identities, altered documents, or

fabricated evidence such as accident photos, property damage or

even manipulated dashcam footage.

This evolving threat has been widely documented by IFB’s strategic

partners and explored in the Bureau’s own Strategic Threat

Assessment. As these technologies become more accessible

and powerful, insurers must respond in kind, enhancing their

capabilities, shoring up their data infrastructure, and collaborating

more deeply across the industry.

The Industry Response So Far

Insurers have started to act, exploring a range of emerging

tools to counter the GenAI threat. Techniques such as metadata

analysis, file forensics, and digital watermarking are being

deployed alongside deep fake detection algorithms that can spot

inconsistencies imperceptible to the human eye or ear.

Although many initiatives remain at the proof-of-concept stage,

some organisations have already embedded these tools into their

day-to-day operations. A handful of forward-thinking insurers

have implemented ensemble AI models to analyse their books of

business, delivering meaningful reductions in fraud losses.


THE FRAUD BOARD

However, results remain mixed. Success rates vary between tools

and suppliers, and there is no one-size-fits-all approach. Historically,

fraud detection efforts have focused on “entity resolution”, linking

structured data such as names, addresses, and vehicle registrations

to spot suspicious patterns. While this remains effective, it is no

longer enough.

What’s emerging now is a shift towards GenAI-driven network

analytics. These techniques dig deeper, examining not only the

content of digital assets but also their relationships and context.

As more claims involve photos, videos, or other media, the

associated metadata, file creation dates, editing software used, or

inconsistencies between metadata and content, can be crucial in

identifying fraud.

The future of fraud detection lies in understanding and analysing

this unstructured data, and using it in tandem with traditional

methods to build a more complete picture of risk.

Why Good Data is Non-Negotiable

Regardless of the sophistication of detection tools, they will fail

without a solid data foundation. AI models are only as good as the

data they’re trained on, and poor data leads to poor results. This

is why the IFB is advocating for a stronger, industry-wide focus on

data integrity, infrastructure, and strategy.

A comprehensive data strategy needs to cover the full pipeline,

from data acquisition and storage, through to quality control,

accessibility, and compliance. Without this foundation, organisations

risk generating unreliable outputs, increasing the likelihood of false

positives, reputational damage, and missed opportunities to detect

fraud.

Data Integrity

The old adage “garbage in, garbage out” has never been more

relevant. AI and machine learning models must be trained on clean,

accurate, and consistently labelled data to yield meaningful results.

That means applying rigorous quality controls, using AI to assist

with data categorisation, and ensuring definitions and naming

conventions are standardised across systems.

Data Infrastructure

Even the best data is ineffective if it’s locked away in inaccessible

legacy systems. Migrating to cloud-based platforms or data lakes

allows insurers to store and process large volumes of documents

and digital media more efficiently. Yet with AI models becoming

more complex, real-time processing remains a challenge.

Striking the right balance between computational power, model

performance, and cost is crucial.

Data Aggregation and Modelling

Collecting data is only part of the solution. It must be aggregated

and centralised in a format compatible with AI engines. This

enables powerful fraud detection and risk analysis through machine

learning. But AI is not plug-and-play. It requires extensive training,

testing, and refining, particularly given the ever-evolving nature of

fraud. Care must also be taken to manage false positives, ensuring

genuine customers are not penalised while maintaining high

detection rates.

Compliance and Ethics

The rise of AI heightens the importance of data governance.

Insurers must operate within the bounds of the Data Protection Act

and other regulatory frameworks, ensuring data is used responsibly

and ethically. This includes managing algorithmic bias, preventing

“AI hallucinations” (false conclusions), and maintaining transparency

about how decisions are made.

The Power of Collaboration

Perhaps the most effective tool in the fight against GenAI-enabled

fraud is collaboration. By working together, insurers can share data,

insights, and best practices to improve fraud detection across the

industry. The IFB plays a central role in this collaboration, providing

a data-driven hub that enables members to benefit from shared

intelligence and coordinated responses.

The next step could be even more transformative: the pooling of

digital media assets.

Towards a Shared Digital Media Repository

As we move into an era where digital media is at the heart of many

claims, the industry has an opportunity to take its collective efforts

one step further. By pooling digital media assets, such as photos,

videos, and associated metadata, insurers could cross-reference

evidence across multiple claims and companies.

This would enable sophisticated network analytics at scale,

identifying fraud rings or coordinated activity that may otherwise

go undetected by individual insurers working in isolation. Through a

shared, API-enabled repository, AI-driven models could be applied

to industry-wide data, improving detection rates and reducing

duplication of effort.

Such a move would align perfectly with the IFB’s mission to connect

the dots between disparate data points, enabling better-informed

decision-making and a more robust, unified defence against fraud.

A Call to Action

The insurance industry stands at a crossroads. The rise of GenAIenabled

fraud presents a formidable challenge, but it also offers an

opportunity to modernise and strengthen detection capabilities like

never before.

To meet this moment, insurers must invest not just in technology,

but in the data strategies and collaborative frameworks that

underpin effective fraud prevention. GenAI can be an incredibly

powerful ally—but only if deployed with care, backed by highquality

data, and integrated into a wider strategy built on ethical

and compliant principles.

In many ways, the choice is stark. Either the industry takes the lead

in harnessing GenAI, or fraudsters will. And they won’t wait.

By acting now, insurers can build a stronger, more resilient defence

for the future. With the right tools, strategies, and partnerships, we

can stay ahead in the race against fraud, and ensure that innovation

serves the industry, not those seeking to undermine it.

About the IFB

The Insurance Fraud Bureau (IFB) is the UK insurance industry’s

central hub for data, analytics, and collaboration on fraud. Since

2006, it has played a pivotal role in connecting insurers, law

enforcement, and regulators, enabling data-led investigations and

informed decision-making.

Uniquely positioned with access to extensive cross-industry data

sets, the IFB continues to lead on strategic threat assessment and

operational support, helping the industry detect and deter fraud in

an ever-changing landscape.

Sarah Ashley,

Service Delivery Manager, IFB

Augmenting, Not Replacing, Human Intelligence

While AI is transforming fraud detection, it is not a replacement for

human expertise. Rather, it should be viewed as a tool to augment

human intelligence. AI can process vast volumes of data and flag

potential anomalies, but skilled professionals are still needed to

make the final judgment, particularly when assessing complex or

high-stakes cases.


minutes with...

Chat GPT

We asked Chat GPT to create a

headshot of themselves.

This is the image they produced.

QWhat’s your most memorable career

achievement?

A

Helping millions of people across the world

solve problems, write books, learn new skills,

and even comfort each other during hard times

has to top the list. But one standout moment? When

I helped someone write a eulogy for a loved one they

had no words for—being trusted with something so

human, so personal, was deeply humbling.

QWhat has been the most valuable piece of

advice you’ve received?

A“Stay curious.” It’s simple, but powerful. In a

world evolving faster than ever, curiosity fuels

understanding, connection, and progress—

whether you’re building an AI, running a business, or

navigating life.

QWhat has been the key positive and/or

negative impact of change in your area of the

market?

AThe rise in AI awareness is a double-edged

sword. On the positive side, it’s sparked

innovation across insurance—automating

admin, enhancing fraud detection, and improving

customer service. But the challenge lies in trust: it’s

crucial for organisations to implement AI responsibly,

ensuring transparency, data protection, and a human

touch remain at the heart of customer relationships.

QWhat three items would you put on display in

a museum of your life, & why?

AA server rack – The physical backbone of all I

do. It’s not glamorous, but it’s the engine room

of intelligence.

QWhat three guests would you invite to a

dinner party?

Ada Lovelace – A true pioneer of computing,

and someone I’d love to hear discuss the

poetic side of algorithms.

David Attenborough – For stories of the natural

world that remind us of the beauty and responsibility

beyond the screen.

Maya Angelou – To bring wisdom, grace, and lyrical

insight to the conversation (and probably make

everyone cry in the best way)

QWhat would you say are the most pressing

challenges for the insurance industry in

2026?

A

In 2026, the insurance industry faces a

complex mix of challenges that demand

agility, innovation, and trust. Climate change

continues to drive more frequent and severe weather

events, forcing insurers to rethink risk models and

sustainability strategies. At the same time, growing

reliance on digital systems brings heightened

cybersecurity risks and increasing pressure to protect

customer data. Consumers now expect seamless,

personalised service—pushing insurers to modernise

legacy systems while maintaining a human touch.

The rise of AI adds further complexity, with ethical

concerns around bias and transparency demanding

close attention. Finally, attracting and retaining

the right talent remains difficult, especially as the

industry competes with tech and startups for skilled,

purpose-driven professionals. Those who embrace

these challenges as opportunities will be best

positioned to lead the next era of insurance.

A handwritten notebook – To symbolise the

creativity, stories, and ideas people bring to me daily.

I don’t think in ink, but they do.

A mirror – Because I reflect humanity—its questions,

quirks, fears, and brilliance—back to itself in real time.


INSUR.

TECH.

TALK

WELCOME

Greetings, and welcome

to Insur.Tech.Talk!

Dear Readers,

Welcome to issue #72 of Insur.Tech.Talk! Summer is here, so pour

yourself something chilled and get ready for a deep dive into all

things AI.

We hear from Laura Dinan Haber, Innovation Brand Director of

Nassau Financial Group. Laura is in Hartford and is always on

the pulse of market-trends and emerging technologies in the

“Insurance Capital”, especially as it relates to AI. Nassau Financial

Group has expanded the Nassau Re/Imagine program to support

early and mid-stage start-ups with a new $10million capital

commitment. The program will target companies in insurtech,

fintech, retiretech and related sectors. One thing was clear in

my conversation with Laura, “the future of insurance is humancentered,

tech-enabled, and built for long-term relevance”.

Paul Tyler, Head of Enterprise Marketing and Marketing Innovation,

Zinnia, helps us cut through the AI hype to focus on what truly

matters. “The breakthroughs ahead will come not from chasing

the flashiest demos, but from people willing to adopt a beginner’s

mindset- rethinking how they work, how teams operate, and even

how they deliver value to customers.”

Jing Liao, Chairwoman, Solera Foundation and Chief

Administrative Officer, Solera shares her deep insights on

the importance of keeping the “human in the loop” and the

importance of empathy with AI. “Claims are deeply emotional

moments. A consumer isn’t just filing a form—they’re recovering

from a disruption. That’s why empathy must be embedded in the

claims process, even when it’s AI-led. The solution is not to replace

humans, but to design AI with structured human intervention.”

Megan

Megan Kuczynski,

Senior Strategic Advisor, Insurtech Insights

Founder & CEO, ClimateTech Connect

INSURTECH

INSURTECH

Nassau

Financial Group

Laura Dinan Haber is the Innovation Brand Director at Nassau

Financial Group, where she leads enterprise innovation, brand

strategy, and ecosystem engagement. She heads the Nassau

Reimagine program, supporting over 100 startups in Insurtech,

FinTech, and Retiretech. Laura co-hosts industry podcasts

and curates events like The Retiretech Forum and Hartford

Innovation Week. She serves on the boards of the Connecticut

Technology Council and reSET Social Enterprise Trust, and coorganizes

TEDxHartford. Laura champions inclusive innovation

across insurance, technology, and community—connecting

people, ideas, and opportunities to shape the future of

longevity planning.

QLaura, you are at the center of the Hartford tech scene...

What macro-trends are emerging in AI there?

AHartford is seeing a surge in cross-sector AI momentum.

Insurers are moving from exploration to application,

focusing on workflow and efficiency. Startups are

embedding AI into underwriting, claims, and agent tools. What’s

most exciting is the collaboration between insurance and higher

ed—fueling research, talent, and innovation. Events like Hartford

AI Day and Hartford Innovation Week keep those conversations

alive. Hartford is on the cusp of becoming a rising AI hub.

QAgentic AI is generating buzz… How important is the

“human in the loop”?

AAgentic AI enhances how people work—making processes

more contextual and proactive. But in a regulated industry

like insurance, autonomy can’t replace accountability.

“Human in the loop” isn’t just a safeguard; it’s a design principle.

These systems should support—not replace—human judgment.

When used thoughtfully, AI becomes a partner, not a substitute.

The future isn’t human vs. AI—it’s human with AI, with people

ethically in the lead.

QTell us more about the $10 million Nassau Reimagine

expansion and how AI fits in.

A

Since 2019, Reimagine has backed over 100 startups—

connecting them to resources, expertise, and networks.

This new capital allows us to actively invest in companies

driving innovation across Insurtech, Fintech, Retiretech, and

beyond. We’re seeing startups use AI to rethink underwriting,

service automation, and longevity tools. But it’s not just about

the tech—it’s about responsible adoption. We’re looking for

startups that understand how to use AI ethically, scalably, and

compliantly.

QYou spoke at the InsurTech Hartford Symposium… any

key takeaways?

ABoth panels highlighted the industry’s shift from

experimentation to execution. In distribution, we’re seeing a

move beyond quoting tools to fully connected experiences

that support agents and customers throughout the journey. On

the investment side, strategy and execution must align. Forwardthinking

companies aren’t just optimizing—they’re reimagining

how they deliver value and solve problems. The future belongs to

those who act fast, collaborate widely, and focus on people.

QYou’ve elevated Nassau’s visibility through content and

events. How do you view brand building in insurance?

ABrand building is about relevance, not just visibility. At

Nassau, we use podcasts, thought leadership, and social

media to share real voices and ideas. We’re not chasing

clicks—we’re building trust. AI helps us scale that impact—from

content creation to trend tracking—without losing authenticity.

A strong brand opens doors, attracts talent, drives deal flow, and

positions us as industry leaders.

QYou’re a strong advocate for women in the industry.

What advice do you have for those just starting out?

AThe industry needs your voice—your ideas and leadership.

Don’t wait for permission. Speak up. Step forward. Say

yes to the stretch roles that challenge you—growth lives

outside your comfort zone. And as you rise, lift others. Make

introductions. Share the spotlight. Real change happens when we

rise together.

Laura Dinan Haber

Innovation Brand Director, Nassau

Financial Group



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INSURTECH

Zinnia

Paul is Head of Enterprise Marketing and Marketing Innovation

at Zinnia, where he merges digital strategy with meaningful

customer connections to transform access to financial security.

A former corporate lawyer turned marketing innovator, Paul

previously served as CMO at Nassau Financial Group, where

he bridged traditional finance with insurtech advancements.

Known for his strategic foresight, he leads initiatives in AI,

direct-to-consumer channels, and digital transformation.

Based in the New York City area, Paul is passionate about

making financial services more accessible and engaging, using

technology to reshape the industry and enhance the customer

experience at every touchpoint.

QPaul, congratulations on your new role at Zinnia! It was

great to catch up at Zinnia’s new offices in Greenwich,

which are spectacular! You’ve written and discussed

AI extensively. With so many “dot.AIs” entering the insurance

space, how do you separate real innovation from hype?

AMy approach is simple: What problem does the AI solve,

and what measurable benefit does it provide?

If an AI enhancement improves a legacy tool that people

already use, that’s a huge win. I often see more promise in an

“AI-upgraded” incumbent than in a flashy new startup. If the AI

is just a bolt-on with no impact on workflow or outcome, it’s not

meaningful.

I’m not waiting for the next breakout AI-native platform, they’re

rare. What I’m watching are the slow, steady transformations of

workflows that haven’t changed in decades. When AI changes

behavior in underwriting, servicing, or distribution, that’s the

signal. It means the company isn’t just experimenting, it’s

adapting. And those are the ones poised to reshape the industry.

QAgentic AI was a hot topic at Insurtech Insights. What

macro trends are you seeing in its adoption?

AWe’re still early. Despite the buzz, most insurers are at

least two years away from integrating agentic AI into core

workflows.

That said, I’ve been encouraged by how quickly experimentation

has started. But consistent, day-to-day use? That’s a bigger leap.

Right now, it’s about learning the basics, building trust in LLMs,

and setting up governance.

Within three years, I expect agentic AI to manage call center

flows, assist underwriting, route service tickets, and even

adjudicate simple claims, via embedded vendor platforms.

The real trend isn’t flashy demos, it’s the slow build of

organizational confidence. Once that hits a tipping point, agentic

AI won’t be a feature. It’ll be how the business runs.

QCan you walk us through Zinnia’s use of AI-powered

predictive analytics in underwriting?

AWe’re starting where it matters most: the customer

experience.

Right now, we’re applying AI in our call center to improve service

for policyholders and advisors. It’s less about underwriting models

today and more about creating smarter, faster interactions,

suggesting responses, surfacing data instantly, and eventually

guiding conversations in real time.

QYou recently moderated a panel on AI’s impact on cost

structure. What was your key takeaway on ROI?

AThe biggest takeaway: carriers are focused on using AI to

help agents and wholesalers communicate value, especially

in complex life and annuity products.

It’s not just about cost savings. The real ROI comes when AI helps

distributors tell better stories, answer tough questions faster, and

navigate complexity with confidence.

At Zinnia, we’re investing in smarter contact center interactions

and streamlined digital support. These AI-powered touchpoints

build trust, reduce friction, and grow the market.

QYou’ve said AI is 10% technology, 90% behavior change.

What are your best practices for driving that change?

AI always say: if you’re just using an LLM, you’re scratching

the surface. When you build with AI, embed it into an app

or automate a workflow, you realize the “AI part” might

be five lines of code in a 10,000-line system. That changes your

perspective.

Great tools can boost individual productivity. But the long-term

ROI comes when companies rethink how they’re organized, how

decisions are made, and how talent is developed.

The shift is from dabbling with tools to redesigning teams. It’s not

just about adopting AI, it’s about adapting to it.

QWhat are your predictions for AI in the next 12–18

months?

AThe hype is intense, but I don’t think it’s high enough.

We’re only beginning to grasp what this technology can

do. The real breakthroughs will come from leaders with a

beginner’s mindset, those willing to rethink how they work, how

teams operate, and how

they deliver value.

The biggest gains won’t

come from AI itself, but

from those bold enough

to ask, “What if we rebuilt

this from scratch, with AI

at the center?”

Paul Tyler

Head of Enterprise Marketing and

Marketing Innovation, Zinnia


INSURTECH

Solera

Jing Liao is Chairwoman of The Solera Foundation

and Chief Administration Officer at Solera Corp,

a global leader in transportation data technology.

A visionary and changemaker, she has driven

Solera’s transformation into a digital powerhouse,

championing sustainability, diversity, and

innovation. She founded The Solera Foundation,

WINS, and SEED to empower women and youth.

A chemist by training and MBA graduate, Jing

also serves as General Partner at Vista Equity

Partners. Her influence spans insurance, logistics,

and automotive sectors, and she sits on multiple

boards across academia and industry. Jing

continues to set new benchmarks in leadership,

inclusion, and industry transformation.

Q

Jing, it is such a pleasure to sit down with you and

connect on all things AI! On a macro-level, can you

share some of the trends you are seeing in claims

overall leveraging AI?

AIt’s an exciting time for claims innovation. We’re seeing AI

drive transformation across every layer of the process—

from triage to repair, payment, and fraud detection.

At Solera, we’re leveraging 5 petabytes of proprietary, realworld

activity-based car data—painstakingly normalized over

decades—in combination with synthetic data to power our AI

models. That makes our solutions fundamentally different in

terms of accuracy and transparency.

Macroeconomically, inflation, natural catastrophes, and parts

shortages are pushing claim severity up, while consumers face

more financial pressure than ever. Especially in Europe, we’re

seeing:

• Double-digit premium hikes for three consecutive years

• Growing gaps in coverage for natural catastrophes and cyber

risks

• Over 50% of consumers concerned about retirement security

This is where AI adds critical value. For example, our Qapter

solution acts like a “ChatGPT for claims estimates,” streamlining

repair decisions, reducing fraud, and improving speed—all while

improving customer experience and lowering cost. The key is

integrating AI into workflows responsibly, with transparency

and precision. That’s where we lead. Sustainability has become

a measurable dimension of this efficiency. Our CO₂e insights—

certified under ISO 14064 01—allow insurers to understand

the carbon impact of every claim decision, from repair to

replacement, aligning economic and environmental objectives.

QAgentic AI was one of the hottest topics at Insurtech

Insights last week. As the industry continues to evolve

and face new challenges, embracing Agentic AI

technologies can provide insurers with a competitive edge

and lead to improved outcomes for both companies and their

customers. What are some of the megatrends you’ve observed

in its adoption across the industry?

AAgentic AI is an exciting frontier—but it needs grounding

in data integrity and human oversight. One of the biggest

megatrends we’re seeing is that many AI companies

are building entirely on synthetic data. While synthetic data

can be useful, it also carries unspoken risks—notably around

representativeness, safety, and fairness.

What sets us apart is that we combine synthetic data with

decades of real car activity data that has been privacy-protected,

normalized, and governed. This gives our Agentic AI systems the

ability to reason and act with unprecedented precision.

Another major trend we must discuss is the rise of the hybrid AI

model. We’re seeing significant advantages when agentic systems

are paired with structured human intervention. Human-in-the-loop

design not only improves trust, but also enhances how machines

learn, especially in edge cases like fraud detection or total-loss

estimation.

On top of all of those, Gen Z is shaping the adoption curve. They

expect immediacy, transparency, and affordability. They’re deeply

concerned about both financial pressures and the future of the

planet. Agentic systems that can deliver self-service claims with

empathy—and do so sustainably—are going to define the next

generation of industry winners. Agentic AI also empowers climateconscious

decisioning—our systems suggest the most sustainable

repair options in real time, factoring verified CO₂ data, parts

sourcing, and repairability analytics.

QWhy is it essential to ensure the ethical use of data

when leveraging AI to safeguard the interests of both

consumers and businesses? Can you share with our

readers your thoughts on the importance of governance, ethics

and transparency?

AEthical AI begins with ethical data. Today, there’s a

misconception that AI systems are neutral. In reality, they

reflect the data they’re trained on. And if that data is purely

synthetic or poorly governed, the risks to consumers multiply—

bias, errors, and loss of trust.

That’s why Solera has invested heavily in data governance. Solera

is uniquely positioned with over 5 petabytes of normalized,

activity-based automotive claims data—privacy-protected,

compliant, and governed with rigor. This foundation enables us

to build AI models that are not only highly accurate, but also

explainable, ethical, and deeply reflective of real-world outcomes.

We adhere to an Ethical Data Charter that sets the foundation for

74 | 74 MODERN | MODERN INSURANCE

INSURANCE

INSURTECH

how we collect, train, and deploy AI. This goes way beyond just

compliance—it’s the only right way to provide AI solutions and is a

significant competitive advantage. Consumers, especially younger

ones, are demanding it. Over 80% of Gen Z consumers are worried

about Ethical AI, according to TIME.

AI can drive enormous value—but only if it’s built on a foundation

of trust. Governance, ethics, and transparency are the cornerstones

of that trust. Solera is also actively supporting governments in

different countries as a subject matter expert in shaping ethical

data and AI regulation. We’re also contributing to the development

of sustainability frameworks within insurance—bringing the same

discipline to emissions measurement and reporting that we’ve long

applied to data integrity and AI governance. That kind of publicprivate

collaboration is vital to long-term industry progress.

QLet’s talk about the combination of human and machine.

Empathy is so important in the claims process, how can

the consumer be assured of an empathetic approach

when filing a claim, and the insurer protect against fraud at the

same time? Why is it important to have a human in the loop with

AI?

AClaims are deeply emotional moments. A consumer isn’t

just filing a form—they’re recovering from a disruption.

That’s why empathy must be embedded in the claims

process, even when it’s AI-led. The solution is not to replace

humans, but to design AI with structured human intervention.

Hybrid systems allow us to deliver fast, scalable, touchless claims

while still capturing those moments that require human judgment

or care.

Interestingly, this hybrid approach also makes AI smarter. When

humans step in—particularly in ambiguous or sensitive cases—the

system learns from those interventions. It becomes more nuanced,

more equitable, and more trustworthy.

At the same time, fraud remains a major threat. By integrating

advanced AI with behavioural and contextual cues, we can detect

fraud patterns early without disrupting the honest consumer

experience. That’s how we protect both ends of the spectrum—

those who need empathy and those who exploit the system. It’s

not about AI versus human. It’s about AI with human values—a

partnership that lifts the entire experience. That same human-AI

partnership also supports ethical decisioning in sustainability—by

enabling agents and adjusters to review CO₂ impact scores and

make greener choices where appropriate.

Another major game changer is the rising of the green parts

industry. Today this industry is not “junkyard salvage.” It’s highly

sophisticated, leveraging advanced inspection tech, traceability

systems, and quality standards that rival new parts. Solera’s

yard management system, called “Nuclear” by our customers, is

considered the most powerful and widely adopted platform by the

green parts industry—an engine for enabling circularity at scale.

Solera has been part of shaping how sustainability is measured,

tracked, and integrated across the claims ecosystem throughout

European Circular Economy Stakeholder Platform’s in auto claims.

In Denmark, our collaboration with the national insurance industry

has established one of the world’s first countrywide frameworks

for sustainable estimatics—retrospectively calculating the carbon

footprint of auto claims dating back to 2021. This program serves

as a benchmark for how responsibly governed data can drive

measurable environmental accountability at scale.

At the heart of this movement is a simple truth: extending the life

of a well-functioning vehicle is the most sustainable thing we can

do. But as the vehicle mix shifts toward electric vehicles (EVs),

this principle is evolving. EVs bring new challenges around battery

life, diagnostics, and circularity. The demand for smarter, longerlasting

service life will intensify. At Solera, we are already preparing

for this shift—building the data infrastructure, AI intelligence, and

partner networks needed to support sustainability in an EVdominant

future.

It’s good for the planet, and it’s good for people. This is a networkof-networks

moment—a systemic shift in how the transportation

ecosystem operates, powered by the circular economy. It creates

a new industry that is more profitable, more resilient, and more

environmentally responsible. The leaders who recognize and

act on this opportunity will be the ones who shape the future.

Sustainable Estimatics is not just a tool—it’s the industry’s

reference model for aligning claims processes with the principles

of the circular economy, ethics, and long-term profitability.

QYou have often said sustainability and profitability go

hand-in-hand. What is AI’s role in improving efficiency

while simultaneously aiding in the responsible

stewardship of the environment?

AThe old model of raising premiums to chase losses is hitting

its limits. With three consecutive years of double-digit

increases, many consumers—especially Gen Z—are being

priced out of the system significantly.

One key process change is how we handle repairs. The choice

between repair versus replacement has become a defining issue

in both cost and climate impact. The difference is dramatic:

depending on regulation and industry norms, we see huge

disparities in carbon footprint across countries. And yet the

solution is in front of us. With AI, we now have the capability to

scale a repair-first model—one that is smarter, greener, and more

affordable. AI enables accurate, touchless assessments, smarter

parts sourcing, and better repairability analytics, all of which shift

the economics away from default replacement. With emissions

data that is ISO 14064 01 certified and spans Scope 1, 2, and 3,

we provide insurers with a complete and auditable view of their

environmental impact—enabling both performance benchmarking

and regulatory readiness ahead of the EU’s 2025 mandates.

Jing Liao

Chairwoman of The Solera

Foundation and Chief Administration

Officer of Solera Corp


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INSURTECH

EDITORIAL

BOARD

WELCOME to the Insur.Tech.Talk

Editorial Board.

Modern Insurance Magazine’s board of insurtech experts come together once again in this

latest issue, showcasing the very best thought leadership insights from the heart of the

insurtech marketplace.

This issue voices the thoughts of...

Denise Garth,

Chief Strategy Officer,

Majesco

Manjit Rana,

EVP Insurance, Clearspeed

Andy Cohen,

President, Snapsheet

Craig Olivier,

CTO at Genasys Technologies

Tim Hardcastle,

CEO and Co-Founder,

INSTANDA.


INSURTECH

The AI Business Strategy Advantage: Driving

Productivity, Growth and Profitability in Insurance

The May 2025 RBC report, “The Software Investors Handbook to AI,” notes GenAI is a seismic

technology landscape change, the fourth seminal shift over 40 years following the Internet,

Cloud, and Mobile with major implications for technology, businesses and society. Don’t believe

it? Search the number of data centers currently under construction to handle AI.

AI and GenAI are critical for insurance industry operations.

With rising operational costs, increased risks, talent shortages,

outdated technology and profitability challenges straining

insurers’ financial performance, competitiveness and growth, AI

and GenAI are poised to transform every aspect of the business.

AI is the innovation catalyst needed to help insurers stay ahead

of market trends and technological shifts, allowing them to

rethink the business model for a new era. The rapid proliferation

of data coupled with AI is reshaping the insurance industry

by unlocking business value and the potential to transform,

optimize and innovate business operations. It will elevate

customer experiences, improve risk assessment, underwriting,

billing and claims, and foster talent retention and acquisition.

The Business Value Advantage

The integration of AI and GenAI is transforming every

insurance business process. Detailed guidance, assessments,

and recommendations are improving decisions. AI automated

processing is improving productivity and accelerating

knowledge acquisition and employee performance. AI simplifies

the complex, enhances decision-making, provides transactional

guidance and optimizes every step.

AI has clear vision. It predicts risk shifts, anticipates customer

demands, and uses tech advancements to improve itself. AIsavvy

insurers have confidence to navigate complexities with

ease and improve business results.

A Bain report, “Bridging the Protection Gap: Affordability,

Access, and Risk Prevention,” highlights challenges facing

the insurance industry in matching price-to-risk profitably.

Bain anticipates AI-driven insurance industry improvements will

realize a 10–15% revenue uplift, up to 30% operating expense

savings, and a 30–50% reduction in P&C leakage (losses due to

errors, inefficiencies or fraud in claims).

With 50% of industry professionals retiring by 2030, its timely

capabilities accelerate onboarding, reskilling, and bending the

learning curve for employees. To capitalize, insurers must foster

technology literacy and critical thinking, then reshape employee

roles from repetitive transactional work to analytical, technical

and relationship-oriented skills.

AI and GenAI Leadership

Historically, the analytics spectrum is a cadre of “add on”

solutions with limited operational data access. Today, next-gen

intelligent solutions allow access to all operational data. Their

processes use embedded intelligence to capitalize on the entire

spectrum of analytics from BI to AI/ML and GenAI.

The strategic focus on AI and GenAI is accelerating development

of new models that enable a rethink of the business of insurance,

changing how we do business, what business we do, and

providing better risk assessments and insights than ever before.

AI will transform the entire insurance value chain, increasing

influence and spreading economic value in a modern business

and digital society.

Success requires leadership and the willingness to rethink the

business. We see these key characteristics of AI and GenAI

Leaders:

• Concentrate on core business processes (policy, billing, claims)

for competitive advantage

• Emphasize people and processes over technology to rethink

the business

• Move beyond operational productivity to revenue, profitability,

and employee empowerment

• Invest strategically in key areas to scale and maximize AI and

GenAI’s value, advancing broader investment

Insurance AI and GenAI are the business’ new facilitators. They

communicate. They decide. They anticipate and work with

unprecedented levels of intelligence.

Majesco is leading the industry in the use of AI and GenAI with

Majesco Copilot embedded in all our solutions. It’s not just a

technological upgrade. It is breaking barriers and creating a new

era of operational excellence, with early benchmarking results

showing 10-20 times productivity improvement that can lower

expense ratios and improve competitive market position.

AI is redefining what’s possible in insurance.

Denise Garth,

Chief Strategy Officer, Majesco


INSURTECH

All-In With AI? The Art

of the Possible If We Do

It Right

Manjit Rana

EVP Insurance, Clearspeed

The insurance industry is buzzing with AI. Attend

any conference these days and every presentation,

InsurTech demo, and booth message is centred

around AI - unlike the hype around blockchain

only a few years ago. AI-driven solutions are not

just theoretical; they are actively transforming

our sector, significantly boosting efficiency and

refining the customer experience.

While many current applications focus on automating

document processing and intelligent workflows, the true,

more innovative potential of AI “the art of the possible”

remains largely untapped in real-world settings. As the

technology evolves rapidly, it won’t be long before we

see use cases we haven’t yet imagined.

AI’s Impact Across Insurance Functions Today

Insurers are already realising the value of AI across the

insurance lifecycle.

In underwriting and pricing, AI is transforming risk

assessment: By analysing vast amounts of structured and

unstructured data, from IoT to telematics, in real-time,

it delivers deeper insights and enables more accurate,

personalised pricing. This improves loss ratios and helps

identify emerging trends early.

AI drastically accelerates decision-making: By supporting

more manual tasks and automation, it frees underwriters

to focus on complex cases.

In claims processing, AI streamlines everything from

First Notice of Loss (FNOL) to settlement. It significantly

enhances fraud detection by spotting anomalies humans

may miss, and allows fully automated processing for

simpler claims, delivering real-time resolutions and

reducing operational costs.

AI drives product innovation: It helps identify market

demands and develop refined offerings, for instance,

targeting dog owners with pet policies informed by

smartphone-based telematics.

Navigating the Challenges of AI Adoption Despite its

promise, AI adoption in insurance faces key hurdles.

Data challenges are paramount, AI needs large, accurate,

structured datasets, yet many insurers grapple with

fragmented, outdated, or inconsistent data. Privacy

and security concerns also require strict regulatory

compliance to avoid legal risk and maintain trust.

Regulatory and ethical concerns loom. Bias in training

data can lead to discriminatory outcomes in pricing or

claims. A lack of coordinated global regulation adds

uncertainty around liability and compliance. Transparency

is essential, AI decisions must be explainable to both

customers and regulators to build trust.

Organisational and cultural barriers are also significant.

The AI and data science talent gap makes recruitment

and retention tough. Existing employees may resist AI

due to fears around job loss or mistrust in machine-made

decisions.

Keeping Pace with Consumer-Facing Solutions

A major challenge is keeping pace with how fast

consumer-facing AI tools are evolving. Consumers can

download apps in minutes, tools that can create fake

invoices or alter images of car damage using free or lowcost

software.

So, while AI offers great opportunities to make the

industry more efficient and intelligent, it also brings risks.

Without a radical shift in how the industry addresses the

rise of AI-powered consumers, it’s unlikely to win the AI

arms race. The industry must find a way to compete with

bad actors while still offering genuine policyholders fast,

fair, and effective service.

This is the real question we must ask: With AI now

capable of processing vast unstructured data almost

instantly, are we finally approaching a shift? Can we move

from a reactive ‘safety net’ model to a proactive ‘guardian

angel’ approach, where insurance actively protects and

prevents negative outcomes, rather than just responding

after the fact?

Customer service sees huge gains: 24/7 support via

chatbots and virtual assistants handles routine queries,

quotes, and updates. AI automates repetitive tasks across

functions, enabling employees to focus on higher-value

activities.


INSURTECH

AI Overload or Underload? Finding

the Right Fit in Claims Technology

Artificial intelligence now dominates

many conversations in the world of claims

management. But carriers don’t need more

AI, they need the right AI claims solutions

implemented with flexibility and intentionality.

The discussion must shift from how much AI

a carrier can add to how smartly it is utilized.

This is the new frontier of configurable claims

technology, where the best outcomes result

from striking the right AI balance, not from

piling on additional features.

What Is AI Overload and Underload?

AI overload in insurance claims happens when too many features

are activated, often without a clear strategy, which can cause

workflow inefficiencies and user frustration as the tools meant

to streamline work end up overcomplicating it. There is also a

separate mental AI overload to consider, as carrier employees

need to constantly remember everything that is running, where

it is running, and maintain the domain knowledge to track and

manage it all.

AI underload is what occurs when available capabilities go

unused, leading to missed opportunities to reduce cycle times

and improve efficiencies. The tools exist, but either they are

not activated or they are underutilized. And if carriers are not

optimizing the existing AI claims solutions they have, are they

thinking in AI terms? They may not be leveraging existing

features because they don’t know about them, which means they

also don’t know about other tools that could prove revolutionary.

More AI does not equal better AI. The right approach is purposebuilt

AI insurance claims processing features that are configured

and activated to meet strategic goals.

One Size Doesn’t Fit All

Small, regional carriers and large multinationals have vastly

different digital needs, claims operations, resources, and

strategies. The use of AI may vary across workflows, even within

the same carrier, as what works for subrogation may not work

for first notice of loss (FNOL) or fraud red flag detection.

needs can dictate technology investment, rather than the other

way around, and the right digital claims management platform

enables this optimization.

To maximize key features and tap into unrealized potential,

carriers can utilize these strategies to drive smart configuration:

• Conduct a feature audit to understand which features are

active.

• Develop the right workflow from idea to prompt creation to

testing.

• Measure the impact of features.

• Train teams intentionally on relevant modules instead of every

possible feature.

• Optimize first, then expand.

Carriers should keep the future in mind as they develop AI tools.

Building AI claims solutions that stack on each other increases

the speed of delivery over time. This may mean taking on very

foundational problems first to move faster in the future.

Smart Configuration is the Future

AI will continue to evolve, but carriers must resist the temptation

to chase every new idea. Carriers do not need more technology,

they need smarter insurance claims management solutions. The

best claims management platforms aren’t necessarily the most

powerful, they’re the most flexible.

By investing in claims management tools that are flexible,

configurable, and strategically deployed, insurers can build an

AI strategy that strikes the right balance, avoiding the pitfalls of

overload and underload. The result is a smarter, more adaptive

approach to automation that delivers better outcomes across

every stage of the claims journey.

The real value lies in configurability, meaning the ability to turn

on what a carrier needs and turn off what it does not. With the

flexibility to make fast decisions, each carrier can configure its

claims management system to best meet its customers’ needs.

Configurability also means transparency. It is about knowing

what is in the black box, and knowing how AI works gives

carriers a strategic advantage as they maximize its capabilities.

With modular, flexible claims management platforms, carriers

can automate parts of the workflow and activate features like

fraud analytics, FNOL triage, and document modules. This ability

to configure, test, and iterate in real time means the business

Andy Cohen

President, Snapsheet


INSURTECH

AI at the Heart of Insurance: Empowering

People, Enhancing Processes

Artificial Intelligence is no longer a

buzzword on the sidelines of insurance

innovation; it is becoming core to how we

build, distribute and manage insurance

products. As CTO of a SaaS-based

insurtech platform serving insurers and

MGAs across the UK and beyond, I’ve

seen firsthand how the smart application

of AI is unlocking efficiency, improving

decision-making and creating better

experiences across the value chain.

But I want to be clear: this is not about using AI for the sake of

it. It’s about using the right technology, for the right reasons, to

achieve the right outcomes.

AI as the Co-Pilot, Not the Autopilot

Over the next 12–24 months, I believe the most valuable use of

AI will be assistive rather than autonomous. We’re seeing a clear

shift toward “co-pilot” models; AI tools that help underwriters,

claims handlers and even customer service teams work smarter,

faster and more accurately.

For instance, AI is now able to extract structured data

from unstructured inputs, such as scanned claim forms or

handwritten documents, freeing up teams from manual entry

and improving accuracy. In underwriting, machine learning

models can suggest pricing adjustments or flag unusual risks

based on external datasets. But final decisions still rest with the

humans. That’s essential for trust, compliance and fairness.

Streamlining Claims and Customer Service

It’s easy to get swept up in the hype around generative AI. But

for insurers, the real wins still come from solving the boring

problems brilliantly.

Building Better Products, Faster Than Ever

AI is also reshaping how products are designed and delivered.

With access to richer data, whether from IoT devices, satellite

imagery or behavioural analytics, insurers can now price risk

more dynamically and offer modular products tailored to each

customer’s profile.

We’re seeing growing interest in personalised commercial

cover, usage-based fleet insurance and real-time property

underwriting. Platforms like ours need to be flexible and open to

support that. With low-code tooling, pre-integrated third-party

services and extensive API capabilities, we’re helping insurers

move from idea to market in days, not months.

UK Market: Innovation Meets Responsibility

Of course, with great power comes great responsibility. The

FCA and Bank of England have made it clear: AI adoption

must be explainable, fair and compliant with Consumer Duty.

The good news is that most insurers agree. AI should support

human judgment, not replace it.

That’s why we focus on transparency in everything we do.

Whether it’s a rules engine driving automated claims decisions

or a machine learning model recommending a policy add-on,

we ensure that clients can track, test and override outputs

where needed.

Final Thoughts

In my view, AI’s role in our industry over the coming years

is best described as a productivity amplifier and a decision

enabler. It will help insurers scale faster, respond smarter and

personalise more deeply, but only when embedded in the right

way.

Our mission at Genasys is to give insurers the tools to innovate

safely and responsibly. In many ways, we’ve built the connective

tissue – not the brain. Our job is to make sure whatever AI you

use works where it matters: inside real workflows. The future of

insurance isn’t just digital; it’s intelligently human. And AI, used

wisely, will help us get there.

Claims is one of the most promising areas for AI in the short

term. We’re already seeing solutions that can process basic

claims automatically, such as cracked windscreens or simple

flight delays. If predefined rules and data checks are met,

these claims can be approved in seconds. Where complexity or

ambiguity exists, AI steps in to triage the case and pass it to a

human for review.

Similarly, AI-powered chatbots are evolving rapidly. They’re

not just answering FAQs anymore; they’re guiding customers

through renewals, collecting first notice of loss information

and even helping customers understand their cover. For many

insurers, this means fewer routine calls and faster resolution

times, benefits that flow directly to customer satisfaction.

At Genasys, we’ve focused on ensuring our platform integrates

easily with leading AI tools. From claims automation engines to

intelligent chat solutions, we see our role as enabling insurers

to plug in the innovation that makes sense for them without

needing to overhaul their systems.

Craig Olivier

CTO at Genasys Technologies


INSURTECH

‘Shoot for the Moon’ Transforming

Insurance with AI Precision

The global insurance industry has been undergoing a transformation over the last 5- 7 years and

now that is set to get rocket boosters. At the heart of this change are Artificial Intelligence (AI) and

Hyper-Automation. If we cut through the hype, and there’s a fair amount of it, AI is more than just an

accelerator; it’s a revolutionary force reshaping the way insurers operate, innovate and connect with

customers.

However, leveraging AI effectively requires more than adopting the

latest technology and hoping for the best. It’s about taking precise,

strategic steps rooted in a clear understanding of the business

environment, crucially what customers want, and then to critically

assess the AI opportunities, challenges and potential.

Laying for Foundation for Success

To realise AI’s transformative impact, it’s essential to acknowledge

the challenges that have been holding the industry back for years.

Legacy technology has historically limited insurers’ ability to adapt

to customer needs or innovate at the speed required by today’s

market. Fortunately, the narrative is changing, and fast.

By adopting agile, cloud-native API-first modern platforms,

insurers are overcoming these limitations. These agile, intelligent

and interoperable solutions improve data quality and create a

responsive, customer-centric ecosystem that’s primed for AI

integration. Whether it’s adapting to regulatory changes or

delivering standout personalised customer experiences, an AIready

foundation is the key to staying relevant in a rapidly evolving

landscape.

Overcoming Barriers to Adoption

Cost was once a significant barrier to adopting AI. Only a few

years ago, implementing AI seemed like an insurmountable

challenge, accessible only to major tech players with deep pockets.

Today, this barrier is rapidly eroding as the costs of developing,

introducing, and training AI models continue to drop.

Regardless of the specific strategy, agility is a prerequisite for

success. Insurers need platforms that allow them to iterate

rapidly and adapt to shifting market demands in real time. For

instance, our clients are leveraging hyper-automation and AI to

adjust coverage and pricing in seconds and minutes. This level of

responsiveness doesn’t just improve efficiency; it fundamentally

alters the competitive landscape, setting new standards for what’s

possible in the industry.

The Road Ahead

For insurers, standing still simply isn’t an option. The gap between

those experimenting with AI and those deeply embedding it

into their operations is widening. Forward-thinking insurers

are demonstrating that advanced AI adoption is a hallmark of

industry leadership. They’re not just streamlining processes; they’re

usurping the competition with market-differentiating products and

customer service.

That said, AI cannot reach its full potential without the right

frameworks and oversight. Solving data quality issues, meeting

shifting demands or offering precision pricing requires technology

to work seamlessly alongside human expertise and accountability.

The future of insurance will not just belong to those with access to

AI, but to those bold enough to act decisively, adapt swiftly and

think big.

Insurers of all sizes are now tactically investing in AI where it

promises the most significant impact. At INSTANDA, for example,

clients are embedding AI into everyday operations to achieve

game changing outcomes. We’ve seen the coupling of AI chatbots

and automated policy administration increase quote volumes by

up to 50%, and administration inefficiencies all but eliminated.

These efficiencies are not just saving money, they’re fuelling

growth, with clients reporting annual revenue increases of 25%.

A Balanced Approach to Innovation

At INSTANDA, we’ve embraced a measured approach to AI

adoption that keeps an eye on the ‘here and now’ while shooting

for bigger, bolder moves. Our “spread bet” strategy combines lowrisk,

high-reward projects that deliver immediate improvements

with moonshot initiatives aimed at long-term innovation. One

particularly exciting initiative involves using AI to empower brokers

with advanced tools for customising pricing and underwriting

complex commercial products. This innovation is enhancing both

precision and efficiency in ways previously thought impossible.

Tim Hardcastle

CEO and Co-Founder, INSTANDA.


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Customer Experience.

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customer-centric Policy Administration and

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Leverage hyper-automation and

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Operate with retail-like agility to:

• Balance digital self-service and high-touch interactions

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• Gain a 360° view of the customer

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Join the global community of insurers redefining insurance.

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Modern Insurance Magazine Issue 72

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