quarterly-insurtech-briefing-q4-2017

Transaction Spotlight
Guidewire Acquisition of Cyence
Visesh Gosrani – Risk and Actuarial Director,
Cyence Risk Analytics, Guidewire
• 15+ years as an Actuary and Chief Risk
Officer in the insurance industry
• Prior to joining Cyence, Visesh founded
and served as COO of Shoal Insurance, a
start-up offering home insurance
• Previously served as Chief Risk Officer
of Asta Managing Agency and Managing
Director at Cygnus Risk and Actuarial
Philip Rosace – Senior Solutions Manager,
Cyence Risk Analytics, Guidewire
• Joined Cyence as its 6th employee in
January 2015 as a domain expert and
product manager for early stage product
• 5+ years of cyber insurance underwriting
experience in various positions with Zurich
and AIG
Describe Cyence’s approach to cyber risk modeling. How is it different from other cyber risk
models that have been developed for the insurance industry?
The cyber risk landscape is continuously shifting and insured risk profiles evolve as businesses adjust their technology
and service provider usage. Additionally, a cyber intruder presents an active adversary who adjusts their efforts
depending on a target’s value. Given the complexity of risk measurement for individual companies and the accumulation
of exposures, we take a data science approach leveraging data collection at internet scale, machine learning, game
theory and behavioral economics to put cyber into dollars and probabilities for the insurance markets.
At Cyence, we have built a data listening platform that continually collects both technical and non-technical cyber risk
information and leverages machine learning to model frequency, severity and the accumulation of cyber exposures. We
have combined the expertise of economic risk modelers, data scientists, white hat hackers and insurance professionals
all under the same roof. Our model can be deployed on all insureds, from small businesses to large commercial risks,
analyzing both individual and accumulation risks in dollars and probabilities – all based on real exposure data.
What are the biggest challenges involved in modeling cyber and other 21st century risks?
21st century risks like cyber are difficult to model for a variety of reasons. The changing risk landscape and multitude
of accumulation paths (including non-obvious ones) are the most evident from our perspective. Utilizing Cyence’s data
listening solution, we are able to collect a wide variety of accumulation data on insureds to help insurers model their
value at risk from customizable disaster scenarios. Additionally, our models leverage machine learning techniques and
real event data to keep pace with the changes in risks for individual companies and aggregate portfolio exposures.
How do you envision the growth trajectory of the global cyber insurance market? What are the
biggest inhibitors to growth of cyber insurance today?
We believe that cyber insurance will grow as cyber risks continue to pervade throughout businesses’ operations and
products. Traditional data breach coverage should continue to grow from General Data Protection Regulation (GDPR)
and other similar regulations expanding the uptake of this coverage globally, and current markets will likely continue to
have increased penetration and higher limits sold. We see many insurers focusing on things like small business portals
and health check reports to expand uptake rates in the SMB space – the key challenge here is keeping the underwriting
process efficient and objective, and we have been able to deliver solutions that do both for our clients. Due to shared
infrastructure and common use of technology products, it will also be important for insurers to evaluate and manage
their accumulation of exposures based on real data with a software application like Cyence.
New products which cover cyber as a peril on other P&C lines of coverage also present opportunities for growth, but
insurers will need to embrace non-actuarial approaches to model emerging exposures with little claim data available.
Other coverages like reputational harm (from a cyber event or other type of event for that matter) present obvious
business value to insureds, but challenge insurers from both the underwriting and claim valuation perspectives. Carriers
will need to leverage new sources of data and actuaries will need to expand their tool kit to leverage data science and
machine learning when tackling these problems.
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