PCM Vol.3 Issue 12


In this issue, we will explore the development of eCommerce, the upcoming trends in 2018 and the key challenges eCommerce merchants face trying to keep up with the fast-paced changes.

Adaptive behavioral analytics can be used to detect

anomalies in real time to identify new types of transactional

fraud when they occur, without time-consuming (and

expensive) manual intervention. The technology also has

become extremely cost-effective to implement.

Adaptive behavioral analytics brings two key components

to help financial institutions minimize fraud and reduce


1. Individualization

Rather than only applying global rules to a vast data set

of thousands of customers, behavioral analytics focuses

on building anonymous statistical profiles of individuals,

using hundreds of inputs. This micro approach helps

banks understand changes being made to the account,

to contextualize what is going on, and detect anomalies

across multiple, complex data collectors. For example,

real estate attorneys have very different transaction

patterns versus those of printing businesses, so it’s

important that a platform is able to contextualize the

types of transactions a bank would expect.

2. Machine-learning capability

Models using adaptive behavioral analytics can teach

themselves. They can “study” customer transactions and

detect different patterns of fraud, constantly updating

their assessment of what is fraudulent and what is not.

By contrast, static models will degrade quickly. Adaptive

models are “non-degrading systems,” obviating the

need for a massive update each year, or continuous

manual updates by staff. Machine-learning models can

also constantly tweak how they weigh inputs to make an

accept or reject decision, including how accountholders

logged on, what their usage patterns are, the destination

of the funds, and how long accounts have been opened.

David Excell

Co-Founder & CTO at Featurespace

Dave is Co-Founder and CTO of Featurespace. He

studied with Professor Bill Fitzgerald (Featurespace

Co-Founder) at the University of Cambridge, where

he developed the ARIC platform. Under Dave’s

leadership, Featurespace has grown from a concept to

a commercial success with many blue-chip customers.

Dave has been awarded 11 prizes and scholarships for

his academic and commercial achievements, including

the 2011 ITC Enterprise Award for Young Entrepreneur.

A practical approach to fraud management

The fundamental takeaway to this changing landscape

is adaptation and evolution. Fraudsters are not static, so

risk models should not be either. They must be able to

detect behavioral anomalies in real time to identify new

types of transactional fraud when they occur, without

manual intervention. This reduces false-positive alerts,

allowing banks to accept more genuine transactions by

better understanding the legitimate behavior of individual

customers. It’s not a question of whether banks can fight

fraud, it’s how efficient and cost effective they can do it

in a world of shrinking payment windows for ACH and

wire transfers. Machine learning and individualization

will help banks adapt rapidly and avoid degradation of

their transaction fraud monitoring. Early adopters of this

practice will ultimately have a competitive advantage over

their peers due to decreased fraud expenses, increased

operational efficiency and most importantly, a better

customer experience.

About Featurespace

Created the ARIC platform, a real-time, machine

learning software system. With offices in the UK and

US, Featurespace has deployed ARIC to financial

services and gaming organizations that have services

or products in more than 180 countries. Customers

include Ally Bank, TSYS®, Worldpay PLC, Playtech,

PaddyPowerBetfair PLC, Vocalink Zapp, CashFlows

and William Hill.


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