Unknown, UnknownsReduce Insurance Fraud with

IBM Predictive Analytics

Every day insurers uncover over

350 insurance frauds, worth an estimated

£3.6 million

(ABI, 2015).

A Case Study

Client

A motor Insurance company, traditionally covering high risk driversbut looking to make inroads into the standard insurance market.

How does it work?IBM SPSS predictive analytics solutions instantly assess the fraud risk of submitted claims and

enable you to move quickly – and in many cases, instantly – to settlement or investigation. These

solutions use proven technologies known as business rules and predicative modeling, which analyse

historical claim data to predict claimant behaviour and identify both known and unknown fraud risks.

ChallengeIntelligence surfaced from the field pointed to a rise in fraudulent claims beyond the "usual", requiring a more systematic, efficient and accurate way to pinpoint fraud.

SolutionA score-based approach to route high risk claims for investigation, or expedite those with a low risk of being fraudulent. A world class claims handling process to offer them an edge in a new market dominated by big name national providers.

Filing a claim is perhaps the single most important “momentof truth” in the relationship between insurers and the insured.

The cost of insurance fraud adds

£50 to the annual insurance bill

per policy holder (ABI, 2015).

£

400%ROI

£9million

Subrogation Recoveries

88%Success rate in pursuing

fraudulent claims (Up 38%)