Smart Consulting

Fraud Detection

Travel Insurance Fraud Identification

Objective

A major insurance company in Singapore used to manually examine each travel insurance claim to identify potentially fraudulent one.
Suspicious claims were subject to a more detailed investigation.
The project objective was to develop a score to identify potentially fraudulent claims which would be subject to greater scrutiny.

Solution

Adaboost - short for Adaptive Boosting, a powerful machine learning algorithm was used for detecting potentially fraudulent cases .
Substantial lift demonstrated. – on the below test data set it sufficed to examine 7.75% of all claims to identify 91.67% of all fraudulent claims.

Challenges

Data included 77,445 claim records of which only 120 had been determined to be potentially fraudulent.
So identified potentially fraudulent claims are rare events (0.15%) and therefore hard to detect.
It was however expected that there could be a large number of undetected fraudulent claims.

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