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Insurance Claim Analytics

Insurance Claim Analytics is one of the most important applications of analytics in insurance business and is used to help the insurers in improving the quality of claims. Identification of fraud claims is a big concern for Insurance companies. With the advent of technology and tightening of regulations in the insurance sector, the time to investigate a claim is also ever deceasing. It is the need of hour to proactively identify fraudulent claims and act within the time to prevent fraudulent claims. More and more cases of staged accidents are being reported. Accidents sometimes are staged where unsuspecting motorists are forced to crash into the back of fraudster’s vehicle. After that claims are made against innocent motorists often including accounts of fictitious injury of gang members. Our clients, having witnessed high increase in their market share of motor insurance market had to face, as a byproduct, a huge increase in the number of fraud claims.

Business problem

A leading Insurance company wanted to develop an analytical tool to accurately identify fraud claims for motor insurance. The manual process of fraud claim identification was increasing the chances of errors like missing or wrong calculations, providing more time to the fraudsters and related with high cost.

Objective

The objective was to develop a set of rule or indicators that can flag potential fraud claims as soon as they registered in the system. The ultimate aim was identified as development of a scoring engine using key indicator to predict/identify the suspicious claims.

Our Approach

The data used for the analysis was mainly claim behavior, Claimant’s behavior and profile, Demographics, Historical policy information, macro-economics and circumstantial story. Once the business objective was identified, we used statistical methods like correlation, association rules, clustering of variables etc. to identify the key drivers of fraud behavior.

Based on the distribution of fraud claims and the relationship of different key drivers with the target, logistic regression was found most suitable to serve the business objectives.

Results

The process of identification/flagging of the suspicious claims was fully automated. Now the client can use the score given by the model to tag very risky and costly claims. The claims with higher risk score are given preference and investigated more rigorously. The model is able to predict fraud claims with >70% accuracy.