Management of Fraud: Case of an Indian Insurance Company

Abstract

Frauds in insurance are typically where a fraudster tries to gain undue benefit from the insurance contract by ignorance or wilful manipulation. Using the claims data in motor insurance obtained from a Mumbai based insurance company for the time period of 2010-2016, this study focuses on studying the pattern exhibited by those claims which have been rejected and accepted as well. The prime objective of the study is to identify the important or the significant triggers of fraud and predicting the fraudulent behaviour of the customers using the identified triggers in an existing algorithm. This study makes use of statistical techniques like logistic regression & CHAID (Chi Square Automatic Interaction Detection) technique to identify the significant fraud triggers and to determine the probability of rejection & acceptance of each claim coming in future respectively. Data mining techniques like decision tree and confusion matrix are used on the important parameters to find all possible combinations of these significant variables and the bucket for each combination.

This study finds that variables like Seats/Tonnage, No Claim Bonus, Type of Vehicle, Gross Written Premium, Sum Insured, Discounts, State Similarity and Previous Insurance details are found to be significant at 1% level of significance. The variables like Branch Code and Risk Types are found to be significant at 5% level of signify cance. The Gain chart depicts that our model is a fairly good model. This research would help the insurance company in settling the legitimate claims within less time and less cost and would also help in identifying the fraudulent claims.