According to PRIAO, the losses incurred due to fraudulent activities have tremendous impact on the insurance companies. Various types of frauds across all the insurance lines comprise of a wide range of overstatements to intentionally causing an accident or injuries that lead to increased costs and premiums that might eventually affect the policyholders as well. Using data mining methods to come up with patterns in the data, one of the ways to identify fraud in the claimed losses is referring to the historical records of the past identified frauds.

This is part of a report on a recent study by IRC entitled “Data Analytics of Car Insurance Fraud Information: Recommendations for Fraud Identification” that has been conducted using applied approach in order to investigate and identify the regular fraud patterns in car insurance business as one of the major challenges of the insurance industry.

In this study, using descriptive statistical methodology and machine learning algorithms, the effective variables in car insurance frauds and fraudulent payments in Iran and the world have been provided. After introducing some of the most frequent types of frauds in domestic insurance companies, a number of strategies in order to identify and prevent insurance frauds based on the experts’ views and machine learning have been proposed.

The results of the study reveal that training claim and sale officers, establishing relationship with legal authorities, forensic medicine authority, and other relevant organizations, and compiling a list of risky individuals with relevant claim records of determined frauds or violations are some of the major recommendations for the prevention of insurance frauds according to machine learning algorithms. It must be noted that the algorithms used in this study can be employed for backend programming of a software or application.

If interested to read more about the study, please refer to the research projects section of IRC’s Persian website.