Employing unsupervised learning to detect fraudulent claims in auto insurance (isolation forest)

Subject Areas : Management Accounting

farbod khanizadeh ¹ , Farzan Khamesian ² , Maryam Esna-Ashari ^{3 *}

1 - Assistant Professor, Property and Casualty Insurance Research Group, Insurance Research Group, Tehran, Iran
2 - Assistant Professor, General Insurance Research Group, Insurance Research Group, Tehran, Iran
3 - Assistant Professor, Property and Casualty Insurance Research Group, Insurance Research Group, Tehran, Iran. (Corresponding author)

Received: 2022-02-03 Accepted : 2022-07-23 Published : 2022-08-23

Keywords: Unsupervised learning, Isolation forest, Fraud detection, Auto insurance,

Abstract :

For insurance companies, fraud detection strategies are of significant importance. Lack of such a plan to prevent insurance fraud and making payments quickly to insured in order to compensate for losses will lead to customer satisfaction and increase companies’ portfolio in short term. However in the long run, it will have dire consequences for the insurance industry. In other words, the cost of fraudulent claims would be transferred indirectly to insured in the form of a rise in premiums. The purpose of this study is to provide insurers with a mechanism to detect fraudulent claims. This goal is achieved through an unsupervised algorithm to detect anomalies in the data set. The use of this algorithm, as it is an ensemble learning, increases the accuracy in detecting suspicious cases and reduces false positives. According to the results, the damage to the culprit, the type and use of the vehicle, and the sex of the victim are among the most important indicators in the detection of fraudulent cases.

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