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

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Khanizadeh Farbod; KHAMESIAN FARZAN; Esna Ashari Maryam

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Journal Paper

Paper Information

Journal: MANAGEMENT ACCOUNTING Year:2022 | Volume:15 | Issue:53 Page(s): 141-153

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Information Journal Paper

Title

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

Author(s)

Khanizadeh Farbod | KHAMESIAN FARZAN | Esna Ashari Maryam | Issue Writer Certificate

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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APA: Copy

Khanizadeh, Farbod, KHAMESIAN, FARZAN, & Esna Ashari, Maryam. (2022). Employing unsupervised learning to detect fraudulent claims in auto insurance (isolation forest). MANAGEMENT ACCOUNTING, 15(53 ), 141-153. SID. https://sid.ir/paper/1063243/en

Vancouver: Copy

Khanizadeh Farbod, KHAMESIAN FARZAN, Esna Ashari Maryam. Employing unsupervised learning to detect fraudulent claims in auto insurance (isolation forest). MANAGEMENT ACCOUNTING[Internet]. 2022;15(53 ):141-153. Available from: https://sid.ir/paper/1063243/en

IEEE: Copy

Farbod Khanizadeh, FARZAN KHAMESIAN, and Maryam Esna Ashari, “Employing unsupervised learning to detect fraudulent claims in auto insurance (isolation forest),” MANAGEMENT ACCOUNTING, vol. 15, no. 53 , pp. 141–153, 2022, [Online]. Available: https://sid.ir/paper/1063243/en

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