Industrial Innovations

Industrial Innovations

Applying Data Mining Techniques in order to assess the Violations of Life Insurance Customers

Document Type : Original Article

Authors
hossein amoozadkhalili 1
mirsaeid hosseini 2
1 islamic azad university sari branch
2 Assistant Prof., Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran.
10.61186/jii.2.1.56
Abstract
The issue of fraud in insurance claims is one of the problems faced by insurance companies. Therefore, the issue of discovering such frauds in all types of insurances is one of the topics of interest for experts in various fields. Insurance fraud can be defined as taking damages from insurance companies by resorting to fraudulent means and documents. Losses caused through fraudulent activities affect the interests of insurers and potentially their financial stability. The current research uses data mining techniques to identify the fraudulent behavior of life insurance policyholders in insurance companies in order to identify the factors affecting these behaviors. The results of the article show that decision tree and support vector machine techniques are useful in identifying frauds and can be considered as the main center in business management to detect fraud. The results of the implementation of different methods on the studied dataset show the superiority of the neural network method over other methods. The neural network method has succeeded in classifying the desired classes in this research with an accuracy of 90.83, which is a good accuracy. Also, from the created decision tree, it is possible to detect frauds or the possibility of violations before issuing the insurance policy by using the data of the insurers under investigation, and if the violation is proven, it can be prevented from being issued.
Keywords
Data mining
Life insurance
Fraud detection
Decision tree
Support Vector Machine (SVM)
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  • Receive Date 24 February 2024
  • Revise Date 31 May 2024
  • Accept Date 22 June 2024