Predicting automobile insurance fraud using classical and machine learning models

Asmawi Haji Hashim

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Type :	Article
Subject :	H Social Sciences (General)
ISBN :	2088-8708
Main Author :	Asmawi Haji Hashim
Additional Authors :	Norimah Rambeli @ Ramli Norasibah Abdul Jalil
Title :	Predicting automobile insurance fraud using classical and machine learning models
Hits :	42

Place of Production :	Tanjung Malim
Publisher :	Fakulti Pengurusan & Ekonomi
Year of Publication :	2024
Notes :	International Journal of Electrical and Computer Engineering
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link
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Abstract : Universiti Pendidikan Sultan Idris

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases. © 2024 Institute of Advanced Engineering and Science. All rights reserved.

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