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Predicting automobile insurance fraud using classical and machine learning models

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General Information Cached
Title
Predicting automobile insurance fraud using classical and machine learning models
Type
Article
Main Author
Asmawi Haji Hashim
Additional Authors
  • Norimah Rambeli @ Ramli
  • Norasibah Abdul Jalil
Subject
H Social Sciences (General)
ISBN
2088-8708
Language Code
eng
Bibliographic Details
Place of Production
Tanjung Malim
Publisher
Fakulti Pengurusan & Ekonomi
Year of publication
2024
Notes
International Journal of Electrical and Computer Engineering
Dissertation Note
Other
Degree Type
Others
Corporate Name
Universiti Pendidikan Sultan Idris

Abstract

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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