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All Journal International Journal of Electrical and Computer Engineering
Shareh Nordin, Shareh-Zulhelmi
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Computer Science & IT Electrical & Electronics Engineering

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Predicting automobile insurance fraud using classical and machine learning models Shareh Nordin, Shareh-Zulhelmi; Wah, Yap Bee; Haur, Ng Kok; Hashim, Asmawi; Rambeli, Norimah; Jalil, Norasibah Abdul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp911-921

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.
Co-Authors Hashim, Asmawi Haur, Ng Kok Jalil, Norasibah Abdul Rambeli, Norimah Wah, Yap Bee