Nordin, Shareh-Zulhelmi Shareh and Wah, Yap Bee and Ng, Kok Haur and Hashim, Asmawi and Norimah, Rambeli and Jalil, Norasibah Abdul (2024) Predicting automobile insurance fraud using classical and machine learning models. International Journal of Electrical and Computer Engineering, 14 (1). 911 – 921. ISSN 2088-8708, DOI https://doi.org/10.11591/ijece.v14i1.pp911-921.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | United International University |
| Uncontrolled Keywords: | Automobile insurance fraud; Classical and machine learning models |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Faculty of Science > Institute of Mathematical Sciences |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Jun 2024 02:19 |
| Last Modified: | 14 Jun 2024 02:19 |
| URI: | http://eprints.um.edu.my/id/eprint/44882 |
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