Auto-Insurance Fraud Detection Using Machine Learning Classification Models

Toluwalope, Toluwalope and Shahra, Essa and Bassura, Shadi (2023) Auto-Insurance Fraud Detection Using Machine Learning Classification Models. In: 8th International Congress on Information and Communication Technology, 20th - 23rd February 2023, London, UK.

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This work explored six machine learning algorithms: Extreme Gradient Boosting (XGBoost), Logistic Regression, Random Forest, Decision tree, Support Vector Machine (SVM), and Naïve Bayes to determine the best algorithm for detecting insurance fraud. The following were used to evaluate the six models: Confusion matrix, Accuracy, Precision, Recall, and F1-measure. The result showed that Random Forest outperformed the others in terms of accuracy. Extreme Gradient Boosting (Xgboost) had the highest precision and F1-measure scores, while the Decision Tree had the highest Recall score. Although two methods (Analysis of Variance (ANOVA) and Random Forest Classifier) were compared to determine the best feature selection, the significant features were selected using the Random Forest classifier because of the many benefits of using this method. The results of this study will be beneficial to insurance companies, stakeholders and policyholders.

Item Type: Conference or Workshop Item (Paper)
13 December 2022Accepted
26 October 2023Published Online
Uncontrolled Keywords: Fraud Detection, Classification, Machine Learning, Random Forest
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Essa Shahra
Date Deposited: 11 Apr 2023 14:42
Last Modified: 09 Aug 2023 15:37

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