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Insurance fraud detection: Evidence from artificial intelligence and machine learning

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  • Aslam, Faheem
  • Hunjra, Ahmed Imran
  • Ftiti, Zied
  • Louhichi, Wael
  • Shams, Tahira

Abstract

This study proposes a framework for fraud detection in the auto insurance industry by using predictive models. The feature selection is performed utilizing a publicly available car insurance dataset and uncovers the most influential feature through Boruta algorithm. Three predictive models (logistic regression, support vector machine, and naïve Bayes) are applied for developing a fraud detection mechanism. Six metrics are computed from the confusion matrix to assess the performance of the predictive model. The results reveal that the support vector machine outperforms in terms of accuracy, and the logistic regression achieves the highest f-measure score. Each influential feature's ranking is performed, and it is revealed that the fault, base policy, and age of the policyholder are the most influential features. The findings of this study are beneficial for fraud detection in the auto insurance industry. Additionally, the underlying framework holds a functionality for real-time problem-solving in the auto insurance industry.

Suggested Citation

  • Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
  • Handle: RePEc:eee:riibaf:v:62:y:2022:i:c:s0275531922001325
    DOI: 10.1016/j.ribaf.2022.101744
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    1. Harel, Arie & Harpaz, Giora, 2021. "Forecasting stock prices," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 249-256.
    2. Aderemi O. Adewumi & Andronicus A. Akinyelu, 2017. "A survey of machine-learning and nature-inspired based credit card fraud detection techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 937-953, November.
    3. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    4. Liu, Tingting & Lu, Zhongjin (Gene) & Shu, Tao & Wei, Fengrong, 2022. "Unique bidder-target relatedness and synergies creation in mergers and acquisitions," Journal of Corporate Finance, Elsevier, vol. 73(C).
    5. Yu, Lean & Huang, Xiaowen & Yin, Hang, 2020. "Can machine learning paradigm improve attribute noise problem in credit risk classification?," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 440-455.
    6. Yu, Lean & Yao, Xiao & Zhang, Xiaoming & Yin, Hang & Liu, Jia, 2020. "A novel dual-weighted fuzzy proximal support vector machine with application to credit risk analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    7. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    8. Liu, Ruicheng & Pun, Chi Seng, 2022. "Machine-Learning-enhanced systemic risk measure: A Two-Step supervised learning approach," Journal of Banking & Finance, Elsevier, vol. 136(C).
    9. Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022. "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, vol. 48(C).
    10. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    11. Polyzos, Stathis & Samitas, Aristeidis & Kampouris, Ilias, 2021. "Economic stimulus through bank regulation: Government responses to the COVID-19 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    12. Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
    13. William Lesch & Johannes Brinkmann, 2011. "Consumer Insurance Fraud/Abuse as Co-creation and Co-responsibility: A New Paradigm," Journal of Business Ethics, Springer, vol. 103(1), pages 17-32, April.
    14. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    15. Michael Danquah & David Mensah Otoo & Amoah Baah†Nuakoh, 2018. "Cost efficiency of insurance firms in Ghana," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 39(2), pages 213-225, March.
    16. Véronique Van Vlasselaer & Tina Eliassi-Rad & Leman Akoglu & Monique Snoeck & Bart Baesens, 2017. "GOTCHA! Network-Based Fraud Detection for Social Security Fraud," Management Science, INFORMS, vol. 63(9), pages 3090-3110, September.
    17. Chamal Gomes & Zhuo Jin & Hailiang Yang, 2021. "Insurance fraud detection with unsupervised deep learning," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 591-624, September.
    18. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
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