Data science for insurance fraud detection: a review

Denisa Banulescu-Radu () and Kougblenou Yannick ()
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Denisa Banulescu-Radu: LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne
Kougblenou Yannick: LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne

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Abstract: The chapter 'Data Science for Insurance Fraud Detection: A Review' delves into the application of data science and machine learning techniques to combat insurance fraud. It begins by discussing the significance of insurance fraud and its impact on the industry, followed by an overview of the historical development of fraud detection methods. The chapter then categorizes fraud detection techniques into unsupervised, supervised, and social network learning approaches, each with its unique advantages and challenges. Notably, the text emphasizes the importance of cost-sensitive learning to address the rarity and high cost of fraudulent claims effectively. The chapter also includes an empirical illustration, demonstrating the superior performance of advanced machine learning models compared to traditional econometric methods in both statistical and financial terms. Throughout, the text highlights the evolving nature of fraud and the need for adaptive, real-time fraud detection systems. This comprehensive review offers valuable insights for professionals seeking to enhance their fraud detection strategies, making it a must-read for those in the insurance and risk management sectors.

Date: 2025
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Published in Springer. Handbook of Insurance, Springer Books, pp.417-446, 2025

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05310599

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