Knowledge Assessment Lab

Abdelrahim Al Aqqad

Chapter Chapter 16 in Fraud Analytics in Action, 2026, pp 411-418 from Springer

Abstract: Abstract Chapter 16 serves as the capstone of Fraud Analytics in Action, translating the theoretical and technical foundations developed throughout the book into a comprehensive, hands-on fraud detection project. Centered on the domain of insurance fraud—a financially significant and analytically complex problem—this chapter guides readers through an eight-stage machine learning workflow encompassing data cleaning, exploratory data analysis, feature engineering, model training, evaluation, and interpretability. Two interconnected lab projects anchor the chapter. The first employs gradient boosting and deep learning models alongside interpretability tools—LIME and SHAP—to detect fraudulent insurance claims and build stakeholder trust through transparent predictions. The second leverages AutoGluon's automated machine learning capabilities to train and evaluate models on a 1,000-record insurance claims dataset with 41 features, addressing the inherent class imbalance typical of fraud detection tasks. Results demonstrate that the weighted ensemble model achieved a test accuracy of 84%, with a recall of 75.51%, precision of 64.91%, and an F1 score of 0.698. The chapter concludes by emphasizing that fraud detection is a continuous, adaptive discipline requiring ongoing model refinement, feature engineering, and collaboration between data scientists and domain experts.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16023-2_16

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DOI: 10.1007/978-3-032-16023-2_16

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