Techniques and Approaches in Fraud Analytics

Abdelrahim Al Aqqad

Chapter Chapter 5 in Fraud Analytics in Action, 2026, pp 63-109 from Springer

Abstract: Abstract This chapter provides a comprehensive road map of the techniques and approaches used in fraud analytics. It begins with expert-based, rule-based systems that rely on domain knowledge and fraud investigators' expertise, using real-world examples such as Uber's fraud detection framework. The chapter then transitions to data-driven and machine learning approaches—including supervised learning, unsupervised learning, and deep learning—examining how these methods identify both known and emerging fraud patterns. Explainable AI (XAI) techniques such as LIME, SHAP, partial dependence plots, and decision tree surrogates are explored as tools for making complex models interpretable and trustworthy. The chapter also addresses hybrid approaches that combine rule-based and ML methods, and concludes with specialized fraud analytics techniques including anomaly detection, social network analysis, text analytics, and artificial neural networks. Ethical considerations, model deployment best practices, and key performance trade-offs are discussed throughout.

Date: 2026
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DOI: 10.1007/978-3-032-16023-2_5

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