aceAn Introduction to Machine Learning Methods for Fraud Detection
Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effectiveness across diverse fraud scenarios. We analyze various fraud types, including credit card fraud, financial statement fraud, insurance fraud, and money laundering, along with their specific detection challenges. The review outlines supervised, unsupervised, and hy…
Review synthesizes evidence that supervised, unsupervised and hybrid machine learning approaches can be applied to detect credit card fraud, financial statement fraud, insurance fraud and money laundering in real-world banking data.
Same machine learning fraud detection systems encounter persistent challenges including data imbalance, concept drift and privacy concerns that complicate implementation in operational financial environments.
Findings are bounded by persistent operational constraints identified in literature, including severe class imbalance, evolving fraud patterns, and privacy restrictions on data sharing.
Evidence
- Peer-reviewedApplied Sciences2025-11-05
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Truvace Impact Record TRV-2026-0477, v1: “An Introduction to Machine Learning Methods for Fraud Detection.” Truvace, 2026-07-22. /record/TRV-2026-0477 (accessed at citation time). sha256 822144ee2e2e85ff…
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