Fraud–AML Convergence: Integrating Fraud and AML Detection, Shared Typologies, and Unified Case Management.
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P123Keywords:
Fraud–AML Convergence, Anti-Money Laundering (AML), Fraud Detection, Financial Crime Compliance, Financial Crime Risk Management, Integrated Risk Framework, Shared Fraud & AML Typologies, Suspicious Activity Monitoring, Transaction Monitoring SystemsAbstract
Fraud and anti–money laundering (AML) programs have historically evolved as separate control functions, often operating on different data, tools, and investigative workflows. However, converging threat landscapes where scams, synthetic identities, mule networks, account takeovers, and laundering chains intersect are forcing financial institutions to rethink fragmented detection and response models. This paper examines the concept of fraud–AML convergence as an integrated approach to identifying illicit activity across the full customer and transaction lifecycle. It analyzes how shared typologies (e.g., authorized push payment fraud feeding mule accounts, trade-based laundering enabled by invoice manipulation, and crypto-fiat layering via fraudulent onboarding) can be modeled through common risk signals, entity resolution, and network analytics. The study further evaluates architectural and operational options for integrating fraud and AML detection, including unified data layers, feature stores, shared rules-and-ML pipelines, and cross-domain alert prioritization. A central focus is unified case management: consolidating alerts, evidence, and investigator actions into a single workflow that reduces duplication, improves triage accuracy, and strengthens auditability and regulatory reporting. The paper also discusses key implementation challenges governance, explainability, privacy constraints, and model risk management alongside practical success metrics such as reduced false positives, faster time-to-disposition, and improved suspicious activity conversion. By presenting an integrated typology-to-workflow framework, this research offers a roadmap for institutions seeking more resilient, efficient, and intelligence-driven financial crime operations.
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