arXiv:2604.17420v2 Announce Type: replace Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled by the lack of realistic benchmarks. Existing transaction-graph datasets suffer from two pervasive limitations: (i) they provide sparse node-level semantics beyond anonymized identifiers, and (ii) they rely on template-driven anomaly injection, which biases benchmarks toward static structural motifs and yields overly optimistic assessments of model robustness. We pro
The increasing sophistication of financial crime coupled with the limitations of existing ML benchmarks is driving the need for more realistic evaluation tools in anti-money laundering.
Improved benchmarks for anti-money laundering (AML) AI systems could significantly enhance the efficacy of financial crime detection, impacting global financial stability and regulatory compliance.
The introduction of a high-fidelity graph benchmark allows for more accurate and robust development of AI models for transaction monitoring, moving beyond previous optimistic biases.
- · Financial institutions
- · AI/ML developers (fraud detection)
- · Regulatory bodies
- · Law enforcement
- · Money launderers
- · Criminal organizations
More effective AI-driven transaction monitoring systems are developed and deployed across financial institutions.
A reduction in illicit financial flows and increased transparency within the global financial system.
Enhanced trust in digital financial systems and reduced operational costs for compliance departments.
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Read at arXiv cs.LG