arXiv:2604.19755v2 Announce Type: replace-cross Abstract: Anti-money laundering (AML) transaction monitoring generates large volumes of alerts that must be rapidly triaged by investigators under strict audit and governance constraints. While large language models (LLMs) can summarize heterogeneous evidence and draft rationales, unconstrained generation is risky in regulated workflows due to hallucinations, weak provenance, and explanations that are not faithful to the underlying decision. We propose an explainable AML triage framework that treats triage as an evidence-constrained decision proc
The increasing maturity and adoption of large language models (LLMs) are pushing their integration into highly regulated and sensitive fields like anti-money laundering, necessitating robust explainability and assurance frameworks.
This development addresses a critical barrier to LLM deployment in regulated industries, enabling automation of complex, document-heavy workflows while maintaining auditability and mitigating risks of hallucination.
The ability to use LLMs with 'evidence-constrained' generation fundamentally changes how financial institutions can approach AML triage processes, moving towards automated rationales rather than just summaries.
- · Financial Institutions (esp. compliance departments)
- · AI/ML Platform Providers
- · Regtech Companies
- · LLM Developers (focused on enterprise safety)
- · Traditional AML Software Providers (without LLM integration)
- · Manual AML Investigation Teams
Financial institutions can process AML alerts significantly faster and with greater consistency, reducing operational costs.
This improved efficiency and explainability could lead to an increase in detected financial crimes, enhancing regulatory effectiveness.
The success in AML may accelerate LLM adoption in other regulated sectors like healthcare and law, standardizing frameworks for trustworthy AI deployments.
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Read at arXiv cs.LG