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Evidence record 6906 · automatically gathered

Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks

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 triag

Record details

Published: 22 March 2026
Source: arXiv
Category: Research
Topics: Regulation · Transparency · Finance, VC & PE
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (22 March 2026), “Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks,” evidence record 6906, https://ethics.ai/record/6906 (originally published by arXiv).

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