Change Control and Human Oversight in Governed LLM-Assisted AML Triage: Validation Flows, Metrics, and Supervisory Readiness

Abstract

The paper presents a study of change control and human oversight mechanisms for governed, non-authoritative large language model (LLM) assistance in anti-money laundering (AML) case triage. While operational monitoring frameworks enable institutions to observe production behavior, sustained regulatory defensibility additionally requires structured validation flows and controlled change management that bind model, template, and validator evolution to measurable evidence and explicit human accountability. The study proposes a governance-oriented validation lifecycle that integrates pre-deployment checks, post-deployment surveillance, and supervisory escalation paths, with human-in-the-loop controls formally embedded into each phase. Quantitative metrics are introduced to evaluate validation stability, override behavior, and post-change risk exposure. A BPMN-based process model is presented to illustrate how governed change flows support supervisory readiness in the face of regulatory scrutiny. The purpose of the work is to determine how structured change control and human oversight can be operationalized for LLM-assisted AML triage through measurable validation metrics and governed workflows, and to assess how these mechanisms contribute to sustained audit readiness. The methodology encompasses: formalizing validation stages across the LLM lifecycle, defining quantitative indicators for change impact and human intervention, modeling governed change workflows using BPMN, and analyzing their suitability for supervisory review and quality assurance. The scientific novelty. For the first time, change control and human oversight for LLM-assisted AML triage are formalized as an integrated validation lifecycle with explicit metrics for override pressure, post-change deviation, and human acceptance behavior, enabling auditable evidence of sustained governance beyond initial deployment. The practical value lies in providing compliance, QA, and engineering teams with a defensible framework for managing LLM changes in production, thereby reducing uncontrolled drift, mitigating automation bias, and strengthening institutional readiness for regulatory inspections. Conclusions. Effective governance of LLM assistance in AML triage depends not only on monitoring outputs but also on controlled validation and change processes that explicitly integrate human judgment. The results demonstrate that structured change control combined with quantitative oversight metrics significantly improves supervisory defensibility compared to ad hoc or informal update practices.