Dynamic Trust Modulation and Human Oversight in AI-Driven AML Systems: A Conceptual Framework for Compliance

This literature review investigates how human trust, decision fatigue, explainability (XAI), and human oversight interrelate to influence analyst decision-making in AI-driven anti-money laundering (AML) systems. While prior research has predominantly emphasized algorithmic performance, detection accuracy, or regulatory compliance in isolation, a critical gap remains in understanding the human-centered dynamics that shape real-world operational outcomes. Addressing this gap, the review examines how financial institutions navigate compliance demands and operational constraints, drawing on the Australian regulatory environment as an illustrative governance reference, including expectations articulated by AUSTRAC. Building on this synthesis, the study identifies structural gaps in Trust Calibration and oversight practices. It introduces a Dynamic Trust Modulation (DTM) framework to conceptualize how trust evolves across AML workflows. The framework models trust as a fluid, context-dependent construct shaped by system behavior, analyst workload, explainability mechanisms, and regulatory pressure. By framing trust, explainability, and decision fatigue as interdependent components of human–AI collaboration, this review advances a more holistic perspective on socio-technical system design in financial crime detection. The proposed framework contributes theoretically by extending human–AI trust research into the AML domain and practically by offering actionable design principles to enhance system accountability, decision defensibility, and adaptive compliance in operational AML environments.

@article{Diaz2025,
  title     = {Dynamic Trust Modulation and Human Oversight in AI-Driven AML Systems: A Conceptual Framework for Compliance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Julian Diaz and Abeer Alsadoon and Oday D. Jerew and Ahmed Hamza Osman and Hani Moetque Aljahdali and Albaraa Abuobieda and Abubakar Elsafi},
  doi       = {10.14569/IJACSA.2025.0161209},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161209}
}