Embedding explainable AI into financial compliance education: Building capacity for cross-border transaction oversight

The global financial system faces an escalating threat from sophisticated money laundering and illicit finance, facilitated by high-volume, cross-border transactions. Traditional compliance training, reliant on static case studies and rule memorisation, is critically inadequate for preparing professionals to oversee advanced, AI-driven surveillance systems, whose opaque “black box” nature erodes trust and accountability. The study aims to demonstrate how Explainable AI (XAI) can be systematically integrated into professional education to build essential human capacity for the effective oversight of algorithmic compliance tools. The proposed method involves the development of an XAI-infused learning architecture. This model incorporates a dynamic scenario engine, generating complex, real-world transaction simulations, and an XAI interrogation interface that allows trainees to actively query AI decisions using techniques like SHAP and counterfactual explanations. This is operationalised through the D.R.I.V.E.N. methodology, “Define, Review, Interrogate, Validate, Evaluate, Navigate”, a structured framework for critical engagement. The application of this model, illustrated through a Hawala network case study, shows a transformation in learning outcomes. Trainees evolve from passive recipients of alerts into active analysts capable of deconstructing AI reasoning. This fosters a critical, interrogative mindset, strengthening decision-making justification and embedding human oversight within the operational workflow. The integration of XAI into compliance education is an urgent strategic imperative. It is a necessary evolution to create a resilient human-machine partnership, mitigating algorithmic bias and enhancing the integrity of financial systems. Despite implementation challenges, this approach is vital for building a workforce capable of governing the future of automated finance.