ADAPTIVE MANAGEMENT IN MODERN BANKS TO PREVENT CROSS-BORDER MONEY LAUNDERING

Abstract

Introduction: traditional banking control mechanisms are highly vulnerable to complex global money laundering networks, necessitating a transition toward adaptive risk management. Purpose. To develop a conceptual model of an adaptive transaction risk management ecosystem capable of preemptively detecting transnational illicit financial flows. Methods. Network graph analysis, predictive machine learning, systematisation, grouping. Results. The proposed architecture merges internal banking analytics with macro-level RegTech. By utilizing topological graph analysis and explainable artificial intelligence, it uncovers hidden transactional pathways, ensures transparent risk prioritization, and continuously retrains algorithms in real time. Conclusion. Transitioning to a dynamic, predictive compliance framework enables banks to proactively block the integration of illicit proceeds. Future research will focus on the empirical validation of the model using large-scale interbank datasets.