Implementation strategies for artificial intelligence in banking compliance monitoring systems

Posted: Feb 26, 2022

• Dr. Prof. Daniel Rossi
• Dr. Prof. David Ivanova
• Dr. Prof. David M¨ uller

Abstract

The integration of artificial intelligence into banking compliance monitoring represents one of the most significant technological transformations in the financial sector. Traditional compliance systems, predominantly rule-based and manual in nature, have become increasingly inadequate in the face of sophisticated financial crimes, rapidly evolving regulatory requirements, and the exponential growth of digital financial transactions. Current systems typically generate false positive rates exceeding ninety percent, creating substantial operational burdens and diverting valuable resources from genuine compliance risks. The challenge extends beyond mere technological implementation to encompass regulatory acceptance, system interpretability, and integration with legacy banking infrastructure. This research addresses the critical gap between theoretical AI capabilities and practical deployment in highly regulated banking environments. While numerous studies have explored AI applications in financial services, few have systematically addressed the implementation challenges specific to compliance monitoring systems. Our work introduces a novel framework that combines quantum-inspired optimization with explainable AI techniques specifically tailored for compliance applications. The approach recognizes that successful AI implementation in banking compliance requires not only technological innovation but also strategic consideration of regulatory constraints, organizational readiness, and operational workflows. Our research questions focus on three key areas: how to design AI systems that maintain regulatory compliance while improving detection accuracy, what implementation strategies effectively bridge the gap between technical capabilities and operational requirements, and how to measure the success of AI integration in compliance monitoring. These questions address fundamental challenges that have limited the widespread adoption of AI in banking compliance despite its theoretical potential. The significance of this research lies in its practical orientation and comprehensive approach. By developing and testing implementation strategies across multiple financial institutions, we provide actionable insights for banks seeking to leverage AI for compliance enhancement.