Event-Driven Compliance Systems: Modernizing Financial Crime Detection Without Machine Intelligence

Abstract

Internal compliance processes for financial crime detection within banking and financial services organizations have, until now, relied heavily upon machine intelligence-oriented methods. While a significant number of suspicious matter reports are generated, the proportion of actionable matters from those reports is small and the reduction of false positives remains a challenge. Late-stage intervention on large and complex data classes is expensive and banks and governments recognize that substantial modernization is needed.

An alternative approach, rooted in event-driven architecture, enables a different form of compliance system that, while not using machine intelligence or analytics to drive detection, holds the potential for greater effectiveness, efficiency and reduced risk. Such a system is positioned as a minor enhancement to existing systems rather than a wholesale replacement. By incorporating high-frequency and low-cost detection strategies, real-time capabilities, triage workflows, governance and risk management, and compliance considerations that address the limitations of the event-driven system, tangible benefits can be sought.