Moving from Baseline Compliance to Efficiency in Financial Crimes

BY Global Econ

Before the recent Binance enforcement action that resulted in a $4.3B fine, the industry was on pace for one of the lowest totals of Financial Crimes Compliance (FCC) related fines paid in recent memory. Before the Binance event, only $700M of fines had been assessed globally across the banking industry. Global FCC fines peaked at over $14B in 2019 and averaged over $11B per year for the five years of 2016 through 2020, then averaged a much lower $3.3B per year for the last three years, including 2023.

Does the lower fines suggest that the industry is doing better and that most financial institutions understand their role in combatting money laundering and terrorist financing?

Or are the biggest cases over, and the newer fines are much smaller?

The answer is likely that the significant investments in AML capabilities at both the institutional and cross-industry levels have improved the industry’s ability to achieve and demonstrate compliance with financial crime regulations.

The challenge across the industry now is how to continue these high levels of compliance while reducing costs and keeping up with the changes in the behavior of bad actors.  According to a recent study, the global financial services industry’s total cost of financial crime compliance is $206B per year. The primary drivers of these costs are:

  • The increasing cost of acquiring and retaining expert staff,
  • The cost of the underlying technology deployed to achieve compliance and integrate those technologies into the increasingly complex digital product and channel configurations and
  • The increased volume of activity as bad actors continue to exploit digital capabilities to achieve their goals.

One technology that has seen a significant increase in its application in the AML space is using machine learning algorithms to increase the accuracy of monitoring systems to detect adverse events. A recent study suggested that AML platforms that leverage machine learning outperform more traditional rules-based systems by 28%. That same study also demonstrated a 67% reduction in manual work by deploying AI and machine learning. Most of the benefits these technologies create come from reducing false positives.

The potential of using advanced AI and machine learning to improve the efficiency of FCC processes is clear. The challenges for financial services institutions is how to leverage these capabilities effectively, including:

  1. Cost – these solutions’ licensing and compute costs can be prohibitive.
  2. Integration – Solutions can be difficult to integrate and introduce new security vulnerabilities.
  3. Regulatory Approval – While many regulators generally support innovation, their knowledge and understanding of these capabilities are limited.                               

As the threat landscape from financial crime continues to evolve, financial institutions must innovate to maintain effective compliance while operating profitably.

Global Econ

Global Economics Group brings together world-class thought leaders, highly experienced experts who have presented before courts and regulatory bodies worldwide, ex-industry executives with deep practical experience and a multi-disciplinary staff including econometricians and finance economists.

You May Also Like

Share This