Why ‘Explainable AI’ Is the Next Frontier in Financial Crime Fighting

By Chad Hetherington

With new technologies like faster payments taking hold, the explosion of readily-available data and the ever-changing regulatory landscape, staying ahead of financial crime and compliance risk has become more complex and expensive than ever before. As these trends show no sign of abating, the compliance operations and monitoring staff of a financial institution often find themselves a major cost center.

Banks must manage compliance budgets without losing sight of primary functions and quality control. To answer this, many have made the move to automating time-intensive, rote tasks like data gathering and sorting through alerts by adopting innovative technologies like AI and machine learning to free up time-strapped analysts for more informed and precise decision-making processes.

As banks often benchmark themselves against their competitors, they are increasingly interested in seeing how these technologies are performing, and they are asking themselves how to leverage artificial intelligence and machine learning to increase insight, reduce false positives and decrease compliance spend.

Understandably, prioritizing decisions around technology spend can be challenging, especially for smaller banks that may be building this technology stack for the first time. Though tempting to dive head-first into the transformational world of emerging technology, when determining their approach to adopting new technologies to fight financial crime and stay compliant, all banks must create a forward-looking strategy to discern the correct integration strategies and build an efficient road map.

Banks are not the only ones thinking critically about how to adopt emerging technologies to address the onslaught of financial crime risks. Recently, a group of U.S. regulators put forth a statement encouraging banks to test new technologies that would improve their anti-money laundering controls. While this gesture was welcomed, the impact of this statement goes deeper than simply an institution making decisions around financial crime prevention and detection technologies.

Institutions that decide to implement AI or machine learning capabilities must consider not just how to approach the system upgrade itself, but also how to communicate the new controls to regulators. Take the example of a machine learning-based system. Banks must be prepared to explain the details of the model, how it works, and to explain the decisions that the approach makes to avoid compliance breaches. Employing an army of data scientists is not enough—though likely highly skilled in technology, having the layer of financial crime domain expertise on top of that is essential in an intricate and highly regulated field.

Smart compliance-focused teams charged with implementing new technologies should consult with a diverse group of financial crime experts, from both inside and outside their organizations, to support how they build out a realistic business road map rooted in data, analytics and the cloud. Additionally, they should address the processes aligned to leaving a detailed audit trail for the regulators—documenting the methodology for how the machine learning system is tested and every step of the decision-making process.

AML and emerging tech

The use cases of emerging technology to fight financial crime are unending, but AML, as evidenced by the U.S. regulators’ statement, is one area that has been prominent. While historically AML compliance has been relatively slow in adopting emerging technologies, recent years have seen a significant shift. As financial institutions deal with a barrage of suspicious activity reports and address the influx of geopolitical sanctions, AML is speeding up technology adoption with a greater emphasis on ROI, not just appeasing the regulators.

Recently, regulators have taken a critical look at AML controls, handing out significant fines to those banks which may be lacking. And, as the financial crime function that is perhaps most customer-facing, fraud prevention is another area that benefits from AI and machine learning, as it continues to be challenged with a host of new risks such as P2P payments, new payment rails and faster payments. Banks are pressed to innovate more quickly than they ever imagined as the challenger fintech firms and next-generation providers attack their market share. This places an ever-increasing burden on financial crime programs to leverage emerging technologies.

Banks and regulators alike are embracing new technologies to fight financial crime, protect customers, and avoid reputational risk. Regulators are beginning to work directly with technology experts to gain a better understanding of the technologies available to fight financial crime today, become experts themselves and find the right balance among emerging technologies, burdensome regulation and cost. This will allow them to find a path forward for both regulators and banks.

As banks continue to become increasingly comfortable with moving many of their financial crime fighting efforts to the cloud, a new paradigm is emerging. Making complex technology easy to use, understand and explain by both banks and regulators is something that the industry must work toward to build and conquer the next frontier in fighting financial time.

But no matter what financial crime road map a bank prepares itself to adopt, having an experienced and diverse team examining the potential as well as the pitfalls of emerging technologies is critical to create a stumble-free, cost-effective integration of today’s latest and greatest technology.

Chad Hetherington is global VP for professional services at NICE Actimize.