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.
Financial institutions 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 financial institutions often benchmark themselves against their competitors, they are increasingly interested in seeing how these technologies are performing, and 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 FIs that may be building this technology stack for the first time. Though it's 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 financial institutions must create a forward-looking strategy to discern the correct integration strategies and build an efficient road map.
FIs 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 FIs 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. FIs 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 organization, 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.
