Imagine a Smarter, Risk-Based Approach to Financial Crime and Compliance Management
It’s easy to understand how financial services firms can feel like they’re losing ground in their efforts to defend against financial crime. Schemes are increasingly sophisticated, and, while detection solutions have advanced, they often result in a flood of false positives and still require significant manual intervention. The result: financial crime and compliance management (FCCM) programs that are inefficient, expensive, and largely unsustainable as threats and requirements grow.
Among the most challenging aspects of instituting effective anti-money laundering (PDF) and anti-fraud programs at financial institutions is the need to adapt quickly to changing patterns of financial crime to limit both financial and reputational risks. Institutions must work unceasingly to create more accurate models to catch ever-more sophisticated illegal activity, reduce the cost of investigating false positives, and protect client relationships.
As a first line of defense, many financial institutions rely almost exclusively on a series of rules-based solutions, which cannot keep pace with the highly dynamic nature of financial crime today; nor can they deliver the immediate insight and predictive intelligence that can enable firms to actually stay one step ahead of the criminals instead of paces behind. In addition, siloed legacy solutions prevent a best-practice enterprise-wide approach to financial crime detection that significantly reduces risk.
With more innovative products and data sources than ever, the ability to continually discover emerging risks and new criminal patterns, coupled with the capacity to rapidly operationalize newly developed models into production, is a necessary requirement for modern financial crime platforms. In this environment, firms look to embrace graph analytics and machine learning to drive a smarter, risked-based approach to FCCM.