Reported by Scott Hamilton

(Summary version featured below)

A recent survey conducted by Celent and Hawk has revealed a growing trend of collaboration between financial institutions’ fraud and anti-money laundering (AML) teams. About 80% of institutions now work together in these areas, with over half sharing systems to some extent, and all using artificial intelligence (AI) in various applications. This convergence is being driven by the need to reduce total cost of ownership, improve return on investment, and gain a holistic view of threats through shared data. The survey covered 30 U.S. mid-market banks, credit unions, and neobanks, mostly with assets between $1-20 billion, and emphasized that while technology plays a role, success starts with cross-functional collaboration and better internal communication.

The shift toward integration is significant, given that many institutions have historically siloed their AML and fraud operations. Hawk’s CEO, Tobias Schweiger, noted that starting with technology is a common misstep—true progress begins with aligning people and processes. Previously, even with advanced systems for customer due diligence and transaction monitoring, there was limited data sharing between fraud and AML departments. The 2025 survey results suggest this is finally changing, with institutions recognizing that proactive collaboration strengthens their ability to detect and respond to financial crimes more effectively.

While the term “FRAML” (fraud + AML) is unpopular due to its oversimplification, the concept behind it is gaining traction. Combining KYC, EDD, transaction monitoring, and case management allows for greater information flow and operational efficiency. AI and machine learning tools are enhancing institutions’ capabilities, especially in identifying anomalies, reducing false positives, and performing predictive analysis. Survey respondents highlighted compliance costs, emerging financial crimes, and regulatory pressures as top concerns—but also emphasized the need to improve customer experience and adapt to digital financial services.

The biggest challenges faced by smaller institutions are staffing shortages and high rates of false positives, which consume significant compliance resources. AI has become critical in addressing these issues by increasing accuracy and automating tedious tasks. Financial incentives for consolidating AML and fraud efforts are considerable—many institutions report saving millions annually through integration. However, cultural and operational barriers remain, and Schweiger advocates for beginning with internal conversations to align teams before implementing technology solutions. Pilot programs and collaborative planning can uncover meaningful efficiencies, while ongoing disconnection between fraud and AML teams risks leaving institutions vulnerable to increasingly sophisticated threats.

Read full report: https://www.finextra.com/the-long-read/1357/how-aml-and-fraud-teams-are-collaborating-to-lower-risk-and-fight-financial-crime