FRAML Webinar: 5 Key Takeaways on Fraud & AML Convergence

When a bank's customer loses thousands of dollars to a scammer, the fraud team needs to know about it. When those stolen funds make their way through multiple accounts, the AML team needs to be able to detect it. And when a SAR is filed, it's the AML team that files it.

It's clear to see: there's much to gain if the fraud and AML teams can work together.

This was the main conclusion from our webinar with ACAMS on the subject of fraud and AML convergence within U.S. banks. The session was hosted by Shana Leyva, Hawk's Head of Marketing, Americas, and Bel Perez, our Senior Product Owner for Fraud & FRAML. Guest speakers were Susan R. Shaffer, VP and BSA Officer at CBI Bank and Trust, and Neil Katkov, Director of Risk at Celent.

Here are the 5 key discussion points and takeaways.

1) What is fraud and AML convergence?

• The need for FRAML convergence is due to a growing need for efficiency, effectiveness, and technological alignment in combating financial crime
• Instead of separate fraud and AML investigations, FRAML enables faster, coordinated investigations through shared systems and processes
• That said, the industry is moving toward "Financial Crime Risk Management" (FCRM) as the preferred term
• FCRM covers a broader umbrella including human trafficking, sanctions, corruption, and cyber-enabled crimes - not just fraud and money laundering

2) What change is fraud and AML convergence driving?

• Combined fraud and AML teams can identify complex criminal typologies that span both domains more effectively
• Customer information, transaction data, and device intelligence can be combined into a single comprehensive view instead of sitting in siloed systems
• A unified system gives the AML and fraud teams access to the same comprehensive customer intelligence and risk profiles
• Unified systems free analysts from requesting information from the other department or piecing together fragmented data themselves
• There’s a mindset change from looking at fraud solely as monetary losses to considering the broader impact on customers and society
• Financial crime professionals aren't just ticking compliance boxes; they want to protect people's family members from scams and stop human trafficking.

3) What are the challenges with converging fraud and AML?

• Merging real-time fraud prevention systems with batch-oriented AML processes creates fundamental architectural challenges
• Teams specialized in either fraud or AML may lack cross-functional knowledge, making it challenging to operate converged systems effectively
• Consolidating existing systems often requires investment and long-term planning
• Consistent methodologies must be developed for how shared data should be processed and analysed across both fraud and AML use cases
• Defining clear handoff points and determining when cases should transition from AML teams to fraud teams or vice versa is important
• Creating effective shared rules and AI models for both fraud and AML is complex – as typologies evolve, models must be continuously updated

4) What are the benefits of fraud and AML convergence?

• Breaking down siloes allows fraud and AML teams to share valuable insights that were previously trapped in separate departments
• Fewer systems prevent the same suspicious activity from triggering multiple alerts across different teams, reducing duplicate investigations
• Consolidated insights on customers means fraud and AML teams consistently have the right information to make informed decisions
• Teams now use existing personnel more efficiently and avoid scrambling to hire twenty highly trained analysts overnight
• Institutions redirect resources toward more strategic, high-value compliance activities by gaining efficiencies from convergence
• Teams now spending less on system maintenance by consolidating infrastructure

5) How to get buy-in for fraud and AML convergence?

• Build a complete business case. Don't just show cost savings. Prove you'll catch more criminals and strengthen regulatory compliance
• Learn from other banks. Talk to institutions that have already made the transition. Their experience accelerates your adoption
• Start small with pilot programs. Test FRAML with a specific use case instead of trying to transform everything at once. Quick wins build momentum

Where is AI helping with fraud and AML convergence?

• Machine learning reduces false positives by incorporating analyst decisions into AI models, creating a feedback loop that improves detection accuracy over time
• AI helps identify specific behavior patterns that indicate the fraud-money laundering nexus that fraud and AML teams struggle to detect when siloed
• While regulators resist fully automated decisioning, AI provides detailed recommendations with supporting analysis, allowing human analysts to make informed decisions with the required oversight and accountability

Our thanks to Susan and Neil for their insights.

The webinar included data from Hawk's recent report with Celent, Trends in Fraud & AML Convergence at U.S. Mid-market Banks and Credit Unions. Download your complimentary copy today.

Alternatively, please contact Hawk for a demo of our holistic solution for AML and fraud.

July 17, 2025