Why Mid-Sized Financial Institutions Find Success by Combining Fraud and AML Tactics

Fraudsters today are more organized than ever. They don’t just commit fraud; they launder the proceeds through sophisticated, multi-layered networks that span borders, payment channels, and financial institutions. For banks and credit unions—especially mid-sized players with leaner budgets and fewer resources—the challenge is clear: fighting fraud and money laundering in silos is no longer sustainable.

Recent research underscores this shift. Over half of mid-sized U.S. banks have already converged fraud and anti-money laundering (AML) functions, with another 40% taking steps toward integration. Those that have fully converged report annual savings of up to $5 million, alongside faster investigations, fewer false positives, and improved detection accuracy.

This move—often referred to as FRAML (Fraud + AML convergence)—isn’t just a tactical budget decision. It represents a strategic reimagining of how financial institutions manage financial crime risk in a hyper-digital economy.

The Case for Convergence

Fraud and AML teams have historically reviewed the same customer behaviours through different lenses:

• Fraud teams focus on immediate risk at the transaction level (e.g., is this payment genuine, or is it a scam?).

• AML teams focus on longer-term suspicious activity (e.g., does this account show signs of layering or structuring?).

When these teams operate separately, duplication is inevitable. Analysts investigate the same customer twice, detection rules overlap, and critical warning signs can fall through the cracks. Convergence solves this by combining operational processes, case management, and supporting technology.

Key benefits include:

• Reduced false positives. Unified rules and AI-driven insights lower noise and free investigators’ time.

• Streamlined case management. Analysts can view risk holistically, eliminating redundant investigations.

• Improved detection accuracy. By merging perspectives, institutions reduce the blind spots that criminals exploit.

• Lower costs. With fewer duplicative systems and less manual work, total cost of ownership drops significantly.

Survey data shows that nearly two-thirds of mid-sized banks have taken concrete steps toward convergence. Two-thirds cited operational efficiency as their primary gain, while more than half pointed to lower total cost of ownership.

Why Mid-Sized Institutions Are Leading the Way

While large global banks may have the budgets to run siloed fraud and AML programs in parallel, mid-sized banks and credit unions face sharper resource constraints. That makes them more willing to rethink their approach.

Three factors explain why mid-sized players are at the forefront:

1. Agility. With smaller organizations, decision-making is often faster and less bureaucratic, making change initiatives easier to implement.

2. Cost pressure. Rising scam volumes, regulatory obligations, and limited budgets force efficiency. Convergence provides a clear ROI.

3. Technology refresh. Many mid-sized banks are upgrading legacy systems, and convergence offers a chance to leapfrog outdated silos with integrated platforms.

Instead of being followers, mid-sized institutions are increasingly setting the pace for the industry, showing that convergence is not just possible but profitable.

Momentum Across Asia-Pacific

Although the research highlights U.S. institutions, the same trends are gaining traction across the Asia-Pacific (APAC) region:

• In Singapore, the Monetary Authority of Singapore (MAS) has strongly encouraged collaborative and converged approaches through the AML/CFT Industry Partnership (ACIP). Scam warnings and mule account crackdowns are driving banks to integrate fraud and AML signals into unified frameworks.

• Australian banks and credit unions face rising scam losses and tighter oversight from AUSTRAC. Converged solutions are increasingly being positioned as the most cost-effective way to comply with both fraud prevention expectations and AML obligations simultaneously.

• In Hong Kong, regulators have emphasised real-time scam detection and intervention, which requires fraud and AML teams to coordinate more closely. Without convergence, real-time detection of mule activity and layering risks is almost impossible.

• In emerging markets like the Philippines, Vietnam, and Indonesia, where digital payments adoption is booming, mid-sized banks and e-wallet providers are seizing the opportunity to build converged FRAML frameworks from scratch, avoiding the silos that older institutions now struggle to unwind.

The APAC story is clear: convergence is not just about efficiency, it’s about resilience in diverse and fast-growing markets.

Unlocking AI’s True Potential

Artificial intelligence is often seen as the answer to financial crime detection. But the truth is that AI is only as good as the data it learns from.

When fraud and AML systems are siloed, AI models see incomplete pictures: fraud systems may detect scam transfers without recognizing the laundering pattern, while AML systems may flag suspicious deposits without knowing about an upstream phishing attempt.

By unifying fraud and AML data, institutions unlock AI’s full value:

• Mule account identification. AI can link fraud signals (e.g., sudden influx of small transfers) with AML red flags (e.g., unusual outgoing remittance flows).

• Improved scam response. AI can recognize that a “legitimate customer transfer” is actually a scam victim being socially engineered.

• Smarter investigations. Case data becomes richer and more contextual, enabling faster decisions and reducing analyst fatigue.

Convergence transforms AI from a buzzword into a business advantage.

Barriers That Remain

Despite the clear benefits, convergence is not without challenges:

• Leadership buy-in. Over 80% of banks cited executive alignment as their biggest hurdle. Without senior sponsorship, convergence can stall.

• Proving ROI quickly. While the long-term savings are substantial, many struggle to demonstrate immediate financial benefits to boards.

• Data silos. For nearly one-third of banks, fragmented legacy systems remain the most stubborn obstacle.

• Misconceptions. Convergence is not about bolting fraud and AML systems together. It requires a shared strategy, unified data infrastructure, and technology that balances real-time fraud detection with long-term AML monitoring.

Institutions that succeed tend to start with specific convergence use cases—such as mule detection or scam response—before expanding to broader integration. This phased approach builds confidence and momentum.

Looking Ahead

For mid-sized banks and credit unions, convergence is no longer an optional experiment. It is already happening, and the institutions that move early are reaping significant financial and operational rewards.

In APAC, convergence represents more than just efficiency. It’s an opportunity to leapfrog legacy systems, align with tightening regulatory expectations, and build resilient defenses against criminals who already exploit the overlap between fraud and money laundering.

Ultimately, convergence is not just about cost savings. It’s about future-proofing institutions with smarter, more connected defenses. As criminals continue to innovate, the banks that fight back successfully will be those who abandon silos and embrace integrated, intelligence-driven approaches.

For senior leaders, the choice is clear:

• Wait and follow, or

• Act now and lead.

Those who commit to convergence today will define what the next generation of financial crime programs looks like—and stand out as the institutions that not only survived, but thrived, in an era of relentless financial crime innovation.

Would you like me to also create a one-page infographic outline (showing key stats: $5M savings, 60% adoption, 77% ROI in five years, etc.) that could be used as a visual companion for this blog on LinkedIn or presentations?