Banking on collaboration: How to overcome the stalemate in AML innovation

Money laundering activities in the United States account for an estimated $300 billion annually alone. Over the past decade, U.S. financial institutions have incurred substantial penalties for anti-money laundering (AML) violations. For instance, in 2023 alone, penalties exceeded $5 billion, marking a 69% increase from the previous year, highlighting the need for more effective approaches.

Yet, AML programs are often tethered to outdated, compliance-driven methods that prioritize ensuring compliance over meaningful risk mitigation. The cost? Missed opportunities to identify and disrupt money laundering and related crimes proactively.

And this isn’t just one bank’s problem—it’s an industry-wide challenge. That’s why groups like Wolfsberg are pushing for change. They want banks to move beyond compliance toward measurable results: finding more criminals while reducing operational overheads.

But achieving this requires a new approach. In this post, we’ll cover the main barriers stalling AML innovation, best practices for change, and showcase how collaborative, technology-driven solutions can unlock a more effective and secure future for better AML strategies.

The 3 biggest barriers to innovation in AML

Even as money laundering cases surge, significant barriers prevent banks from innovating their AML programs. Here’s what’s holding the industry back.

#1. Structural challenges

*️⃣Data fragmentation: Banks are fighting financial crime with one hand tied behind their back. Their systems work in isolation from each other, making it impossible to spot criminal patterns that span multiple institutions. The Wolfsberg Group puts it plainly: ‘without connecting these dots, sophisticated criminals will keep slipping through’.

*️⃣Legacy systems: Most banks are stuck with outdated technology that can’t keep up with modern threats. These systems resist integration with AI-driven tools and collaboration platforms. The result? Teams waste time on manual processes while delivering subpar results.

#2. Privacy and security concerns

Banks face a frustrating dilemma: sharing intelligence could transform how we fight financial crime, but privacy regulations make collaboration risky. Even when institutions want to work together, requirements like GDPR create serious compliance and reputational concerns. Without secure ways to share data, crucial insights remain trapped in silos.

#3. Regulatory and incentive gaps

*️⃣Lack of innovation incentives: The current regulatory landscape rewards compliance over effectiveness. Banks get little credit for adopting advanced approaches that catch more criminals. Instead, they focus on regulatory compliance, leaving little room for innovation.

*️⃣Missing feedback loops: Banks submit thousands of SARs without knowing if they’re making a difference. This lack of feedback, as the Wolfsberg Group notes, leaves institutions in the dark about their AML effectiveness—and unsure where to focus their efforts.

These barriers stand between banks and truly effective AML programs that go beyond compliance. Let’s look at how secure collaboration and modern approaches can help overcome them.

Best practices: How to make your AML program more effective

While the barriers are significant, banks can overcome them with the right approach. Here’s what works.

1. Focus on effectiveness

The Wolfsberg Group is clear: banks must move beyond basic compliance towards measurable outcomes. This means:

💡Allocating resources to higher-risk activities and customers, not treating all risks equally.

💡Focusing on meaningful outcomes, such as identifying and disrupting criminal networks, instead of generating excessive alerts.

💡Designing AML programs that align with national and global priorities.

💡Creating synergy between institutional efforts and broader regulatory goals.

When banks prioritize effectiveness, AML programs become strategic assets rather than operational burdens.

2. Build flexible systems

One size doesn’t fit all in AML. Banks need systems that adapt to their unique risk profiles and evolving threats. As the Wolfsberg Group highlights, tailored frameworks enable banks to scale their efforts in line with their size, customer base, and geographic footprint.

This means:

💡Implementing risk assessment processes that evolve with emerging threats.

💡Avoiding rigid, one-size-fits-all solutions that stifle innovation and fail to address institution-specific challenges.

💡Establishing clear protocols for integrating new technologies without disrupting operations.

The result? Banks stay agile as financial crime tactics constantly change.

3. Use advanced technology

To fight financial crime, external parties have developed AML solutions. For instance, Federated Learning allows banks to gain a fuller picture of illicit activity across banking – without exposing sensitive customer data or compromising compliance. Key benefits include:

✅Privacy preservation: Banks enhance risk detection without sharing raw data.

✅Enhanced collaboration: Institutions work together to spot complex cross-border patterns.

✅Scalable solutions: Even smaller banks can access cutting-edge technology without massive infrastructure investments.

With Consilient’s Federated Learning solution, banks build secure frameworks that drive better AML outcomes–all while maintaining compliance. And by working together, banks can get ahead of financial criminals.

Keen to learn more? 📕Read: Beyond the 1%: How AI Federated Learning is catching more financial criminals

4. Build feedback mechanisms

Banks need to know what works. Regulators often collect vast amounts of data, such as Suspicious Activity Reports (SARs), but rarely provide institutions with actionable feedback on their quality or impact.

Here’s how to improve:

💡Develop mechanisms for evaluating and communicating the effectiveness of SARs and other AML activities.

💡Share insights about successful case outcomes to improve detection methods and resource allocation.

💡Build an ongoing dialogue between regulators and institutions to align on priorities and expectations.

When institutions share insights and learn from each other, they strengthen the entire industry’s defense against financial crime.

These four practices are key to overcoming AML’s biggest challenges. Let’s see how banks are putting them into action.

Collaboration in action: Real results

The future of AML lies in collaboration. It’s a bold statement but it’s backed by real-world examples. Here’s how it’s working:

The vision: Shared intelligence systems

The Wolfsberg Group emphasizes that shared intelligence systems are crucial for effective AML. When banks work together to analyze suspicious activity patterns, they catch more criminals. But, this collaboration needs to balance data sharing with privacy protection.

Consilient’s solution: Federated Learning

We are the only firm that has solved this challenge. How? With Federated Learning. Federated learning creates secure frameworks for collaboration, allowing banks to:

1. Enhanced risk detection through secure collaboration
By using federated learning, Consilient enables multiple financial institutions to train AI models on decentralized data–without ever sharing sensitive customer information. This means:

✅Improved detection through analysis of broader patterns across institutions.

✅Fewer false positives, letting teams focus on truly high-risk cases.

2. Measurable efficiency gains
Institutions that work with us have reported significant gains in operational efficiency, like:

✅Time savings: AML teams have streamlined their processes, cutting down on manual reviews and redundant alerts.

✅Cost reduction: Reduced false positives and enhanced detection accuracy have lowered the cost of compliance by minimizing wasted resources.

Here’s an example:

Real results from a real bank

We recently worked with a bank to tackle cross-border money laundering. Using federated learning achieved:

🟣A 300% improvement in AML detection rates, driven by the ability to analyze shared patterns of suspicious activity.

🟣A 80% reduction in false positives, saving thousands of hours for AML teams and reallocating resources to higher-priority risks.

🟣Enhanced compliance with international regulations, improving relationships with regulators and reducing the risk of penalties.

The bottom line: By leveraging shared intelligence, banks can work together to build a stronger defence against financial crime.

A new era for AML

It’s fair to say that the old way of working—siloed systems and basic compliance—can’t keep up. Banks need a new approach that brings together institutions, regulators, and technology providers.

The financial services sector has an opportunity to turn AML programs into proactive, intelligence-driven systems that protect against evolving risks while meeting regulatory expectations. Secure collaboration is the key. With technologies like federated learning, banks can now share intelligence while protecting sensitive data. 

Consilient makes this possible. By leveraging secure technologies like federated learning, institutions can break down the barriers of data fragmentation and privacy concerns, enabling a collective fight against financial crime.Ready to strengthen your AML program? We’d love to hear from you. Contact us today and we will talk you through how Federated Learning can help with AML while reducing costs.