The Compliance Officer’s Guide to AI: Insights from the Wolfsberg Statement

By Yaron Hazan 5 days agoNo Comments
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Let’s take a look at the latest Wolfsberg Group Statement on “Effective Monitoring for Suspicious Activity” 2024 and see what it means for us in the world of anti-money laundering (AML) and countering the financing of terrorism (CFT). Spoiler alert: AI is the game-changer we’ve been waiting for. 

The Updated Statement: What’s New on Effective Monitoring?

First things first, the Wolfsberg Group just released a Statement on “Effective Monitoring for Suspicious Activity” following up on their 2019 Statement on Effectiveness. Why has there been an update? Because the financial crime landscape has changed drastically, and so have the priorities of government agencies along with the implementation of innovative new technologies. The new report emphasizes risk-based due diligence, better technology for detecting financial crimes, and improved public-private partnerships (PPPs). 

Why AI is the Star of the Show

Why are we discussing AI in the context of this financial crime risk management (FCRM) report? Simple. For decades policymakers have been testing AI in a variety of risk management and compliance environments and publishing guidelines for responsible implementations. The report highlights the need for sophisticated detection scenarios, and guess what, AI fits the bill perfectly. 

How do big banks know? In 2019, the Wolfsberg Group published a statement that highlighted banks were expected to test the effectiveness of their current anti-money laundering controls to eliminate redundant tools and upgrade with better technology. 

The current rule-based approach and extensive use of legacy systems have led to financial institutions filing a lot more reports worldwide. 

Furthermore, the Group suggested that to gauge how well their Monitoring Suspicious Activity (MSA) works, each bank should understand the real value of their reports. The best way to understand if Suspicious Activity Reports (SARs) and Suspicious Transaction Reports (STRs) are helpful is to get feedback straight from government authorities. Once banks have figured out how their MSA is working, it is recommended to consider using additional ways to measure and improve the effectiveness of their programmers, including assessment of false negatives and completeness of SAR/STR information. 

When financial institutions decide to upgrade their programs with new technologies, it’s crucial to set clear goals when evaluating and implementing impact and results, including explainability, regulatory upskilling, and greater success in the identification of financial crime. Banks should focus on defining and prioritizing valuable outcomes beyond just filing SARs/STRs.

For regulators, important considerations include fairness, transparency, accuracy, speed, ethics, accountability, and above all explainability (ensuring that AI systems are not a ‘black box’). But beyond any of these concerns, regulators have understood that AI has succeeded where decades of rule-based approaches have failed and that responsible implementation requires a deep understanding of the AI model. 

How do regulators know? According to the Wolfsberg Group, the use of better technology (AI and machine learning) is a recent industry trend in FCRM leading to a significant increase in the effectiveness of information provided to law enforcement. More and more banks are adopting these new systems-as-a-service (SaaS). Some banks use them to enhance rules-based systems, while others rely on them as their main detection tool. Machine learning stretches beyond just monitoring transactions – it helps fill case management systems, speeds up information processing, and simplifies investigations.

As a former police lead investigator on CFT cases, feedback from my network shows that authorities have started receiving valuable SARs. When verifying the origin of these risk alerts, many have credited AI for the high-quality flags. This means AI is not only flagging more risk types, but it’s flagging the right ones.

The Wolfsberg Group’s 2024 call to redefine compliance effectiveness resonates strongly in today’s dynamic regulatory landscape. AI and machine learning technologies offer not just a technological upgrade but a strategic advantage.

The Practical Implications for Compliance Officers

Let’s break down what this means for us on the ground:

  1. Enhanced Detection and Reporting: With AI, compliance officers can streamline reporting processes. AI algorithms can sift through big data, spotting patterns and anomalies that a human might miss. By learning from historical patterns and adapting to new threats, AI can significantly reduce false positives – some reports suggest up to 60%. Picture this: you are at your desk, and your AI system alerts you with a suspicious activity report that’s spot-on. No more shifting through false positives. Your team can focus on the real threats and genuine risks.
  2. Risk-Based Approach: AI helps compliance teams adopt a more nuanced, risk-based approach to compliance which is recommended by the Wolfsberg Group and regulatory bodies. Machine learning algorithms can analyze diverse data sources – including transactional data, customer profiles, and market trends – to identify and prioritize risks. Instead of a one-size-fits-all strategy, risk managers can tailor controls to focus on high-risk areas, making their efforts more efficient and effective.
  3. Public-Private Partnerships: You know what makes a real difference? Getting intelligence straight from the source – national Financial Intelligence Units (FIUs) and law enforcement. The Wolfsberg Statement emphasizes how proactive sharing of intelligence can seriously help, for example, law enforcement provides banks with the latest on criminal typologies they’re tracking. This kind of insight allows financial institutions to fine-tune monitoring programs to target real threats and align resources with national priorities. Feedback on completed investigations is extremely helpful – helping tweak compliance approaches, creating a cycle where better inputs lead to even better outputs. AI facilitates better collaboration by providing real-time data and insights that can be shared across the board, enhancing overall response strategies. 

The Future is AI-powered Collaboration

Not surprisingly the proven value of AI is at the center of how compliance should be planned and executed. The great benefit of AI in managing financial crime risks is that it actually works, and it’s getting better by the hour!

The Wolfsberg Report is clear: AI isn’t just an add-on; it’s essential for modern AML/CFT frameworks. By integrating AI into compliance strategies, big banks are not only keeping up with the times – they’re leaping ahead of the curve. With the advent of Intuitive AI, we’re entering a new era of proactive, intelligent, and highly effective compliance. 

The real revelation? Collaboration between banks leveraging new technologies and law enforcement and supervisors following real risk. This joint effort can supercharge our detection abilities, making customer experiences smoother, and deliver top-notch intelligence to government agencies so that they can hit criminals where it hurts: the money flow. 

Yaron Hazan

Written by:

Yaron Hazan

VP of Regulatory Affairs, ThetaRay

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