Artificial intelligence has the power to transform anti-money laundering (AML) systems, but without strong regulatory frameworks, even the best AI can fall short—or worse, cause more harm than good. Ignas Dovidonis explores how smarter rules, not just smarter tech, are key to unlocking the real potential of AI in AML.
Traditional AML systems are notoriously inefficient, overwhelmed by a flood of false positives and reactive reporting. Many financial institutions have turned to AI to detect patterns, flag suspicious activity, and streamline compliance. But this shift has outpaced the laws and regulations meant to guide it. As a result, banks are deploying AI tools into compliance environments where outdated rules often prevent them from operating effectively.
The core issue? Most regulations still enforce rigid, one-size-fits-all requirements. Every transaction is treated with the same suspicion, regardless of actual risk. This not only overburdens compliance teams, but also wastes time and resources that could be better spent focusing on high-risk activities. What’s needed is a risk-based regulatory model—one that encourages banks to use AI to prioritize threats based on context and probability, rather than sheer volume.
Additionally, the conversation around AI in AML can’t ignore the importance of explainability and fairness. Without clear regulatory guidance, financial institutions may unknowingly deploy biased algorithms or “black box” systems that produce results no one can fully understand. Transparency, documentation, and regular auditing of AI tools must be built into the regulatory framework from the start. Regulators should require that banks can explain how decisions are made by AI systems, especially when those decisions impact real people or raise red flags.
Collaboration will also be key. If regulators and financial institutions work together—sharing insights, test results, and best practices—both sides can develop better tools and smarter standards. Regulatory sandboxes, where AI solutions can be safely tested in real-world scenarios, are a strong example of this collaborative approach in action.
In practice, smarter AML regulation would include policies that:
- Encourage the use of AI for risk-based analysis
- Define standards for data quality, privacy, and bias mitigation
- Require transparency and explainability in AI decision-making
- Provide safe testing environments through regulatory sandboxes
The goal isn’t just to reduce the noise—it’s to improve outcomes. With the right rules in place, AI can help detect real threats faster and more accurately, while easing the burden of compliance on financial institutions. Done right, this could lead to fewer missed red flags, stronger financial protections, and a more agile, responsive AML system overall.
In short: AI can supercharge AML efforts, but it won’t succeed without smart, clear, and modern regulations to guide it. The future of financial crime prevention depends on this balance.
