Financial institutions are under growing pressure to strengthen anti-money laundering (AML) controls while managing rising compliance costs.
Artificial intelligence (AI) is widely seen as part of the solution, yet adoption remains cautious. Financial crime compliance is one of the most tightly regulated areas across banking, payments, WealthTech and InsurTech.
Napier AI recently delved into how firms can implement compliance-first AI for AML.
According to the Napier AI / AML Index 2025–2026, global money laundering losses are at least $5.5tn. At the same time, regulated firms could save up to $183bn annually by implementing AI-driven compliance systems, while economies could recover more than $3.3tn by reducing illicit flows.
A recent survey of financial crime professionals suggests most organisations are still in early stages. Only a small minority report AI as fully embedded in AML operations. However, within the next 12–24 months, most expect to be actively using or testing AI solutions.
Regulators are increasingly encouraging responsible experimentation, Napier explained. In the UK, the Financial Conduct Authority (FCA) has launched initiatives including the Synthetic Data Expert Group, the Supercharged Sandbox with NVIDIA, and AI live testing through its AI Lab. Napier AI has also collaborated with The Alan Turing Institute, Plenitude Consulting and the FCA to create synthetic datasets for training financial crime detection models in controlled environments.
Globally, regulators are taking similar steps. The Monetary Authority of Singapore (MAS) introduced Project MindForge to explore generative AI in financial services. Bank Negara Malaysia (BNM) has issued a discussion paper on AI governance. The European Banking Authority (EBA) and the U.S. Department of the Treasury have both sought industry input on AI’s risks and applications in financial services.
Despite this progress, firms face practical barriers. Data fragmentation, legacy systems and complex policy documentation limit AI readiness. Explainability remains critical, as compliance teams must justify decisions to regulators and law enforcement. Black-box models are unlikely to meet expectations.
Reducing false positives is a common objective, but focusing solely on alert volumes risks overlooking false negatives. Effective AI strategies balance detection accuracy with operational efficiency, automating repetitive reviews while preserving human oversight.
Ultimately, compliance-first AI is essential. Institutions must design systems that prioritise transparency, fairness and auditability from the outset. When implemented correctly, AI can enhance AML effectiveness while supporting regulatory obligations — not undermining them.
For more insights, read the story here.
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