AI anti money laundering
AI in anti-money laundering is the use of models to assess patterns in customer and transaction data for signs of illicit activity. The models produce risk scores that help investigators decide which activity needs closer examination. Money laundering conceals the criminal origin of funds, so a suspicious pattern requires an investigation into its context.
An investigator uses the score and its explanation to review the customer, payment history and supporting records. The purpose is to focus limited review capacity on activity that may warrant a report to the authorities. Banks remain responsible for checking model performance and deciding how an alert should be handled within their monitoring process.
Why rule-based monitoring needs help
Most transaction monitoring still runs on rules written by hand: amounts above a threshold, transfers to certain countries, many cash deposits in a short time. Rules are easy to explain, but many of the alerts they raise turn out to be harmless, and every alert costs an analyst's time. Google Cloud quotes the UN Office on Drugs and Crime estimate that 2 to 5 percent of global GDP, up to USD 2 trillion, is laundered each year.
AML AI from Google uses models trained on the bank's own core banking data and its history of suspicious activity, produces monthly risk scores for retail and commercial customers with an explanation for each, and can replace or complement legacy monitoring. HSBC reported that the system found two to four times as much suspicious activity with 60 percent fewer alerts.
Methods beyond the risk score
Money laundering moves through networks of accounts, so graph methods that look at who pays whom find patterns a score per customer misses. A recent study on AI in anti money laundering adds retrieval over a knowledge graph to support customer due diligence, federated learning so banks can train on shared patterns without pooling customer data, and human review built into the workflow so analysts can follow and correct what the system proposes.
Generative AI helps at the end of the chain, where an analyst writes up a case. A draft narrative still needs the analyst's judgment before it becomes a report to the Financial Intelligence Unit. Transaction monitoring in AML covers how scenarios are tuned and what happens to an alert.
What German and EU law requires
Section 25h of the German Banking Act requires banks to run data processing systems that detect suspicious transactions and business relationships. The law does not prescribe rules or models, but a bank has to be able to explain to BaFin and its auditor why the system finds what it should. A model that cannot say why it raised or suppressed an alert is hard to defend in that review.
The EU anti money laundering package moves supervision of the largest cross-border firms to AMLA in Frankfurt, and the single rulebook is described on the EU AML package. AML in Germany covers suspicious activity reports under the German Money Laundering Act.
Upcoming events on AI in finance in Germany
Does AI reduce false positives in AML?
Banks mainly adopt AI to reduce false positives in monitoring. A model that scores the whole customer relationship can rank alerts by risk and suppress the ones rules raise for harmless reasons. HSBC reported 60 percent fewer alerts alongside more findings of suspicious activity.
Who decides on a suspicious activity report when AI scores the customer?
A person decides whether to file a suspicious activity report. The score directs the analyst to a case and explains the main factors, but the decision to file a report stays with the bank's staff and its money laundering reporting officer.
AI in anti money laundering and Finance Loop
Finance Loop brings money laundering officers, financial crime analysts and the regtech firms that build their tools together at events in Frankfurt, the seat of AMLA. Finance Loop highlighted fAInance by Sopra Steria and Fraunhofer IAIS, whose program had a station on AI against financial crime, and lists a course on AI and KYC in its academy.
Finance Loop is a professional network supporting the institutional use of emerging technologies in finance, such as AI, digital payments, cloud and blockchain solutions. Finance Loop helps its members build skills and personal networks in these fields: Investment & Digital Assets, Payments & Digital Money, Digital Infrastructure & Sovereignty, and Risk & Compliance.