Money laundering is a huge problem for the financial services sector. According to the United Nations Office on Drugs and Crime, the estimated $2 trillion is “cleaned” through the banking system each year. Fines for banks who fail to stop money laundering have increased by 500X in the last decade to more than $10 Billion per year. As a result, banks have built large teams of people and given them the time-consuming task of finding and investigating suspicious transactions which often take the form of numerous small transfers within a complex network of players. Investigation teams have used rules-based systems to find suspicious transactions, but the rules quickly become outdated and produce large numbers of false positives that still need to be reviewed.
AI, especially time series modeling, is particularly good at looking at series of complex transactions and finding anomalies. Anti-money laundering using machine learning techniques can find suspicious transactions and networks of transactions. These transactions are flagged for investigation and can be scored as high, medium or low priority so that the investigator can prioritize their efforts. The AI can also provide reason codes for the decision to flag the transaction. These reason code tell the investigator where they might look to uncover the issues and help to streamline the investigative process. AI can also learn from the investigators as they review and clear suspicious transactions and automatically reinforce the AI model’s understanding to avoid patterns that don’t lead to laundered money.