Money Laundering and AI: A Growing Threat

In recent years, money laundering has become a major problem. Criminals are using new technologies like AI, cryptocurrency, and social media to hide their illegal activities. This has made it harder for banks and other financial institutions to detect and prevent these crimes. One example of this is the Feeding Our Future case. This was one of the largest Covid-19 fraud schemes in the United States. Over $250 million in federal child-nutrition funds meant for low-income children were stolen. Nearly 100 people have been charged, and at least 60 have been convicted. The case has sparked a lot of debate and tension in Minnesota. Money laundering is not just a problem in the United States. It is a global issue. For example, Goldman Sachs paid $2. 9 billion in 2020 for enabling money laundering linked to Malaysia’s 1MDB fund. This was a massive international financial scandal. TD Bank also paid a record $3. 1 billion in 2024 for its money-laundering failures. These failures allowed criminals to launder more than $670 million through the bank. Criminals are using AI in many ways to commit fraud. One of the most popular uses is creating deepfakes. These are AI-generated pieces of media that can impersonate real people. For example, a finance worker at a multinational firm was tricked into paying out $25 million to fraudsters using deepfake technology. The worker thought he was talking to the company’s chief financial officer in a video conference call, but it was all fake.AI is also being used to break large sums of money into small, time-distributed transactions. This is known as structuring. It is done to avoid triggering anti-money laundering (AML) red flags. Criminals are also using AI to systematically move value through mirror-trade commodity flows and cryptocurrency. This merges legal trade with illegal profits. But AI is not just being used by criminals. It is also being used to combat money laundering. Financial institutions are using AI-driven transaction monitoring to stay ahead of evolving criminal tactics and regulatory demands. Machine learning models can sift through vast datasets to detect patterns and anomalies indicative of suspicious activities. These systems can continuously learn and adapt based on new data, allowing for more dynamic and responsive monitoring. The use of AI in money laundering is a growing threat. It is important for financial institutions to stay ahead of these crimes. They need to use advanced technologies like AI and machine learning to improve efficiency, accuracy, and adaptability.