{"id":99609,"date":"2025-07-09T22:40:55","date_gmt":"2025-07-09T21:40:55","guid":{"rendered":"https:\/\/www.ictrechtswijzer.be\/?p=99609"},"modified":"2025-07-09T22:43:22","modified_gmt":"2025-07-09T21:43:22","slug":"is-the-use-of-ai-by-banks-in-the-fight-against-money-laundering-a-blessing-or-a-curse-for-the-banking-client","status":"publish","type":"post","link":"https:\/\/www.ictrechtswijzer.be\/en\/is-het-gebruik-van-ai-door-banken-in-de-strijd-tegen-witwassen-een-zegen-of-een-vloek-voor-de-bankclient\/","title":{"rendered":"Is the use of AI by banks in the fight against money laundering a blessing or a curse for the bank customer?"},"content":{"rendered":"

The fight against money laundering and terrorist financing has gained momentum in recent years. Financial institutions, such as banks, play a crucial role in this as \"gatekeepers\" of our financial system. They are legally obliged to monitor their clients' transactions and report unusual transactions to the Financial Information Processing Unit (CFI).<\/a>. To analyze the immense flow of transactions, banks are increasingly using advanced technology, particularly artificial intelligence (AI). But what does this evolution mean for you as a bank customer? And what guarantees are there that these smart systems do not violate your rights?<\/p>\n\n\n\n

The legal reporting obligations of banks<\/strong><\/h4>\n\n\n\n

Banks, by virtue of the anti-money laundering legislation<\/a> required to conduct ongoing customer due diligence. This means that they must identify and verify their clients, and establish a risk profile. They must then review their clients' transactions against this profile. When a transaction does not fit the expected pattern or when the bank otherwise suspects that the funds have an illegal origin or are related to terrorism, it must report this to CTIF-CFI. <\/p>\n\n\n\n

At the heart of the matter is the assessment of whether a transaction is \"suspicious. The legislature has largely left this assessment to the subjective judgment of the bank itself. <\/sup><\/sup><\/sup><\/sup>It is the bank that must determine whether there is \"a presumption.\" <\/sup>If that suspicion is there, she is obligated to report; if it is not there, she is just not allowed to report. <\/sup><\/p>\n\n\n\n

Artificial intelligence as new tracking dog<\/strong><\/h4>\n\n\n\n

To support and automate this subjective assessment, banks are increasingly turning to AI. Broadly speaking, two types of systems can be distinguished:<\/p>\n\n\n\n

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  1. Rule-based systems (Expert systems):<\/strong> These systems, which can be considered a rudimentary form of AI, operate based on predefined rules and decision trees (\"if x, then y\"). For example, consider a rule that generates an alarm for every cash deposit over a certain amount.<\/li>\n\n\n\n
  2. Artificial Neural Networks (ANNs):<\/strong> This is the more advanced form of AI, mimicking the workings of the human brain. These systems, which also underlie technologies such as ChatGPT, are trained with huge amounts of data on transactions. Using \"deep learning,\" the algorithms learn to independently recognize patterns that may indicate money laundering, without a programmer having to input every line. The system can thus calculate the probability that a transaction is suspicious and classify it. <\/li>\n<\/ol>\n\n\n\n

    The promise of AI is great: more efficient and effective detection of suspicious transactions, benefiting society as a whole. <\/sup>The systems can detect deviations from normal patterns (anomalies) that a human analyst might overlook. <\/sup><\/p>\n\n\n\n

    The legal risks: \"automation bias\" and lack of transparency<\/strong><\/h4>\n\n\n\n

    However, the deployment of AI is not without its dangers. One of the biggest risks is the lack of transparency of deep learning models. Because the algorithms are self-learning, it becomes extremely difficult, if not impossible, to figure out exactly why the system flagged a particular transaction as suspicious. <\/sup><\/sup><\/sup><\/sup> We refer to this as the \"black box\" effect.<\/p>\n\n\n\n

    This raises fundamental legal questions. An AI system can make statistical connections, but it cannot reason legally. <\/sup><\/sup><\/sup><\/sup><\/sup><\/sup><\/sup>It cannot understand the context of a transaction or weigh what is reasonable and fair in a specific case. <\/sup><\/p>\n\n\n\n

    When a bank blindly relies on the output of an AI system - a phenomenon known as \"automation bias <\/sup> - the consequences for the client can be dramatic:<\/p>\n\n\n\n