Money laundering is big criminal business worldwide. Banks are tasked by the regulators with reducing the volume and value of money laundering over their services, but that’s easier said than done. In response, many are now starting to use artificial intelligence (AI) to tune results, finding small anomalies within a large amount of data. In the fight against money laundering, banks need both scale and granularity.
In most countries, the regulatory requirements make it difficult to track the success of anti-money laundering (AML) projects, however. Banks are tasked with identifying and investigating potentially fraudulent activity, and disclosing it to the authorities as appropriate. However, there are only two countries worldwide where the authorities will come back and tell the bank what happened – whether they were right. That being the case, how can banks push for greater accuracy in their AML projects when they don’t see the results?
