Money laundering is a massive drain on the world’s financial, legal and economic institutions and current rule based AML controls with a false positive rate of 90% are just not adequate to detect and monitor them. AML is ripe for disruption and innovation through use of Artificial Intelligence (AI) and Machine Learning (ML) and even the regulators are encouraging the same. Key areas of AML where AI and ML have been shown to work through recent published papers are risk scoring; customer segmentation and transaction monitoring using clustering (k-means); classification (support vector machines) and deep learning (graph convolutional networks). These approaches shows us a glimpse of the near future state of AML controls and how new technology can help solve the seemingly insurmountable problem of money laundering as it exists now.
In an announcement this month that went under the radar due to the US-China trade war grabbing the headlines, the People’s Bank of China (PBoC) announced
OCC Spring 2019 Semiannual Risk Report Background: The Office of the Comptroller of the Currency (OCC) publishes a report (Semiannual Risk Perspective) twice a year