Predicting Financial Suspicious Activity Reports with Online Learning Methods

This paper studies the problem of applying artificial intelligence (AI) methods to the detection of financial Suspicious Activity Reports (SARs). Financial institutions use semi-automated systems based on predetermined rules to identify suspicious activities, but most of the generated alerts are false positives and do not lead to a reporting to the authorities. From the initial investigation based on a predefined triggering scenario, to the decision of a case worth reporting to the authorities, critical parts of the process are carried out by human analysts. Part of a collaborative project with HSBC, the main objective of this research is to automate the identification of truly suspicious transactions by reducing the need for human intervention, as well as limiting the number of false alerts, using Neural Networks (NNs). We implement a prediction framework based on a combination of two unsupervised NNs used in an online learning mode and we compare the performances of our solution against other Machine Learning (ML) models.