Active Learning via Sequential Design with Applications to Detection of Money Laundering

Money laundering is an act to hide the true origin of funds by sending them through a series of seemingly legitimate transactions. Because it often involves criminal activities, financial institutions have the responsibility to detect and inform about them to the appropriate government agencies in a timely manner. However, detecting money laundering is not an easy job because of the huge number of transactions that take place each day. The usual approach adopted by financial institutions is to extract some summary statistics from the transaction history and do a thorough and time-consuming investigation on those accounts that appear to be suspicious. In this article, we propose an active learning method using Bayesian sequential designs to identify the suspicious accounts. The method uses a combination of stochastic approximation and D-optimal designs to judiciously select the accounts for investigation. The sequential nature of the method helps to identify the suspicious accounts with minimal time and effort. A case study with real banking data is used to demonstrate the performance of the proposed method. A simulation study shows the efficiency and accuracy of the proposed method, as well as its robustness to model assumptions.