Active Learning Through Sequential Design, With Applications to Detection of Money Laundering

Abstract

Money laundering is a process designed to conceal the true origin of funds that were originally derived from illegal activities. Because money laundering often involves criminal activities, financial institutions have the responsibility to detect and report it to the appropriate government agencies in a timely manner. But the huge number of transactions occurring each day make detecting money laundering difficult. The usual approach adopted by financial institutions is to extract some summary statistics from the transaction history and conduct a thorough and time-consuming investigation on those suspicious accounts. In this article we propose an active learning through sequential design method for prioritization to improve the process of money laundering detection. 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 identify the optimal prioritization criterion with minimal time and effort. A case study with real banking data demonstrates the performance of the proposed method. A simulation study shows the method’s efficiency and accuracy, as well as its robustness to model assumptions.

Keywords:  :

• Bayesian estimation
• Optimal design
• Pool-based learning
• Stochastic approximation
• Threshold hyperplane