Explainable deep learning model for predicting money laundering transactions

Abstract Money laundering has been a global issue for decades. The ever-changing technology landscape, digital channels, and regulations make it increasingly difficult. Financial institutions use rule-based systems to detect suspicious money laundering transactions. However, it suffers from large false positives (FPs) that lead to operational efforts or misses on true positives (TPs) that increase the compliance risk. This paper presents a study of convolutional neural network (CNN) to predict money laundering and employs SHapley Additive exPlanations (SHAP) explainable artificial intelligence (AI) method to explain the CNN predictions. The results highlight the role of CNN in detecting suspicious transactions with high accuracy and SHAP’s role in bringing out the rationale of deep learning predictions.