Explainable AI - enhanced ensemble learning for financial fraud detection in mobile money transactions

This research paper addresses the pressing problem of financial fraud in the changing context of digital banking by integrating machine learning and explainable AI, specifically exploiting SHapley Additive exPlanations (SHAP). With a focus on enhancing both accuracy and interpretability, this study utilizes a synthetically generated dataset from the PaySim simulator, encompassing 6,362,620 records. The usefulness of an Ensemble Learning Model with a Voting Classifier is shown by its evaluation of different machine learning models, which achieves an excellent accuracy of 99.904%.Emphasizing transparency, accountability, and regulatory compliance, this work employs SHAP analysis to unveil attribute-level interpretability, providing stakeholders with clear insights. The goal of this interdisciplinary endeavor is to provide a safe space for digital finance by bridging the gap between precision and interpretability, which will aid in the creation of open methods.