AI-Powered Fraud Detection and Prevention in Banking

Abstract

Deep learning techniques have been integrated in modern banking fraud detection and have reached an extreme degree at which fraud is eliminated. In this research, we develop a Long Short-Term Memory (LSTM) network for the task of transaction series analyses for detecting anomaly pattern that signifies possible fraud activities. Despite their poor performance with respect to the changing fraud approaches used, Long Short-Term Memory (LSTM) networks are shown to excel at finding complicated time-related patterns. With this model fraud prediction becomes possible in real time as it uses the past transactions records to identify protected behaviour with minimum error but maximum accuracy. SHAP (SHapley Additive Explanations) enhances our model as it allows us to observe how the model treats individual cases so as to abide by the rules of the financial industry. We find that although LSTM-SHAP is more expensive than traditional machine learning models in terms of training, it achieves more efficient fraud detection with more transparent operation. Accordingly, the study also positions deep learning approaches as a means to attack financial frauds and enhance the banking security as well as the trust in the banking customers.