Unveiling the Black Box: An XAI-based Anti-Money Laundering Model

In the existing anti-money-laundering process, judging abnormal transactions still requires human resources, which is time-consuming and requires companies to pay many human costs. Many experts and scholars have used AI to identify abnormal trading behavior of accounts, but the problem of highly unbalanced data leads to poor model performances. In addition, the complex neural network of deep learning models is considered a black box, which is less likely to explain the model's results. Therefore, our research proposed an "XAI-based AI Anti-Money Laundering Model."We utilize the DNN model to detect laundering, with a recall of 0.94. By applying SHAP to the model, we evaluate the effectiveness of the dataset's ten features on the model. We find that "Payment Format"is the most crucial feature of the anti-money laundering model.