A Natural Language Processing Approach for Financial Fraud Detection

Due to the proliferation of online banking, people are more exposed than ever to attacks. Moreover, frauds are becoming more sophisticated, bypassing the security measures put in place by the financial institutions. In this paper, we propose a novel approach to fraud detection based on Natural Language Processing models. We model the user’s spending profile and detect frauds as deviations from it. To do so, we employ the attention mechanism that allows us to model and fully exploit past transactions. Our evaluation on real-world data shows that our model achieves a good balance between precision and recall, outperforming traditional approaches in different scenarios.