Enhancing Fraud Detection in Payment Systems Using Explainable AI and Deep Learning Techniques | IEEE Conference Publication | IEEE Xplore

Enhancing Fraud Detection in Payment Systems Using Explainable AI and Deep Learning Techniques


Abstract:

The evolving nature of financial fraud necessitates advanced detection methods beyond traditional rule-based systems and statistical approaches. This study developed a de...Show More

Abstract:

The evolving nature of financial fraud necessitates advanced detection methods beyond traditional rule-based systems and statistical approaches. This study developed a deep learning-based model for financial fraud detection, incorporating explainable AI (XAI) techniques to enhance interpretability. The model achieved a precision of 99.995% in identifying financial fraud, demonstrating significant improvement over traditional methods. By integrating XAI, using SHAP was further effective in identifying influential features in the model. Our findings indicate that the model not only enhances fraud detection accuracy but also offers stakeholders a clear understanding of fraud identification factors, potentially leading to more robust and trustworthy financial systems. However, this study highlights the need for further research on enhancing the interpretability of deep learning models in fraud detection, emphasizing the importance of balancing the model complexity with explainability to have a resilient financial security system.
Date of Conference: 23-24 October 2024
Date Added to IEEE Xplore: 25 November 2024
ISBN Information:
Conference Location: Marrakech, Morocco

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