EXPLORING EXPLAINABLE AI TECHNIQUES FOR CREDIT CARD FRAUD DETECTION MODELS
Abstract:
Credit card fraud is a significant concern for financial institutions and individuals alike. As fraudulent activities continue to evolve in sophistication, traditional rule-based fraud detection systems are becoming less effective. The emergence of machine learning techniques, particularly deep learning models, has shown promising results in detecting credit card fraud by leveraging complex patterns and anomalies in transaction data. However, the inherent black-box nature of these models raises concerns about their interpretability and transparency, hindering their adoption in critical applications.
This paper aims to explore explainable AI techniques for credit card fraud detection models, which provide insights into the decision-making process and enable human-understandable explanations. We investigate various explainable AI techniques, such as rule-based models, feature importance analysis, and local interpretability methods, and their applicability in the context of credit card fraud detection.
First, we analyze the limitations of conventional fraud detection models and highlight the need for interpretable solutions. Next, we delve into different explainable AI techniques and their capabilities in providing interpretable insights. We discuss rule-based models, which offer transparency through explicit rules and decision paths. Additionally, we examine feature importance analysis methods, such as permutation importance and SHAP values, to identify the most influential features in fraud prediction. Furthermore, we explore local interpretability techniques, including LIME and SHAP, to understand model predictions at the individual transaction level.
To evaluate the effectiveness of these explainable AI techniques, we conduct experiments on real-world credit card transaction datasets. We compare the performance of traditional black-box models with explainable AI models in terms of fraud detection accuracy, interpretability, and explainability. The results demonstrate that explainable AI techniques provide interpretable insights into the decision-making process of credit card fraud detection models without sacrificing accuracy.
Overall, this study contributes to the field of credit card fraud detection by shedding light on the importance of explainability in AI models. The findings highlight the potential of explainable AI techniques to enhance transparency, accountability, and trust in credit card fraud detection systems. The insights gained from this research can aid financial institutions and regulators in deploying more effective and explainable models to combat credit card fraud, ultimately protecting both consumers and organizations from financial losses.