Robust Credit Card Fraud Detection Based on Efficient Kolmogorov-Arnold Network Models

Credit card fraud detection remains a significant challenge in the financial industry, necessitating advanced models to identify fraudulent activities while minimizing false positives accurately. Traditional machine learning approaches, such as Multilayer Perceptrons (MLP), have been widely used but often struggle with interpretability and parameter optimization issues. Kolmogorov-Arnold Networks (KAN) present a promising alternative by addressing these limitations through their inherent structure, which allows for more interpretable and potentially more accurate models. This paper explores the application of KAN in the context of credit card fraud detection, motivated by the need for more effective and interpretable solutions. We implement and evaluate three MLP, KAN, and efficient KAN models using two publicly available credit card fraud datasets. Our experimental results demonstrate that both KAN and efficient KAN significantly outperform the traditional MLP model in terms of detection accuracy while reducing the number of parameters compared to MLP. The findings underscore the potential of KAN and its efficient variant as superior alternatives for credit card fraud detection, offering enhanced accuracy and interpretability. This study provides valuable insights into model performance and parameter efficiency, guiding future research and practical applications in fraud detection systems.