Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice
It addresses fraud detection for financial security, but is incremental as it adapts an existing method with practical guidelines.
This study evaluated Kolmogorov-Arnold Networks (KAN) for fraud detection and found their effectiveness depends on data context, proposing a PCA-based decision rule and heuristic hyperparameter tuning to reduce computational costs.
This study evaluates the applicability of Kolmogorov-Arnold Networks (KAN) in fraud detection, finding that their effectiveness is context-dependent. We propose a quick decision rule using Principal Component Analysis (PCA) to assess the suitability of KAN: if data can be effectively separated in two dimensions using splines, KAN may outperform traditional models; otherwise, other methods could be more appropriate. We also introduce a heuristic approach to hyperparameter tuning, significantly reducing computational costs. These findings suggest that while KAN has potential, its use should be guided by data-specific assessments.