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.

翻译：本研究评估了Kolmogorov-Arnold网络（KAN）在欺诈检测中的适用性，发现其有效性依赖于具体情境。我们提出了一种基于主成分分析（PCA）的快速决策规则，用以评估KAN的适用性：若数据能通过样条在二维空间中有效分离，则KAN可能优于传统模型；否则，其他方法可能更为合适。我们还引入了一种启发式的超参数调优方法，显著降低了计算成本。这些结果表明，尽管KAN具有潜力，但其使用应基于针对具体数据的评估来指导。