Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice

Yang Lu; Felix Zhan

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arXiv · 2408.10263

Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice

Yang Lu·Felix Zhan

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Abstract

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.

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Yang Lu, Felix Zhan. 2024-08-15. Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice. https://arxiv.org/abs/2408.10263

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