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

• URL: http://arxiv.org/abs/2408.10263v2
• Date: Tue, 3 Sep 2024 22:23:06 GMT
• Title: Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice
• Authors: Yang Lu, Felix Zhan,
• Abstract summary: 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.
• Score: 3.692410936160711
• License: http://creativecommons.org/licenses/by/4.0/
• 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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