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

TL;DR

This paper explores the use of Kolmogorov-Arnold Networks for fraud detection, proposing a PCA-based decision rule and heuristic hyperparameter tuning to determine their effectiveness in different contexts.

Contribution

It introduces a practical decision rule and hyperparameter tuning heuristic for applying KAN in fraud detection, bridging the gap between theory and practice.

Findings

KAN effectiveness varies with data context

PCA-based rule helps identify suitable scenarios for KAN

Heuristic hyperparameter tuning reduces computational costs

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.