Solve fraud detection problem by using graph based learning methods

TL;DR

This paper introduces a graph p-Laplacian semi-supervised learning approach combined with undersampling techniques to improve credit card fraud detection, outperforming existing methods.

Contribution

It presents a novel application of graph p-Laplacian semi-supervised learning with undersampling for fraud detection, showing superior performance over traditional graph Laplacian methods.

Findings

Outperforms state-of-the-art graph Laplacian methods

Effective in imbalanced fraud detection datasets

Demonstrates robustness with undersampling techniques

Abstract

The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised learning techniques and supervised learning techniques. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Experimental results show that the graph p-Laplacian semi-supervised learning methods outperform the current state of the art graph Laplacian based semi-supervised learning method (p=2).