Credit Card Fraud Detection Model Based on Correlation Feature Selection

Ahmad Salim^1,*, Salah N. Mjeat¹, Daniah Abul Qahar Shakir², Mohammed Awad Alfwair³

¹Middle Technical University, Baghdad, Iraq

²University of Anbar, Anbar, Iraq

³The University of Jordan, Amman, Jordan

Emails: ahmadsalim@mtu.edu.iq; salahnoori@mtu.edu.iq; daniahashakir@uoanbar.edu.iq; m.alfoair1990@gmail.com

Abstract

Credit card fraud is a widespread cybercrime that threatens financial security. Effective cybersecurity measures are essential to mitigate these risks. Machine learning has shown promising results in detecting credit card fraud by analyzing transaction data and identifying patterns of suspicious behavior. Feature selection is crucial in machine learning because it simplifies the model, improves its performance, and prevents overfitting. This research introduces a machine learning model designed for credit card fraud detection. The model makes use of three types of correlations. Pearson, Spearman, and Kendall, to identify features and enhance the fraud detection process. Testing on datasets yielded impressive results achieving category accuracies of 99.95% and 99.58% surpassing alternative approaches. Also, the results showed that Kendall correlation is the best among the three types of correlation in selecting attributes in all approved datasets.

Keywords: Cybersecurity; Credit card fraud detection; Machine learning; Feature selection; Correlation