A Comparative Analysis of Machine Learning Techniques for Detecting Credit Card Fraud

Authors

  • Rajani P. K. Faculty of Electronics and Telecommunication Department, Pimpri Chinchwad College of Engineering, Pune – 411044, INDIA
  • Roshan Mathew Department of Electronics and Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Pune – 411044, INDIA
  • Atharva Walawalkar Department of Electronics and Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Pune – 411044, INDIA
  • Prathmesh Patil Department of Electronics and Telecommunication Engineering, Pimpri Chinchwad College of Engineering, Pune – 411044, INDIA
  • Ujwal Shirode Faculty of Electronics and Telecommunication Department, Pimpri Chinchwad College of Engineering, Pune – 411044, INDIA
  • Ajjay Gaadhe Faculty of Electronics and Telecommunication Department, Pimpri Chinchwad College of Engineering, Pune – 411044, INDIA

Keywords:

Decision Tree, Gradient Boosting, Random Forest, SMOTE, SVM, XGBoost

Abstract

Fraudulent use of credit cards is a major problem across the world, causing enormous financial losses for banks, retailers, and customers. Machine learning algorithms are effective in detecting fraudulent transactions, but the imbalanced dataset with the majority of transactions being legitimate poses a challenge. The SMOTE technique is used to address the imbalanced dataset caused by the minority class (fraudulent transactions) in this study, which assesses the effectiveness of multiple machines learning classifiers, including Logistic Regression, Random Forest, Decision Tree, Gradient Boosting, XGBoost, as well as SVM. The Random Forest model is the most effective classification algorithm overall, with a recall of 0.8482, precision of 0.8526 and F1 score of 0.8504 in the 60:40 split. It had a recall of 0.8603, precision of 0.8357 and F1 score of 0.8478 for the 70:30 split. For the 80:20 split, it had a recall of 0.8367, precision of 0.9213 and F1 score of 0.8770. The study indicates that the SMOTE approach and various classifiers are effective for detecting credit card fraud, with Random Forest being the best-performing classifier.

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Published

24.03.2024

How to Cite

P. K., R., Mathew, R. ., Walawalkar, A. ., Patil, P. ., Shirode, U. ., & Gaadhe, A. . (2024). A Comparative Analysis of Machine Learning Techniques for Detecting Credit Card Fraud. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 146–153. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5232

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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