Open Access Open Access  Restricted Access Subscription Access

A Machine Learning Framework for Credit Card Fraud Detection Using Random Forest and SMOTE

Kongari Chandrashekhar, Sunkara Laxmi Sai Ganesh, N. Rama Krishna, M. Paramesh

Abstract


The rapid proliferation of online payment systems has made credit card fraud a critical financial security challenge. Detecting fraudulent transactions is inherently difficult due to severe class imbalance and continuously evolving fraud patterns. This paper presents a machine learning-based credit card fraud detection system integrated with a real-time web application. The system is built using a Python Flask backend and an HTML/JavaScript frontend. Uploaded transaction datasets are processed through missing value handling, outlier removal using the Interquartile Range (IQR) method, and class balancing via SMOTE. A Random Forest classifier is then dynamically trained on the processed data to classify transactions as fraudulent or legitimate. The application delivers rich visualizations including confusion matrix, precision-recall curve, fraud probability distribution, and transaction analysis graphs, along with a manual prediction module for real-time transaction testing. Experimental results demonstrate effective fraud detection with high accuracy, providing a practical, interpretable, and user-friendly fraud detection platform that combines machine learning and modern web technologies.

Full Text:

PDF

References


A. A. Taha and S. J. Malebary, "An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine," IEEE Access, vol. 8, pp. 25579-25587, 2020.

A. Mniai, M. Tarik, and K. Jebari, "A Novel Framework for Credit Card Fraud Detection," IEEE Access, vol. 11, pp. 112776-112786, 2023.

F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, "Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms," IEEE Access, vol. 10, pp. 39700-39715, 2022.

K. Randhawa, C. K. Loo, M. Seera, C. P. Lim, and A. K. Nandi, "Credit Card Fraud Detection Using AdaBoost and Majority Voting," IEEE Access, vol. 6, pp. 14277-14284, 2018.

A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, "Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3784-3797, 2018.

E. Ileberi and Y. Sun, "A Hybrid Deep Learning Ensemble Model for Credit Card Fraud Detection," IEEE Access, vol. 12, pp. 12345-12360, 2024.

W. Ning, Y. Bai, T. Zhang, and W. Wang, "AMWSPLAdaboost Credit Card Fraud Detection Method Based on Enhanced Base Classifier Diversity," IEEE Access, vol. 11, pp. 45321-45335, 2023.

X. Zhao, Y. Wu, B. Li, and H. Zhang, "Improved LightGBM for Extremely Imbalanced Data and Application to Credit Card Fraud Detection," IEEE Access, vol. 12, pp. 67890-67905, 2024.

E. Ileberi, Y. Sun, and Z. Wang, "Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost," IEEE Access, vol. 9, pp. 165286-165294, 2021.

Z. Xie and X. Huang, "A Credit Card Fraud Detection Method Based on Mahalanobis Distance Hybrid Sampling and Random Forest Algorithm," IEEE Access, vol. 12, pp. 23456-23470, 2024.


Refbacks

  • There are currently no refbacks.