Credit Card Fraud Detection Using Machine Learning with Undersampling and SMOTE Oversampling

Muhammad Talha Jahangir; Nauman Khursheed; Usama

Authors

Muhammad Talha Jahangir Department of CS, MNS University of Engineering and Technology, Multan, Pakistan.
Nauman Khursheed Department of CS, MNS University of Engineering and Technology, Multan, Pakistan.
Usama Department of CS, MNS University of Engineering and Technology, Multan, Pakistan.

Keywords:

Credit Card Fraud Detection, Machine Learning, SMOTE, Random Forest, Class Imbalance, Ensemble Methods., IoT Attacks, IoT Anomalies, Random Under Sampling, Machine Learning

Abstract

Credit card fraud detection is currently the most popular implementation domain of Computational Intelligence techniques. A common issue in the present world is being faced by many organizations and institutions. This is due to the increase in the frequency of transactions, which are now conducted electronically and a higher increase in the number of electronic commerce platforms. In the present world, we are experiencing many credit card issues. In this paper, we apply various algorithms of machine learning as random forest, logistic regression and k-Nearest Neighbors (KNN) to train the specified machine learning model using a given dataset to design the comparative conducted on the accuracy and various measures of the models as it is being implemented via each of such algorithms. To address this, we evaluate the possibility of under-sampling and SMOTE as approaches that can enhance multiple machine-learning models. An accuracy of 99.99% in the dataset was achieved using the SMOTE technique with the Random Forest model. This research concludes that SMOTE improves the performance of the machine learning model for fraud identification and presents a more efficient approach to address the problem of class imbalance.

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