Improving Credit Card Fraud Detection Using Machine Learning with Under-Samplingand SMOTE Techniques
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
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.
Authors and Affiliations
Muhammad Talha Jahangir, Nauman Khursheed, Usama
Keywords
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