Improving Credit Card Fraud Detection Using Machine Learning with Under-Sampling and SMOTE Techniques

Aug 3, 2026

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Credit card fraud detection is treated as a high-impact application of computational intelligence as electronic payments and e-commerce expand transaction volume and fraud frequency. The work applies machine learning models, including Random Forest, Logistic Regression and K-Nearest Neighbors, and examines how class imbalance affects training and evaluation. To mitigate skewed distributions, under-sampling of the majority class and SMOTE-based synthetic over-sampling for the minority class are compared. Results report very high accuracy with SMOTE-enhanced Random Forest and conclude that SMOTE improves fraud identification efficiency by addressing class imbalance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Problem of credit card fraud and class imbalance

Methodology

Data preparation and sampling strategy (under-sampling and SMOTE)

Model training (Random Forest, Logistic Regression, KNN, Naïve Bayes)

Research Hierarchy/Flow

Evaluation metrics and comparison

Novelty of Study

Combined resampling and ensemble-based analysis

Conclusion

Improving Credit Card Fraud Detection Using Machine Learning with Under-Sampling and SMOTE Techniques Muhammad Talha Jahangir*, Nauman Khursheed, Usama Department of CS, MNS University of Engineering and Technology, Multan, Pakistan. *[Correspondence:](Correspondence: mtalhajahangir@mnsuet.edu.pk)[ ](Correspondence: mtalhajahangir@mnsuet.edu.pk)[mtalhajahangir@mnsuet.edu.pk](Correspondence: mtalhajahangir@mnsuet.edu.pk) Citation | Jahangir. M. T, Khursheed. N, Usama, “Credit Card Fraud Detection Using Machine Learning with Under Sampling and Smote Oversampling”, IJIST, Vol. 06 Issue. 04 pp 1568-1585, Oct 2024 Received| Aug 26, 2024 Revised| Sep 28, 2024 Accepted| Oct 06, 2024 Published| Oct 9, 2024. 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. Keywords: Credit Card Fraud Detection, Machine Learning, SMOTE, Under Sampling, Random Forest, Class Imbalance, Ensemble Methods. Introduction: The widespread adoption of digital transactions has revolutionized the movement of money, enhancing its efficiency, convenience and accessibility. However, this advancement has also resulted in a concerning rise in credit card fraud, affecting both lenders and consumers alike. In addition to eroding consumer confidence and imposing extra costs on financial institutions, credit card fraud can result in significant financial losses. Conventional fraud detection techniques, including manual inspections and rule-based systems, find it challenging to manage the intricacy and magnitude of fraudulent operations in the contemporary digital landscape. Credit card identity theft is one of the most significant challenges faced by financial companies today. With the rise of e-commerce and banking services offered online along with other electronic payment systems, the frequency and complexity of fraud have also changed. Besides, the direct financial losses caused by credit card thefts, significantly erode customer trust, increase operational costs and pose a serious risk to the reputation of financial institutions. The dynamic nature of credit card fraud detection is a much more challenging task. Inherently unpredictable, fraudsters continuously adapt their tactics to exploit vulnerabilities in payment systems. Rule-based systems are not effective in adapting to change, as they rely on established patterns that can quickly become outdated. Consequently, there is a greater necessity for more effective and diverse technology to precisely identify fraudulent transactions in real-time. Moreover, every unauthorized transaction that goes undiscovered can damage an institution's reputation by raising doubtsin the minds of clients about its ability to protect their money, in addition to causing financial loss. To address this issue, we implemented a methodology that overcam

FAQs

Why is class imbalance a key challenge in credit card fraud detection?

Fraudulent transactions are rare compared with normal transactions, causing skewed class distributions. This imbalance makes conventional models struggle to learn patterns for the minority (fraud) class.

How do under-sampling and SMOTE work in this study?

Under-sampling reduces the majority class size (normal transactions), while SMOTE generates synthetic minority-class samples (fraudulent transactions). The approach aims to balance training data while avoiding overfitting caused by simple duplication.

Which techniques and models achieved the strongest performance?

The study reports that SMOTE combined with a Random Forest model achieved very high accuracy on the dataset. It also evaluates multiple models using accuracy, precision, recall, and F1-score for comparative assessment.

Aug 3, 2026

Upload by Asher