IMPLEMENTATION OF MACHINE LEARNING TECHNIQUES TO DETECT FRAUDULENT CREDIT CARD TRANSACTIONS ON A DESIGNED DATASET

Baisholan N. Turdalyuly M. Gnatyuk S. Baisholanova K. Kubayev K.
15 July 2023 Little Lion Scientific

Journal of Theoretical and Applied Information Technology
2023 #101 Issue 13 5279 - 5287 pp.

The rise in technology, particularly the increase in online shopping, has made it easier for cybercriminals to obtain and exploit stolen payment card information. Traditional fraud detection systems are finding it increasingly challenging to keep up with the rapid pace of technological advancement, leading to a surge in payment card fraud. Hence, it is essential for companies to continually update their fraud detection methods to keep up with the latest tactics employed by fraudsters. Machine learning algorithms have the ability to analyze large datasets and quickly identify anomalies or deviations from normal behaviour, making them a highly effective tool for payment card fraud detection. By detecting fraud early, organizations can minimize their financial losses and prevent further damage. In this study, we generated a credit card fraud dataset that comprises three types of fraud cases. The dataset is imbalanced, with a ratio of fraudulent transactions at 0.004, making it close to real-world data. To handle the imbalance in the dataset related to credit card fraud detection, we employed popular machine learning models such as Random Forest, Decision Tree, Logistic Regression, and XGBoost. The results showed that XGBoost and Random Forest outperformed the other models on both the training and test sets. However, the Decision Tree algorithm with unlimited depth had the highest average accuracy on the training set and the lowest average accuracy on the test set, indicating that this algorithm should be avoided due to overfitting. In conclusion, our study highlights the significance of using machine learning algorithms for payment card fraud detection. The results demonstrate that XGBoost and Random Forest are the most effective models for detecting credit card fraud in imbalanced datasets. By employing these models, organizations can improve their fraud detection capabilities and minimize the financial impact of payment card fraud.

Anomaly Detection , Decision Tree , Fraud Detection , Imbalanced Dataset , Logistic Regression , Random Forest , Transaction Fraud Dataset

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Al-Farabi Kazakh National University, Almaty, Kazakhstan
Satbayev University, Almaty, Kazakhstan
National Aviation University, Kyiv, Ukraine

Al-Farabi Kazakh National University
Satbayev University
National Aviation University

10 лет помогаем публиковать статьи Международный издатель

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