Credit card fraud detection using ensemble data mining methods

Nowadays, credit card fraud has become one of the most complex and vital issues in the world, even more than the past decades. Widespread use of credit cards is one of the most attractive forms of online transactions in the banking sector. Credit cards' attractiveness is the ease of life for people, which allows customers to use their credit at any time, place, and amount, without carrying cash and without the hassle of carrying cash. This is to make it easy to pay for purchases made via the Internet, mobile phones, Automated teller machines (ATMs), etc. Meanwhile, financial information acts as the main factor of financial transactions in the market. Due to the popularity of using credit cards, various security challenges are increasing, and this issue has intensified fraud intending to obtain unauthorized financial benefits. Researchers have proposed different solutions for detecting and predicting credit card fraud, which has been successful. One of these methods is data mining and machine learning. The issue of accuracy in predicting problems is vital in this regard. In this study, we examine Ensemble Learning methods, including gradient boosting(LightGBM and LiteMORT), and combine them by averaging methods(Simple and Weighted Averaging methods) and then evaluate them. Combining these methods reduces error rates and increases efficiency and accuracy. By evaluating the models by Area under the curve(AUC), Recall, F1-score, Precision, and Accuracy criteria, we reached the best results of 95.20, 90.65, 91.67, 92.79, and 99.44 for the combination of LightGBM and LiteMORT using weighted averaging, respectively.