The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the development of technology and the rise in fraud cases. This paper implements the random forest (RF) algorithm to solve the issue in the hand. A dataset of credit card transactions was used in this study. The main problem when dealing with credit card fraud detection is the imbalanced dataset in which most of the transaction are non-fraud ones. To overcome the problem of the imbalanced dataset, the synthetic minority over-sampling technique (SMOTE) was used. Implementing the hyperparameters technique to enhance the performance of the random forest classifier. The results showed that the RF classifier gained an accuracy of 98% and about 98% of F1-score value, which is promising. We also believe that our model is relatively easy to apply and can overcome the issue of imbalanced data for fraud detection applications.

翻译：信用卡已成为线上和线下交易中最流行的支付方式。随着技术的发展和欺诈案件的增多，迫切需要开发一种欺诈检测算法来准确识别并阻止欺诈活动。本文采用随机森林算法解决这一问题。研究中使用了信用卡交易数据集。处理信用卡欺诈检测时的主要挑战是数据集的不平衡性，其中绝大多数交易为正常交易。为克服不平衡数据集问题，本文采用了合成少数类过采样技术（SMOTE）。同时，通过超参数技术优化随机森林分类器的性能。结果表明，随机森林分类器准确率达到98%，F1分数也约为98%，表现良好。我们认为该模型易于实施，并能有效解决欺诈检测应用中的不平衡数据问题。