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.

本论文研究了信用卡欺诈检测的难点 - 数据集不平衡的问题，采用随机森林算法和合成少数过采样技术（SMOTE）以提高欺诈检测的准确性，在信用卡交易数据集上实验，成功实现了欺诈检测的 98% 准确率。