With the rapid growth in the use of credit cards in electronic payments, financial institutions and financial service providers are becoming vulnerable to fraud, which leads to huge losses every year. The development and implementation of an effective credit card fraud detection system is essential to reduce such losses. The presented paper analyzes current scientific work in the field of developing methods and models of artificial intelligence to ensure the security of financial transactions. The purpose of this paper is to review and compare machine learning models and methods for conducting secure financial transactions using credit cards. The above publications in this area mainly use a data set on fraudulent credit card transactions collected from European cardholders. It also mentions publications that use both synthetic and other datasets. Among the machine learning algorithms used in these publications, the effectiveness of decision trees, random forests, SVM, logistic regression and other methods on anonymized credit card fraud data, as well as algorithms using neural networks, is investigated and tested. The researchers apply these methods to preprocessed data samples. To assess the quality of the machine learning model, various special metrics are considered in classification tasks, such as accuracy, completeness, F-measure, etc. А comparative analysis of these publications has revealed several of the most preferred and effective methods for processing financial transactions.