The paper presents the demand for the spread of payment systems. This is caused by the development of technology. The open issue of application of payment systems - fraud - is singled out. It is established that there is no effective algorithm that would be the standard for all financial institutions in detecting and preventing fraud. This is due to the fact that approaches to fraud are dynamic and require constant revision of forecasts. Prospects for the development of scientific and practical approaches to prevent fraudulent transactions in payment systems have been identified. It has been researched that machine learning is appropriate in solving the problem of detecting fraud in payment systems. At the same time, the detection of fraud in payment systems is not only to build the algorithmic core, but also to build a reliable automated system, which in real time, under high load, is able to control data flows and effectively operate the algorithmic core of the system. The paper describes the architecture, principles and operation models, the infrastructure of the automated fraud detection mechanism in payment systems. The expediency of using a cloud web service has been determined. The deployment of the model in the form of automated technology based on the Amazon Web Services platform is substantiated. The basis of the automated online fraud detection system is Amazon Fraud Detector and setting up payment fraud detection workflows in payment systems using a customizable Amazon A2I task type to verify and confirm high-risk forecasts. The paper gives an example of creating an anomaly detection system on Amazon DynamoDB streams using Amazon SageMaker, AWS Glue and AWS Lambda. The automated system takes into account the dynamics of the data set, as the AWS Lambda function also works with many other AWS streaming services. There are three main tasks that the software product solves: prevention and detection of fraud in payment systems, rapid fraud detection (counts in minutes), integration of the software product into the business where payment systems and services are used (for example, payment integration services in financial institutions, online stores, logistics companies, insurance policies, trading platforms, etc.). It is determined that the implementation of an automated system should be considered as a project. The principles of project implementation are offered. It is established that for the rational implementation of the project it is necessary to develop a specific methodology for the implementation of the software product for fraud detection in payment systems of business institutions.