Abnormal Payment Transaction Detection Scheme Based on Scalable Architecture and Redis Cluster

Abstract

Log file based data analysis methods in the closed fault tolerant OS have shown several problems. First, it is not easy to add or change the data analysis direction while the service is running after the analysis process has been set and compiled. Second, in an independent closed system, due to the limited resource policy, it is difficult to perform real-time data analysis. Finally, it is not easy to utilize new technologies and open sources such as in-memory database and python. Due to these problems, existing methods have difficulty in detecting abnormal payment transactions in real time. In this paper, we propose an abnormal payment transaction detection scheme based on scalable network architecture and Redis cluster, which can collect transaction data quickly and perform their analysis in real-time. We show its performance by implementing a prototype system and performing several experiments on it. Furthermore, we show that our proposed scheme can be used for data analysis through the reproduction of data using in-memory storage, which can solve the aforementioned problem of unidirectional analysis by doing parallel processing on the distributed Redis repository.