Application of Apriori Algorithm in Digital Financial Risk Information Management System

The current era belongs to the information era, with a wide variety of information data. However, we also see that the current financial management information system is often limited to the statistical analysis of data, and less work really involves prediction, decision-making and optimization. Therefore, it can only replace a part of heavy manual labor, and cannot fully play the role of financial management information system. Based on this complex situation, this text proposes the use research of Apriori algorithm in information management system. In financial risk (FR) information management system, Apriori algorithm can greatly reduce the complexity of calculation and improve the speed of calculation. It transforms transaction database into Boolean matrix for storage. This text uses the Apriori algorithm in the Statistical Package for the Social Sciences Clementine 12.0 software to implement association analysis. The research results show that when processing customer information, Apriori algorithm has the largest growth rate when the number of customer transactions is 9000, which is 164% higher than the traditional ones. When the number of customer transactions is 6000, with the increase of the minimum support (min-sup), the execution efficiency (EE for short here) of both the traditional algorithm and Apriori algorithm is declining, but the decline trend of the operation efficiency of the traditional algorithm is greater than that of the Apriori algorithm proposed in this text, which verifies its stable performance. Therefore, the Apriori algorithm studied in this text provides a pillar for the use of FR information management system.