RETRACTED ARTICLE: Design and implementation of bank CRM system based on decision tree algorithm

• Published in: Neural computing & applications Vol. 33; no. 14; pp. 8237 - 8247
• Main Authors: Chen, Caixia, Geng, Liwei, Zhou, Sheng
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Probability and Statistics in Computer Science; S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications; Special Issue on Intelligent Computing Methodologies in Machine learning for IoT Applications; Decision tree algorithm; Information system; Data mining; Customer relationship management
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-04959-8

With the rapid development of the national economy, the level of informationization and office automation of banks has gradually increased. At the same time, with the development of bank management concepts, the traditional bank customer relationship management (CRM) method has been unable to meet the basic needs of bank development. The purpose of this paper is to design and implement a bank CRM system based on decision tree algorithm. This paper uses decision tree technology, data preprocessing, and other technologies for data mining. Based on the mining results, a customer relationship management system is designed based on the scalability and maintainability of the system. Finally, the system is designed, implemented, and performed functional tests. The experimental results show that the system can fully exploit the consumer demand and consumption habits of existing customers and improve customer satisfaction. In addition, it can adapt to the complex banking information system environment, with sufficient computing power and high accuracy can provide valuable information for bank decision makers. The data mining performance of the system was tested, and the time for running 1 million data volumes was 650 s. It can be seen that the system has excellent running performance.