Bank Customer Behavior Analysis And Research Based On Data Mining

Bank customer behavior analysis is the process by which banks collect data and information about their customers,such as business behavior,customer characteristics,customer loyalty,needs and preferences,and then segment their customers based on the information and data obtained to provide personalized management strategies for them.Data mining algorithms are widely used in bank customer behavior analysis because they can extract patterns from a large amount of data.As a classical data mining algorithm,random forest has the advantages of strong generalization ability,high classification prediction accuracy,fast training speed and easy parallelization implementation,etc.It is very suitable for the scenario of bank customer credit classification and can help banks to conduct customer behavior analysis.As a combinatorial classifier algorithm,the overall classification performance of random forest is determined by the decision trees that are the base classifiers.Due to the random nature of the random forest construction process and the complexity of the training data set,some decision trees with poor classification performance and high similarity will be generated,affecting the overall classification performance of the model.In addition,when selecting decision trees in random forest,the existing performance evaluation metrics often fail to balance the diversity of decision trees and classification accuracy.Aiming at these problems and some shortcomings of existing methods,we conduct research on improved random forest-based customer credit classification methods in this thesis.Firstly,the theories related to bank customer behavior analysis and data mining algorithms are studied,and then a system division and comparative analysis are carried out on the application of data mining algorithms in bank customer behavior analysis,which can discover the association between data mining algorithms and customer behavior analysis and lay a theoretical foundation for subsequent research.To address the problems of the traditional random forest algorithm,a comprehensive performance index for evaluating decision trees is proposed,which can compromise between the diversity and classification accuracy of decision trees,making the performance evaluation index of decision trees more comprehensive and effectively screening the decision trees in the forest.On the basis of this comprehensive performance index,an improvement is made to the traditional random forest,and a random forest bank customer classification method that balances accuracy and diversity is proposed.The experimental results show that the method effectively improves the classification performance of the random forest model and can be flexibly applied to different customer credit classification scenarios.Finally,combining the theoretical research with the practice,a prototype system for customer credit rating based on the improved random forest is designed and implemented.The system adopts the improved random forest customer credit classification method proposed in this thesis,and includes four modules: data collection,data processing,model management and rating analysis.The test results of the system show that it can accomplish the task of customer credit classification very well.The research results of this thesis can provide new ideas for the research of data mining in the field of customer behavior analysis,and at the same time it can be put into practical application,which has high theoretical value and broad application prospects.