User Analysis And Application Based On Data Mining Of Bank CRM System

The continuous development of computer technology and information technology brings us convenience and brings a series of problems.One of the problems is the explosive growth of data volume and the increasingly complex relationship between data.How to deal with these massive data,To explore hidden in the data of the potential theoretical value and practical value has become the focus of attention in all areas of life.Bank customer credit assessment has become the banking sector urgent need to solve the primary problem.China's customer credit evaluation started late,while the lack of professional personal credit database,leading to the bank's bad credit events have occurred.Personal bad assets has become the bank's normal business activities,the primary problem,has been related to the success or failure of China's commercial banks.This paper first analyzes the customer credit index system in detail,according to the actual life of the customer credit evaluation of the relevant factors.Secondly,the neural network is discussed in detail,and the BP neural network model is found.After analyzing the advantages and disadvantages of BP neural network,combined with the current popular genetic algorithm,a neural network based on genetic algorithm is designed.Through the GA-BP neural network designed in this paper,the customer's credit situation is classified,and the effective customer's credit rating is evaluated by the effective statistics of the classification,which provides a sufficient reference for the customer's future credit level.The research focus of this paper is that the weights and thresholds of the system can be effectively optimized by combining the genetic algorithm and the BP neural network.The two methods can be used alone,and the effective combination can improve the processing capability of the traditional neural network.Methods can also be complementary.Firstly,the BP neural network is described,and the concrete steps of using genetic algorithm to optimize the neural network are given,and the necessary data(training and debugging of the neural network)are obtained.Secondly,the network layout is analyzed to determine the necessary network data.Genetic neural network can also use genetic algorithm to improve the network coding,its function is calculated to determine the parameters.Using MATLAB to simulate the neural network model andgenetic neural model,we must also ensure the accuracy of the model.Followed by a summary of the paper,while pointing out the direction of future work.The system successfully put the design model into practice,preprocess the customer credit evaluation data,and the data modeling and simulation analysis through MATLAB,and give the corresponding genetic neural network to realize the code.Through the genetic neural network can be an individual personal credit situation for an effective assessment,and the credit situation for effective prediction.The neural network designed by this paper regulates the bank's credit behavior and reduces the credit risk for the bank.