Abstract
Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.
Highlights
Customers in modern society have more choices
Customer relationship management is an important means for financial enterprises to gain competitive advantage
This paper uses the improved self-adaptive genetic neural network customer classification model, which can distinguish the customers with different purchasing tendencies of financial enterprises and provide financial enterprises with different customers as the target, differentiated, and effective marketing services, which reduces the cost and improves the operating profit and facilitates the financial customer relationship management of enterprises [3]
Summary
Customers in modern society have more choices. Customers’ needs have been personalized. It can provide ways and channels to analyze needs and understand how to improve product quality and service, so that financial institutions can tailor personalized needs for customers This framework uses various machine learning methods and technologies to classify emotions [5]. At the same time of competition, financial institutions have a series of documents and methods to control risks, the borrower’s intention is not obvious, which leads to a great reduction in the controllability of risks In view of this serious phenomenon, we study the method of machine learning to promote handwriting analysis, which reflects a certain degree of intention from the unique properties of handwriting, and the recognized handwriting association is helpful to map individuals to corresponding personality types. Comparing nearly 10,000 data of Internet financial companies with real financial institutions, it shows that its model is available and effective [7]
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