Open Access

ARTICLE

Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

by Fang Yu¹, Wenbin Bi², Ning Cao^3,4,*, Hongjun Li¹, Russell Higgs⁵

1 College of Information Engineering, Qingdao Binhai University, Qingdao, 266555, China
2 School of Computer and Software, Dalian Neusoft University of Information, Dalian, 116023, China
3 College of Information Engineering, Shandong Vocational and Technical University of International Studies, Rizhao, 276826, China
4 School of Intergrated Circuit, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
5 School of Mathematics and Statistics, University College Dublin, Dublin, Ireland

* Corresponding Author: Ning Cao. Email:

Computer Systems Science and Engineering 2023, 47(1), 1-17. https://doi.org/10.32604/csse.2023.018349

Received 06 March 2021; Accepted 01 May 2021; Issue published 26 May 2023

Abstract

In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation, at the smart contract level of the blockchain, a customer churn prediction framework based on situational awareness and integrating customer attributes, the impact of project hotspots on customer interests, and customer satisfaction with the project has been built. This framework introduces the background factors in the financial customer environment, and further discusses the relationship between customers, the background of customers and the characteristics of pre-lost customers. The improved Singular Value Decomposition (SVD) algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers, and the key index combination is screened to obtain the data of potential lost customers. The framework will change with time according to the customer’s interest, adding the time factor to the customer churn prediction, and improving the dimensionality reduction and prediction generalization ability in feature selection. Logistic regression, naive Bayes and decision tree are used to establish a prediction model in the experiment, and it is compared with the financial customer churn prediction framework under situational awareness. The prediction results of the framework are evaluated from four aspects: accuracy, accuracy, recall rate and F-measure. The experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends, so as to obtain potential customer data with high churn probability, and then these data can be transmitted to the company’s customer service department in time, so as to improve customer churn rate and customer loyalty through accurate service.

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Keywords

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