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  • Open Access


    Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-CatBoost

    Yaling Xu, Congjun Rao*, Xinping Xiao, Fuyan Hu*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2715-2742, 2023, DOI:10.32604/cmes.2023.029023

    Abstract As the banking industry gradually steps into the digital era of Bank 4.0, business competition is becoming increasingly fierce, and banks are also facing the problem of massive customer churn. To better maintain their customer resources, it is crucial for banks to accurately predict customers with a tendency to churn. Aiming at the typical binary classification problem like customer churn, this paper establishes an early-warning model for credit card customer churn. That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm (GSA) and an Improved Beetle Antennae Search (IBAS) is proposed to… More > Graphic Abstract

    Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-CatBoost

  • Open Access


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

    Fang Yu1, Wenbin Bi2, Ning Cao3,4,*, Hongjun Li1, Russell Higgs5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1-17, 2023, DOI:10.32604/csse.2023.018349

    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… More >

  • Open Access


    Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

    Nagaraju Jajam, Nagendra Panini Challa*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 977-997, 2023, DOI:10.32604/cmc.2023.036098

    Abstract In the insurance sector, a massive volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining existing ones. The success of retention initiatives is determined not only by the accuracy of forecasting churners but also by the timing of the forecast. Previous works on churn forecast presented models for anticipating churn quarterly or monthly with an emphasis on customers’ static behavior. This paper’s objective is to calculate daily churn based on dynamic variations in client… More >

  • Open Access


    Research on Early Warning of Customer Churn Based on Random Forest

    Zizhen Qin, Yuxin Liu, Tianze Zhang*

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 143-154, 2022, DOI:10.32604/jai.2022.031843

    Abstract With the rapid development of interest rate market and big data, the banking industry has shown the obvious phenomenon of “two or eight law”, 20% of the high quality customers occupy most of the bank’s assets, how to prevent the loss of bank credit card customers has become a growing concern for banks. Therefore, it is particularly important to establish a customer churn early warning model. In this paper, we will use the random forest method to establish a customer churn early warning model, focusing on the churn of bank credit card customers and predicting… More >

  • Open Access


    Social Opinion Network Analytics in Community Based Customer Churn Prediction

    Ayodeji O. J Ibitoye1,*, Olufade F. W Onifade2

    Journal on Big Data, Vol.4, No.2, pp. 87-95, 2022, DOI:10.32604/jbd.2022.024533

    Abstract Community based churn prediction, or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer’s opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn… More >

  • Open Access


    Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

    Vani Haridasan*, Kavitha Muthukumaran, K. Hariharanath

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3531-3544, 2023, DOI:10.32604/iasc.2023.030628

    Abstract Customer retention is one of the challenging issues in different business sectors, and various firms utilize customer churn prediction (CCP) process to retain existing customers. Because of the direct impact on the company revenues, particularly in the telecommunication sector, firms are needed to design effective CCP models. The recent advances in machine learning (ML) and deep learning (DL) models enable researchers to introduce accurate CCP models in the telecommunication sector. CCP can be considered as a classification problem, which aims to classify the customer into churners and non-churners. With this motivation, this article focuses on… More >

  • Open Access


    Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

    Olfat M. Mirza1, G. Jose Moses2, R. Rajender3, E. Laxmi Lydia4, Seifedine Kadry5, Cheadchai Me-Ead6, Orawit Thinnukool7,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3757-3769, 2022, DOI:10.32604/cmc.2022.030428

    Abstract Presently, customer retention is essential for reducing customer churn in telecommunication industry. Customer churn prediction (CCP) is important to predict the possibility of customer retention in the quality of services. Since risks of customer churn also get essential, the rise of machine learning (ML) models can be employed to investigate the characteristics of customer behavior. Besides, deep learning (DL) models help in prediction of the customer behavior based characteristic data. Since the DL models necessitate hyperparameter modelling and effort, the process is difficult for research communities and business people. In this view, this study designs More >

  • Open Access


    A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

    Soban Arshad1, Khalid Iqbal1,*, Sheneela Naz2, Sadaf Yasmin1, Zobia Rehman2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4283-4301, 2022, DOI:10.32604/cmc.2022.025442

    Abstract Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world large-scale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data… More >

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