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 optimize the parameters of the… More >
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