B. Srikanth1,*, Swarajya Lakshmi V. Papineni2, Gutta Sridevi3, D. N. V. S. L. S. Indira4, K. S. R. Radhika5, Khasim Syed6
Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 21-34, 2022, DOI:10.32604/iasc.2022.022423
Abstract In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography,… More >
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