Abstract
Recently, banks are constantly facing the problem of customers churning. Customer churn not only leads to a decline in bank funds and profits but also reduces its credit capacity and affects the bank’s operational management. As an important component of Customer Relationship Management, predicting customer churn has been increasingly urgent. Inspired by biological neurons, we build up a dendritic neural regression model (DNRM) with four layers, namely the synaptic layer, the dendritic layer, the membrane layer, and the soma layer for bank customer churn prediction. To pursue better prediction performance in this experiment, the Chicken Swarm Optimization (CSO) algorithm is defined as the training algorithm of DNRM. With the ability to balance exploration and exploitation, CSO is implemented to optimize and improve the accuracy of the DNRM. In this paper, we propose a novel dendritic neural regression model called CSO-DNRM for churn prediction, and the experimental results are based on a benchmark dataset from Kaggle. Compared with other algorithms and models, our proposed model obtains the highest accuracy of 92.27% and convergence speed in customer churn prediction. Due to the novel bionic algorithms and the pruning function of the model, it is evident that our proposed model has advantages in accuracy and computational speed in the field of customer churn prediction and can be widely applied in commercial bank customer relationship management.
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Acknowledgements
This research was supported by the Social Science Fund Project of Hunan Province, China (Grant No. 20YBA260), the Natural Science Fund Project of Changsha, China (Grant No. kq2202297), and Key Project of Hunan Provincial Department of Education: Research on Credit Risk Management of Supply Chain Finance Based on Adaptive Dendritic Neural Network Model (Project No. 22A0178).
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Wang, Q., Zhang, H., Ji, J., Tang, C., Tang, Y. (2024). Dendritic Neural Regression Model Trained by Chicken Swarm Optimization Algorithm for Bank Customer Churn Prediction. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1969. Springer, Singapore. https://doi.org/10.1007/978-981-99-8184-7_20
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DOI: https://doi.org/10.1007/978-981-99-8184-7_20
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