Optimized Deep Neural Network Based Predictive Model for Customer Attrition Analysis in the Banking Sector

Sandeep Kumar Hegde · Monica R. Mundada

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Customer churn and segmentation Data Mining and Machine Learning Applications AI and HR Technologies

Abstract

Background: In recent time with the growth of the technology and the business model, customer attrition analysis is considered as a very important metric which decides the revenues and profitability of the organization. It is applicable for all the business domains irrespective of the size of the business even including the start-ups. Because about 65% revenue for the organization comes from the existing customer. The goal of the customer attrition analysis is to predict the customer who is likely to exit or churn from the current business organization. In this research work, the literature review is carried out to explore the related work which has been already carried out in the field of customer attrition analysis. The literature review also focuses on some of the patents which are issued in the area of customer attrition or churn analysis. The goal of the research paper is to predict accurately the customer attrition rate in the Banking Sector. Objective: The main objective of this paper is to predict accurately the attrition rate in the Banking sector using an optimized deep feed-forward neural network. Methods: In the proposed work the predictive machine learning model is implemented using the optimized deep feed-forward neural network having five hidden layers in it. The model is trained using Adam optimizer algorithm to obtain the optimal accuracy. The Banking Churn data set is passed as input to the Optimized Deep Feed Forward Neural Network Model. In order to perform the comparative analysis, the same data set is passed as input to the other machine learning algorithm such as Decision Tree, Logistic Regression, Gaussian Naïve Bayes, and Artificial Neural Network. Results: The test results indicate that the proposed optimized deep feedforward neural Network model performed better in accuracy compared to existing machine learning techniques. Conclusion: The proposed optimized deep neural network model is an accurate model for customer attrition analysis in the Banking sector compared to the existing machine learning techniques.

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References

2016Developing a prediction model for customer churn from electronic banking services using data mining 2016Predicting Customer Churn in Banking Industry using Neural Networks 2017An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing 2017Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry 2016A model for evaluation of customer satisfaction with banking service quality in an uncertain environment 2018pRNN: A Recurrent Neural Network based Approach for Customer Churn Prediction in Telecommunication Sector

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2024Enhancing Customer Churn Prediction in the Banking Sector through Hybrid Segmented Models with Model-Agnostic Interpretability Techniques

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