PSO-SVM Based Algorithm for Customer Churn Prediction in the Banking Industry

Customer churn is a major concern for the banking industry and there are many methods being investigated to predict whether a customer would potentially churn. To solve this problem, one such approach is the use of machine learning techniques combined with one of the widely used models Support Vecto...

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Bibliographic Details
Published in2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI) pp. 220 - 225
Main Authors Ponnusamy, Raja Rajeswari A-P, Rana, Muhammad Ehsan, Manickavasagam, Sarnesh A-L, Hameed, Vazeerudeen Abdul
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2023
Subjects
Banking
Big Data
Classification
Data Mining
Industries
Machine learning
Machine learning algorithms
Particle Swarm Optimization
Prediction algorithms
Support Vector Machine (VM)
Support vector machines
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Summary:Customer churn is a major concern for the banking industry and there are many methods being investigated to predict whether a customer would potentially churn. To solve this problem, one such approach is the use of machine learning techniques combined with one of the widely used models Support Vector Machine (SVM). However, SVM required the right set of hyperparameters to function optimally. This paper proposes the use of Particle Swarm Optimization (PSO) to find the optimal hyperparameters for the SVM model. The data used is obtained from Kaggle and several preprocessing techniques have been deployed to prepare the data. The results are compared with existing models that have used the same dataset. Based on the results, the proposed model obtained a significantly higher accuracy than existing SVM-based models.
DOI:10.1109/BDAI59165.2023.10257097