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Informative Article | Finance | India | Volume 12 Issue 6, June 2023 | Rating: 5.1 / 10
Comparative Analysis of Machine Learning Algorithms for Bank Customer Churn Prediction
Karthika Gopalakrishnan
Abstract: This paper investigates the application of Machine Learning (ML) algorithms for predicting customer churn in the banking industry. Customer churn, signifying customer defection to competitors, stands as a significant hurdle to bank growth and profitability. By proactively identifying at-risk customers, banks can implement retention strategies to mitigate churn. We present a case study employing a bank customer churn dataset. Four prominent ML algorithms - Random Forest, Support Vector Machine (SVM), Decision Trees, and XGBoost - are utilized to predict churn. A comparative analysis is conducted to assess the performance of these algorithms using metrics like accuracy, precision, recall, and F1-score. The results emphasize the efficacy of ML in churn prediction compared to traditional methods. We conclude by outlining potential areas for future research.
Keywords: Churn Prediction, SVM, Banking Industry, XGBoost, Decision Trees, Machine Learning Automation
Edition: Volume 12 Issue 6, June 2023,
Pages: 2980 - 2983