A Comprehensive Analysis of Classification Methods for Churn Prediction in Financial Services

Year : 2025 | Volume : 12 | Issue : 02 | Page : 47 61
    By

    Chintal Kumar Patel,

  1. Associate Professor, Department of Computer Science and Engineering, Geetanjali Institute of Technical Studies, Dabok, Rajasthan, India

Abstract

Persistent issues that affect long-term revenue in the banking sector include excessive client attrition. Customary churn models depend on measures related to customer satisfaction, which often result in low predictive accuracy due to their subjective nature. This study proposes an effective early warning model to address customer churn in financial services. Data is preprocessed through cleaning, one-hot encoding, Z-score normalization, and Min-max scaling. To handle class imbalance, the SMOTE algorithm is applied, improving prediction reliability. Feature engineering is also conducted to boost model effectiveness. The LightGBM (LGBM) Classifier is selected for its high performance, achieving 91% accuracy, 93% precision, 88% recall, and a 90% F1-score. It further demonstrates robustness with an AUC of 0.96 and a confusion matrix showing 1,464 true negatives and 1,386 true positives. The superiority of LGBM is confirmed by a comparison with Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) models on all measures. The results highlight how effective LGBM may be as a churn prediction tool, allowing banks to establish prompt and focused client retention plans.

Keywords: Customer churn, banking industry, financial services, churn prediction, LightGBM classifier, machine learning, SMOTE, feature engineering, model evaluation, customer retention strategies

[This article belongs to Journal of Advanced Database Management & Systems ]

How to cite this article:
Chintal Kumar Patel. A Comprehensive Analysis of Classification Methods for Churn Prediction in Financial Services. Journal of Advanced Database Management & Systems. 2025; 12(02):47-61.
How to cite this URL:
Chintal Kumar Patel. A Comprehensive Analysis of Classification Methods for Churn Prediction in Financial Services. Journal of Advanced Database Management & Systems. 2025; 12(02):47-61. Available from: https://journals.stmjournals.com/joadms/article=2025/view=228307


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 19/01/2025
Accepted 01/07/2025
Published 27/09/2025
Publication Time 251 Days
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