AI-based Customer Churn Prediction for Financial Institutions: Algorithms, Applications and Challenges

Feng Chen ()
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Feng Chen: Beijing Normal University - Hong Kong Baptist University United International College, Faculty of Science and Technology

A chapter in Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024), 2025, pp 852-858 from Springer

Abstract: Abstract In today’s competitive financial landscape, customer loyalty is vital for the success of financial institutions. However, the growth of the finance market has led to increased customer churn, where clients switch to competitors for better services. Predicting customer churn is essential for financial institutions to retain clients and maintain revenues, as customer churn can also harm brand image and market share. This paper explores various methodologies, particularly in the field of artificial intelligence (AI), to effectively predict customer churn. Traditional methods like Logistic Regression (LR) are widely used but may struggle with complex relationships, making them less effective. In contrast, Decision-tree-based algorithms and Artificial Neural Networks (ANN) offer improved accuracy but struggling with challenges regarding interpretability and computational costs. By utilizing historical data and advanced machine learning techniques, institutions can identify potential churn patterns. This paper discusses the advantages and limitations of different predictive models, emphasizing the need for transparent and interpretable methods. Additionally, it highlights future prospects such as SHAP and LIME for model interpretability, federated learning for privacy, and transfer learning for better adaptability across different contexts. The findings underscore the importance of integrating AI into churn prediction strategies to enhance customer retention and institutional profitability.

Keywords: Customer Churn Prediction; Machine Learning; Business (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-652-9_91

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