Credit card attrition: an overview of machine learning and deep learning techniques

Abstract

Credit card churn, where customers close their credit card accounts, is a major problem for banks and other financial institutions. Being able to accurately predict churn can allow companies to take proactive steps to retain valuable customers. In this review, we examine how machine learning and deep learning techniques can be applied to forecast credit card churn. We first provide background on credit card churn and explain why it is an important problem. Next, we discuss common machine learning algorithms that have been used for churn forecasting, including logistic regression, random forests, and gradient boosted trees. We then explain how deep learning methods like neural networks and sequence models can capture more complex patterns from customer data. The available input features for churn models are also reviewed in detail. We compare the performance of different modeling techniques based on past research. Finally, we discuss open challenges and future directions for predictive churn modeling using machine learning and deep learning. Our review synthesizes key research in this domain and highlights opportunities for advancing the state-of-the-art. More robust churn forecasting can enable companies to take targeted action to improve customer retention.

Keywords

• churn prediction; logistic regression; decision trees; random forests; neural networks