ADVANCED CUSTOMER CHURN PREDICTION IN BANKING USING USING CLASSICAL AND QUANTUM LEARNING MODELS

Customer churn prediction has become a crucial task in the banking sector due to increasing competition and the
need for customer retention. This study presents a comprehensive approach for predicting customer churn using
Machine Learning (ML), Deep Learning (DL), and Quantum Machine Learning (QML) techniques. Traditional
statistical approaches often fail to capture complex customer behavior patterns. Therefore, this work utilizes
advanced algorithms such as Logistic Regression, Random Forest, Gradient Boosting, Artificial Neural Networks,
and Variational Quantum Classifiers.
The proposed system processes customer demographic, transactional, and behavioral data to predict churn
probability. Experimental results demonstrate that ensemble-based ML models and deep learning techniques
provide high predictive accuracy, while QML offers a future-oriented exploratory approach. The system enables
banks to identify high-risk customers and implement targeted retention strategies, thereby improving profitability
and customer satisfaction