Hybrid CNN-LSTM Approach for Predicting and Analyzing Customer Churn in the Banking Sector

Customer attrition therefore becomes a major issue in the context of highly competitively, provider specific analysis in banking sector that affects the organizational profitability and its sustainable growth. Identifying those customers most likely to churn is strategic important for the establishment of timely customer retention strategies among banks. This research seeks to reduce customer attrition in the banking framework using a CNN-LSTM composite model. It uses CNN which, for its part, helps extract or learn features while using the LSTM networks for learning the temporal patterns because it also incorporates time and behavioral patterns in the customer data. Further, customer segmentation is applied in order to increase the precision of churn predictions made based on the different levels of tension found in specific customer segments. This hybrid architecture allows for a more comprehensive analytical approach to the customer data with the ability to identify various types of behavioral patterns and temporal changes that may impact churn. For churn prediction, the proposed framework incorporates a CNN-LSTM model. CNN layers work on temporal customer transaction data extracting crucial features; LSTM layers maintain long-term dependencies. The framework also applies segmentation analysis based on cluster analysis to categorize customers with regard to behaviour, age, and product consumption patterns. The model is developed in Python and tested using a real-word data involving customer data. Using churn-forecasting metric, the hybrid CNN-LSTM model achieved accuracy and robustness with an accuracy of 98.5%, precision of 97%, recall score of 95% and an F1-score of 97%. This performance enhanced the capability to capture spatial patterns and temporal dependencies of customer behaviour beyond the traditional models.