Improved Customer Churn Estimation Using LSTM Networks

In the competitive landscape of financial services, the high cost of client acquisition makes retention a top strategic priority.This study addresses the limitations of conventional credit card churn prediction specifically rigid architectural constraints and inefficient categorical data processing by proposing a dynamic framework based on Long Short-Term Memory (LSTM) networks.By restructuring standard tabular datasets into"pseudo-sequences" and utilizing dense embeddings for high-cardinality features, we enable the model to capture nuanced temporal shifts in customer behavior.Our methodology evaluates four distinct LSTM configurations: Vanilla, Stacked, Bidirectional, and a hybrid Bidirectional-Stacked variant.By integrating these architectures into a unified ensemble, we achieved a peak classification accuracy of 92.35%.Beyond raw accuracy, the ensemble demonstrates exceptional recall performance.For banking institutions, this translates to a more reliable early-warning system that identifies at-risk accounts with precision, effectively reducing the revenue loss associated with undetected churn.