Published Paper Details:

Customer attrition within banking institutions severely affects both financial performance and customer retention rates. The research implements an Artificial Neural Network (ANN) predictive model which utilizes historical purchases and individual data profiles alongside behavioural information to identify customer attrition. The methodology contains a sequence of operations starting from data pre-processing followed by exploratory analysis then leads to ANN model training and hyperparameter optimization. The predictive model succeeds at a rate of 81% which proves its superior performance over conventional approaches. Banking institutions can develop enhanced retention strategies by obtaining proactive identification of their most at-risk customers which enables them to offer tailored offers and enhance their service delivery. This system supports scalability while also permitting connectivity to Customer Relationship Management platforms which enable real-time predictions. The study demonstrates how data-based strategies generate high impact on both customer retention and churn reduction within banking institutions.