Customer retention is a top concern in the banking industry, where poor churn prediction leads to massive revenue loss and lower customer loyalty. This paper solves the issue by proposing a hybrid deep learning model that combines Feedforward Neural Networks (FNN) and XGBoost to improve predictive accuracy and interpretability of churn prediction. For dimensionality reduction and emphasizing key predictors, the model uses LASSO regularization for feature selection. FNN handles customer information and computes churn probabilities, which are then boosted by XGBoost to obtain final classification. Model interpretability is also improved with SHAP (SHapley Additive exPlanations), providing a view into individual feature contribution and facilitating actionable customer retention strategies. The proposed model is tested on a 10,000 customer record dataset drawn from a retail bank and achieves superior performance with accuracy at 99.9% and ROC-AUC at 99.81%. These results substantially outperform a strong benchmark—calibrated LightGBM—on all standard metrics. The combination of deep learning, regularized feature selection, and explainability is a new solution to churn prediction in banking. This paper not only enhances classification performance but also facilitates informed decision-making, allowing financial institutions to harness interpretable and data-driven tools to actively manage customer relationships and reduce attrition risk.