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The paper introduces a framework for integrating explainable AI into CRM systems for customer churn prediction in telecommunications, benchmarking classifiers and using SHAP and LIME for interpretable predictions to enhance retention strategies.
This paper presents a hybrid architecture combining FT-Transformer with gradient-boosted trees via calibration-aware stacking for customer churn prediction on structured tabular data, achieving improved F1 and AUC-ROC on a public bank churn dataset.