Advanced Customer Lifetime Value Predictive Model

Design a comprehensive predictive model for customer lifetime value (CLV) in a banking context using cross-functional data sources. Develop a methodology that integrates transactional history, demographic data, behavioral patterns, and risk scoring. Create a modular framework that can handle non-linear relationships, account for seasonal variations, and provide confidence intervals for CLV predictions. Include specific techniques for handling missing data, feature engineering, and model interpretability using techniques like SHAP values.