Predicting Customer Churn and Negative Equity Trends: Evaluating Machine Learning Models for Financial Stability and Market Insights in the USA

Abstract

Customer churn prediction and negative equity trend analysis are critical challenges in financial stability and market risk assessment. This study evaluates various machine learning (ML) models to enhance predictive accuracy in identifying potential customer attrition and forecasting negative equity trends in the U.S. housing market. Using real-world datasets from financial institutions and housing market reports, models such as Logistic Regression, Random Forest, Gradient Boosting, and Deep Learning architectures were assessed based on precision, recall, and explainability. The results indicate that deep learning models, particularly Multi-Layer Perceptron (MLP) for churn prediction and Long Short-Term Memory (LSTM) networks for negative equity forecasting, outperformed traditional ML techniques in predictive accuracy and robustness. SHAP analysis was utilized to enhance model interpretability, revealing key determinants such as loanto-value ratios, interest rate fluctuations, and customer transaction behavior. The study underscores the potential of AI-driven analytics in financial risk management, offering valuable insights for banking institutions, mortgage lenders, and policymakers. Future research should explore hybrid AI models integrating domain-specific heuristics to enhance predictive power further.