Explainable Federated Learning for U.S. State-Level Financial Distress Modeling

The recent application of federated learning (FL) to the U.S. National Financial Capability Study marks a significant advancement in financial distress modeling. By treating each state as a distinct data silo, this approach allows for the prediction of consumer financial distress without the need to centralize sensitive data. The integration of explainable AI techniques enables the identification of both nationwide and state-specific predictors of financial hardship, such as interactions with debt collection agencies. This machine learning model is tailored for highly categorical and imbalanced survey data, providing a robust framework for early warning systems in finance. The implications of this work extend beyond mere prediction; it offers a scalable, regulation-compliant blueprint that can enhance financial inclusion and consumer credit risk management, demonstrating how FL can facilitate socially responsible AI applications in the financial sector.