Explainable Federated Learning for U.S. State-Level Financial Distress Modeling
This work provides a scalable, regulation-compliant blueprint for early warning systems in finance, addressing financial inclusion and credit risk for consumers and policymakers.
The paper tackles the problem of predicting consumer financial distress across U.S. states without centralizing sensitive data by applying federated learning to the National Financial Capability Study, resulting in an interpretable framework that identifies both global and local predictors like debt collection contact.
We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financial distress across all 50 states and the District of Columbia without centralizing sensitive data. Our cross-silo FL setup treats each state as a distinct data silo, simulating real-world governance in nationwide financial systems. Unlike prior work, our approach integrates two complementary explainable AI techniques to identify both global (nationwide) and local (state-specific) predictors of financial hardship, such as contact from debt collection agencies. We develop a machine learning model specifically suited for highly categorical, imbalanced survey data. This work delivers a scalable, regulation-compliant blueprint for early warning systems in finance, demonstrating how FL can power socially responsible AI applications in consumer credit risk and financial inclusion.