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

TL;DR

This paper introduces an interpretable federated learning framework for predicting financial distress across U.S. states, preserving privacy and identifying key predictors without centralizing sensitive data.

Contribution

It is the first to apply federated learning to nationwide financial distress modeling with explainability, handling highly categorical, imbalanced survey data.

Findings

Successful implementation of FL across 50 states and D.C.

Identification of global and state-specific predictors of financial hardship.

Scalable, regulation-compliant early warning system for financial distress.

Abstract

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.