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

ArXiv ID: 2511.08588 “View on arXiv”

Authors: Lorenzo Carta, Fernando Spadea, Oshani Seneviratne

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.

Keywords: federated learning, consumer financial distress, interpretable AI, cross-silo FL, financial inclusion, Consumer Credit

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced distributed machine learning (federated learning) and interpretable AI techniques (SHAP, Owen values) but is grounded in a real-world dataset (NFCS) with detailed methodology, performance metrics, and a provided GitHub repository.
  flowchart TD
    A["Research Goal: Predict U.S. State-Level<br>Financial Distress via Federated Learning"] --> B["Data: National Financial Capability Study<br>50 States + DC"]
    B --> C["Method: Cross-Silo Federated Learning<br>Each State = Silo"]
    C --> D["Computations: Privacy-Preserving<br>Model Training & Weight Aggregation"]
    D --> E["Key Outcomes:<br>1. Global & Local Explanations<br>2. State-Specific Risk Patterns<br>3. Scalable FL Framework"]