Paper: arXiv 2511.08588

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.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (21)

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  2. The FINRA Foundation’s National Financial Capability Study: Unpacking 12 years of data on US financial capability (2024)
  3. Communication-efficient learning of deep networks from decentralized data (2017)
  4. Fed-RD: Privacy-preserving federated learning for financial crime detection (2024)
  5. Federated machine learning: Concept and applications (2019)
  6. Federated Learning for Credit Risk Assessment. (2023)
  7. A novel federated learning approach with knowledge transfer for credit scoring (2024)
  8. Financial Data Analysis with Robust Federated Logistic Regression — → 2504.20250
and 13 more
  1. Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-score model (2017)
  2. Financial distress prediction models: a review of their usefulness 1 (2019)
  3. Using Machine Learning and Counterfactual Explanations for Financial Distress Prediction (2024)
  4. The many Shapley values for model explanation (2020)
  5. Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features (2023) — link
  6. Secure and transparent banking: explainable AI-driven federated learning model for financial fraud detection (2025)
  7. Robust integration of blockchain and explainable federated learning for automated credit scoring (2024)
  8. Adam: A method for stochastic optimization (2014) — link
  9. Access to Credit and Financial Health: Evaluating the Impact of Debt Collection (2017) — link
  10. The Number of Americans with Debt in Collections Fell during the Pandemic to 64 Million (2022) — link
  11. Flower: A friendly federated learning research framework (2020) — link
  12. Language modeling with highway LSTM (2017)
  13. Empower sequence labeling with task-aware neural language model (2018)

1 of 21 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.