IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2511.08588.html

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

Author

Listed:
  • 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.

Suggested Citation

  • Lorenzo Carta & Fernando Spadea & Oshani Seneviratne, 2025. "Explainable Federated Learning for U.S. State-Level Financial Distress Modeling," Papers 2511.08588, arXiv.org.
    • Handle: RePEc:arx:papers:2511.08588
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2511.08588
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kun Yang & Nikhil Krishnan & Sanjeev R. Kulkarni, 2025. "Financial Data Analysis with Robust Federated Logistic Regression," Papers 2504.20250, arXiv.org.
    2. Saif Khalifa Aljunaid & Saif Jasim Almheiri & Hussain Dawood & Muhammad Adnan Khan, 2025. "Secure and Transparent Banking: Explainable AI-Driven Federated Learning Model for Financial Fraud Detection," JRFM, MDPI, vol. 18(4), pages 1-26, March.
    3. Julia Fonseca & Katherine Strair & Basit Zafar, 2017. "Access to credit and financial health: evaluating the impact of debt collection," Staff Reports 814, Federal Reserve Bank of New York.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Romeo, Charles & Sandler, Ryan, 2021. "The effect of debt collection laws on access to credit," Journal of Public Economics, Elsevier, vol. 195(C).
    2. Johannes Kriebel & Kevin Yam, 2020. "Forecasting recoveries in debt collection: Debt collectors and information production," European Financial Management, European Financial Management Association, vol. 26(3), pages 537-559, June.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2511.08588. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.