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Empowering financial supervision: a SupTech experiment using machine learning in an early warning system

Author

Listed:
  • Andrés Alonso-Robisco

    (Banco de España)

  • Andrés Azqueta-Gavaldón

    (Banco de España)

  • José Manuel Carbó

    (Banco de España)

  • José Luis González

    (Banco de España)

  • Ana Isabel Hernáez

    (Banco de España)

  • José Luis Herrera

    (Banco de España)

  • Jorge Quintana

    (Banco de España)

  • Javier Tarancón

    (Banco de España)

Abstract

New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the corporate level to detect sentiment anomalies for specific corporations (idiosyncratic risks), while employing a wide range of network metrics to monitor systemic risks. In the realm of supervisory technology (SupTech), anomaly detection in sentiment analysis serves as a proactive tool for financial authorities. By continuously monitoring sentiment trends, SupTech applications can provide early warnings of potential financial distress or systemic risks.

Suggested Citation

  • Andrés Alonso-Robisco & Andrés Azqueta-Gavaldón & José Manuel Carbó & José Luis González & Ana Isabel Hernáez & José Luis Herrera & Jorge Quintana & Javier Tarancón, 2025. "Empowering financial supervision: a SupTech experiment using machine learning in an early warning system," Occasional Papers 2504, Banco de España.
    • Handle: RePEc:bde:opaper:2504
    DOI: https://doi.org/10.53479/39320
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    References listed on IDEAS

    as
    1. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    2. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2015. "Systemic Risk and Stability in Financial Networks," American Economic Review, American Economic Association, vol. 105(2), pages 564-608, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    suptech; natural language processing; machine learning; network analysis; sentiment;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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