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

Andrés Alonso-Robisco, Andrés Azqueta-Gavaldón, José Manuel Carbó, José Luis González, Ana Isabel Hernáez, José Luis Herrera, Jorge Quintana and Javier Tarancón
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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

No 2504, Occasional Papers from 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.

Keywords: suptech; natural language processing; machine learning; network analysis; sentiment (search for similar items in EconPapers)
JEL-codes: C63 D81 G21 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2025-03
New Economics Papers: this item is included in nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:bde:opaper:2504

DOI: 10.53479/39320

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