A SPOT in the dark: using AI to assess financial stability risks

Domenic Kellner, Jan Hannes Lang, Marek Rusnák and Lukas Joseph Nagy

No 3262, Working Paper Series from European Central Bank

Abstract: Financial stability risks consist of two distinct components: vulnerabilities and possible trigger events. While there has been considerable progress regarding the measurement of vulnerabilities, the assessment of possible trigger events remains largely qualitative. To fill this gap, we employ Large Language Models to extract information about the Severity and Probability Of potential Trigger events (SPOT) from a large dataset of financial news articles over the period2005 – 2026. The SPOT indicator increases ahead of major historical trigger events, correctly identifies trigger sources, and helps to improve forward looking model estimates of downside risks to the economy. The results indicate that the use of AI-based signal extraction from text can be a promising avenue to improve the monitoring of financial stability risks. JEL Classification: C55, C88, E32, E44, G01

Keywords: artificial intelligence; crisis indicators; financial stability; growth-at-risk (search for similar items in EconPapers)
Date: 2026-07
Note: 2731285
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20263262

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