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

Author

Listed:
  • Kellner, Domenic
  • Lang, Jan Hannes
  • Rusnák, Marek
  • Nagy, Lukas Joseph

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

Suggested Citation

  • Kellner, Domenic & Lang, Jan Hannes & Rusnák, Marek & Nagy, Lukas Joseph, 2026. "A SPOT in the dark: using AI to assess financial stability risks," Working Paper Series 3262, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20263262
    Note: 2731285
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    References listed on IDEAS

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    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises

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