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From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI

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  • Alex Kim
  • Maximilian Muhn
  • Valeri Nikolaev

Abstract

We explore the value of generative AI tools, such as ChatGPT, in helping investors uncover dimensions of corporate risk. We develop and validate firm-level measures of risk exposure to political, climate, and AI-related risks. Using the GPT 3.5 model to generate risk summaries and assessments from the context provided by earnings call transcripts, we show that GPT-based measures possess significant information content and outperform the existing risk measures in predicting (abnormal) firm-level volatility and firms' choices such as investment and innovation. Importantly, information in risk assessments dominates that in risk summaries, establishing the value of general AI knowledge. We also find that generative AI is effective at detecting emerging risks, such as AI risk, which has soared in recent quarters. Our measures perform well both within and outside the GPT's training window and are priced in equity markets. Taken together, an AI-based approach to risk measurement provides useful insights to users of corporate disclosures at a low cost.

Suggested Citation

  • Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023. "From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI," Papers 2310.17721, arXiv.org, revised Mar 2025.
    • Handle: RePEc:arx:papers:2310.17721
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    Cited by:

    1. Edward Li & Min Shen & Zhiyuan Tu & Dexin Zhou, 2024. "The Promise and Peril of Generative AI: Evidence from GPT as Sell-Side Analysts," Papers 2412.01069, arXiv.org, revised Oct 2025.
    2. Voraprapa Nakavachara & Tanapong Potipiti & Thanee Chaiwat, 2025. "Experimenting with Generative AI: Does ChatGPT Really Increase Everyone’s Productivity?," PIER Discussion Papers 229, Puey Ungphakorn Institute for Economic Research.
    3. Voraprapa Nakavachara & Tanapong Potipiti & Thanee Chaiwat, 2024. "Experimenting with Generative AI: Does ChatGPT Really Increase Everyone's Productivity?," Papers 2403.01770, arXiv.org.
    4. Christian Fieberg & Lars Hornuf & Maximilian Meiler & David J. Streich, 2025. "Using Large Language Models for Financial Advice," CESifo Working Paper Series 11666, CESifo.
    5. Correia, Maria, 2025. "Accounting and corporate failure: the evolving role of accounting information in bankruptcy prediction," LSE Research Online Documents on Economics 128340, London School of Economics and Political Science, LSE Library.
    6. Yikai Zhao & Jun Nagayasu & Xinyi Geng, 2024. "Measuring Climate Policy Uncertainty with LLMs: New Insights into Corporate Bond Credit Spreads," DSSR Discussion Papers 143, Graduate School of Economics and Management, Tohoku University.

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