AI for Economic, Financial and Insurance Risk: Bias, Narratives, and Forecasting with LLMs
Led by Dacheng Xiu, Professor of Econometrics and Statistics at the University of Chicago Booth School of Business, this new research project will run from 2026 to 2029.
In conjunction with the University of Chicago Booth School of Business, SCOR is backing a new research project titled AI for Economic, Financial and Insurance Risk: Bias, Narratives, and Forecasting with LLMs.
Large language models are rapidly entering economic and financial decision-making, but their use also introduces new forms of model risk. This project develops rigorous and practical tools to diagnose bias, simulate alternative risk narratives, and improve macro-financial forecasting with LLM-based features.
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The project is closely aligned with the SCOR Foundation's mission to deepen scientific understanding of risk and to support methods that are both rigorous and useful in practice.
For insurers and reinsurers, the work has direct benefits: tools to audit bias and instability in AI-assisted risk analysis, a framework to test how alternative policy or regulatory narratives may affect expectations and solvency perceptions, and forecasting methods that may detect macro-financial stress earlier than conventional approaches.
