Large Language Models for Corporate Financial Distress Prediction: Overview and Exploration

With the increasing complexity of the business environment and the evolution of information disclosure tools, financial distress prediction (FDP) is gradually transforming from structured data-driven to semantic information fusion. Traditional models rely on financial ratios and statistical indicators, which make it difficult to capture risk propensity in “soft signals” such as management tone and textual metaphors. And the existing large language models (LLMs) provide a new perspective for the text-driven FDP system by the excellent semantic modeling and inference generation capabilities. This paper systematically sorts out the application path of LLMs in FDP by focusing on variable construction and model construction. Three types of representative text features, namely, emotional tone, semantic embedding, and generative variables, are summarized. The modeling mechanism analyzes LLMs as categorical predictive models and their fusion patterns in multimodal integrated systems. In addition, this work points out that there are still challenges such as scarce data labels, non-interpretable models, high cost of system deployment and lack of compliance mechanisms in existing studies, which urgently requires the evolution towards an intelligent early warning system with high credibility, transparency and adaptability under the synergistic promotion of multidisciplinary efforts. This work will provide a cutting-edge reference for constructing intelligent risk control systems and developing financial regulatory technology.