Does Firm-Level AI Adoption Improve Early-Warning of Corporate Financial Distress? Evidence from Chinese Non-Financial Firms

Frederik Rech, Fanchen Meng, Hussam Musa, Martin \v{S}ebe\v{n}a and Siele Jean Tuo
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Frederik Rech: School of Economics, Beijing Institute of Technology, Beijing, China
Fanchen Meng: Faculty of Economics, Shenzhen MSU-BIT University, Shenzhen, China
Hussam Musa: Faculty of Economics, Matej Bel University, Bansk\'a Bystrica, Slovakia
Martin \v{S}ebe\v{n}a: Faculty of Arts and Social Sciences, Hong Kong Baptist University, Hong Kong, China
Siele Jean Tuo: Business School, Liaoning University, Shenyang, China

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Abstract: This study investigates whether firm-level artificial intelligence (AI) adoption improves the out-of-sample prediction of corporate financial distress models beyond traditional financial ratios. Using a sample of Chinese listed firms (2008-2023), we address sparse AI data with a novel pruned training window method, testing multiple machine learning models. We find that AI adoption consistently increases predictive accuracy, with the largest gains in recall rates for identifying distressed firms. Tree-based models and AI density metrics proved most effective. Crucially, models using longer histories outperformed those relying solely on recent "AI-rich" data. The analysis also identifies divergent adoption patterns, with healthy firms exhibiting earlier and higher AI uptake than distressed peers. These findings, while based on Chinese data, provide a framework for early-warning signals and demonstrate the broader potential of AI metrics as a stable, complementary risk indicator distinct from traditional accounting measures.

Date: 2025-12
New Economics Papers: this item is included in nep-ain, nep-sbm and nep-tid
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