对比大模型与传统方法，发现后者在破产预测中更可靠
Are Foundation Models Useful for Bankruptcy Prediction?
- 用Llama-3和TabPFN对比经典机器学习模型
- XGBoost、CatBoost在所有时间窗口表现更好
- 大模型概率估计不可靠，适合专业人士参考
基础模型在金融领域展现出潜力，但其在企业破产预测中的有效性尚未系统评估。本文使用Llama-3.3-70B-Instruct和TabPFN，在包含超过一百万条公司记录的维斯格拉德集团数据集上进行破产预测研究。这是首次系统比较基础模型与传统机器学习基线在此任务上的表现。结果表明，XGBoost和CatBoost在所有预测时点均持续优于基础模型。基于大语言模型的方法存在不可靠的概率估计，限制了其在风险敏感型金融场景中的应用。尽管TabPFN在简单基线上表现可比，但其计算开销巨大，性能提升无法合理化成本。研究显示，当前基础模型虽具通用性，但在破产预测任务上仍不及专用方法。
原文摘要 · Abstract (English)
Foundation models have shown promise across various financial applications, yet their effectiveness for corporate bankruptcy prediction remains systematically unevaluated against established methods. We study bankruptcy forecasting using Llama-3.3-70B-Instruct and TabPFN, evaluated on large, highly imbalanced datasets of over one million company records from the Visegrád Group. We provide the first systematic comparison of foundation models against classical machine learning baselines for this task. Our results show that models such as XGBoost and CatBoost consistently outperform foundation models across all prediction horizons. LLM-based approaches suffer from unreliable probability estimates, undermining their use in risk-sensitive financial settings. TabPFN, while competitive with simpler baselines, requires substantial computational resources with costs not justified by performance gains. These findings suggest that, despite their generality, current foundation models remain less effective than specialized methods for bankruptcy forecasting.
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