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.

翻译：基础模型在各类金融应用中展现出潜力，但其在企业破产预测任务中的有效性尚未与成熟方法进行系统性评估。本研究使用Llama-3.3-70B-Instruct与TabPFN模型，基于维谢格拉德集团超过一百万条高度不平衡的企业记录数据集进行破产预测分析。我们首次系统性地比较了基础模型与此任务的经典机器学习基准方法。结果表明，XGBoost和CatBoost等模型在所有预测时间跨度上均持续优于基础模型。基于大语言模型的方法存在概率估计不可靠的问题，限制了其在风险敏感金融场景中的应用。TabPFN虽然能与简单基准模型竞争，但需要大量计算资源，其性能提升无法匹配相应的成本。这些发现表明，尽管当前基础模型具有通用性，但在破产预测领域仍不及专用方法有效。