# Are Foundation Models Useful for Bankruptcy Prediction?

arXiv: https://arxiv.org/abs/2511.16375 · HTML: https://mlsanity.com/paper/2511.16375/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities](https://mlsanity.com/paper/2502.17361/) (2025)
- [TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems](https://mlsanity.com/paper/2502.02527/) (2025)
- [Kronos: A Foundation Model for the Language of Financial Markets](https://mlsanity.com/paper/2508.02739/) (2025)
- [FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models](https://mlsanity.com/paper/2308.00065/) (2023)
- [FinCast: A Foundation Model for Financial Time-Series Forecasting](https://mlsanity.com/paper/2508.19609/) (2025)
- [Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges](https://mlsanity.com/paper/2507.18577/) (2025)
- [From Numbers to Words: Multi-Modal Bankruptcy Prediction Using the ECL Dataset](https://mlsanity.com/paper/2401.12652/) (2024)
- [Multimodal Generative Models for Bankruptcy Prediction Using Textual Data](https://mlsanity.com/paper/2211.08405/) (2022)

## Built on by (most cited first, 2 of 2)

- [Evaluating LLMs in Finance Requires Explicit Bias Consideration](https://mlsanity.com/paper/2602.14233/) (2026)
- [TabPATE: Differentially Private Tabular In-Context Learning Without Public Data](https://mlsanity.com/paper/2606.31474/) (2026)

## Abstract (arXiv)

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 Visegr'ad 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.