Recent LLMs have demonstrated promising ability in solving finance related problems. However, applying LLMs in real-world finance application remains challenging due to its high risk and high stakes property. This paper introduces FinTrust, a comprehensive benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications. Our benchmark focuses on a wide range of alignment issues based on practical context and features fine-grained tasks for each dimension of trustworthiness evaluation. We assess eleven LLMs on FinTrust and find that proprietary models like o4-mini outperforms in most tasks such as safety while open-source models like DeepSeek-V3 have advantage in specific areas like industry-level fairness. For challenging task like fiduciary alignment and disclosure, all LLMs fall short, showing a significant gap in legal awareness. We believe that FinTrust can be a valuable benchmark for LLMs' trustworthiness evaluation in finance domain.
@article{arxiv.2510.15232,
title = {FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain},
author = {Tiansheng Hu and Tongyan Hu and Liuyang Bai and Yilun Zhao and Arman Cohan and Chen Zhao},
journal= {arXiv preprint arXiv:2510.15232},
year = {2025}
}