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arXiv · 2510.15232

FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain

Tiansheng Hu·Tongyan Hu·Liuyang Bai·Yilun Zhao·Arman Cohan·Chen Zhao
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Abstract

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.

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Tiansheng Hu, Tongyan Hu, Liuyang Bai, Yilun Zhao, Arman Cohan, Chen Zhao. 2025-10-17. FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain. https://arxiv.org/abs/2510.15232

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