# FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain

arXiv: https://arxiv.org/abs/2510.15232 · HTML: https://mlsanity.com/paper/2510.15232/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 29 of 29)

- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks](https://mlsanity.com/paper/2404.02151/) (2024)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge](https://mlsanity.com/paper/2410.02736/) (2024)
- [DeepInception: Hypnotize Large Language Model to Be Jailbreaker](https://mlsanity.com/paper/2311.03191/) (2023)
- [Societal Biases in Language Generation: Progress and Challenges](https://mlsanity.com/paper/2105.04054/) (2021)
- [Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration](https://mlsanity.com/paper/2402.00367/) (2024)
- [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://mlsanity.com/paper/2206.01347/) (2022)
- [Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty](https://mlsanity.com/paper/2401.06730/) (2024)
- [The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts](https://mlsanity.com/paper/2401.13136/) (2024)
- [CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists](https://mlsanity.com/paper/2403.18771/) (2024)
- [XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters](https://mlsanity.com/paper/2305.12002/) (2023)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [FinTextQA: A Dataset for Long-form Financial Question Answering](https://mlsanity.com/paper/2405.09980/) (2024)
- [DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents](https://mlsanity.com/paper/2311.09805/) (2023)
- [Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination](https://mlsanity.com/paper/2311.15548/) (2023)
- [FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains](https://mlsanity.com/paper/2311.09797/) (2023)
- [FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging](https://mlsanity.com/paper/2506.05828/) (2025)
- [What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis](https://mlsanity.com/paper/2212.11311/) (2022)
- [Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions](https://mlsanity.com/paper/2310.02431/) (2023)
- [FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents](https://mlsanity.com/paper/2411.05764/) (2024)
- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
- [SEC-QA: A Systematic Evaluation Corpus for Financial QA](https://mlsanity.com/paper/2406.14394/) (2024)
- [Course-Correction: Safety Alignment Using Synthetic Preferences](https://mlsanity.com/paper/2407.16637/) (2024)
- [Expect the Unexpected: FailSafe Long Context QA for Finance](https://mlsanity.com/paper/2502.06329/) (2025)
- [Open FinLLM Leaderboard: Towards Financial AI Readiness](https://mlsanity.com/paper/2501.10963/) (2025)

## Built on by (most cited first, 8 of 8)

- [FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments](https://mlsanity.com/paper/2601.07853/) (2026)
- [TradeTrap: Are LLM-based Trading Agents Truly Reliable and Faithful?](https://mlsanity.com/paper/2512.02261/) (2025)
- [Enhancing Trustworthiness with Mixed Precision: Benchmarks, Opportunities, and Challenges](https://mlsanity.com/paper/2511.22483/) (2025)
- [Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.07825/) (2026)
- [NextFund: A Unified Performance Tracking Platform for Agentic Portfolio Management](https://mlsanity.com/paper/2607.11141/) (2026)
- [Confidence Estimation for Financial Vision-Language Models in Chart and Document Understanding](https://mlsanity.com/paper/2608.06532/) (2026)
- [Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems](https://mlsanity.com/paper/2608.24069/) (2026)
- [A Citation-Grounded Benchmark for Trustworthy Earnings Call Transcript Analysis with Large Language Models](https://mlsanity.com/paper/2610.00969/) (2026)

## Abstract (arXiv)

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.