@inproceedings{dou-etal-2026-finguard,
title = "{F}in{G}uard: Detecting Financial Regulatory Non-Compliance in {LLM} Interactions",
author = "Dou, Huaixia and
Zhu, Jie and
Wu, Minghao and
Jiang, Shuo and
Li, Junhui and
Guo, Lifan and
Chen, Feng and
Zhang, Chi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.629/",
doi = "10.18653/2026.findings-emnlp.629",
pages = "13213--13232",
ISBN = "979-8-89176-462-0",
abstract = "As large language models (LLMs) are increasingly deployed in financial services, a single non-compliant interaction can expose institutions to regulatory penalties and direct consumer harm. Existing guard models are built around general harm taxonomies and overlook violations grounded in specific financial regulations. We address this gap with a regulation-driven pipeline that operates directly on regulatory documents, inducing a financial compliance risk taxonomy and synthesizing grounded training data without any predefined violation categories. Instantiating the pipeline on Chinese financial regulations, we release $\textbf{FinGuard-Bench}$, to our knowledge the first benchmark for financial regulatory compliance detection, with expert-annotated labels at both the query and response levels. We further train $\textbf{FinGuard}$, a financial compliance detection model built on Qwen3-8B and trained on the regulation-grounded data via supervised fine-tuning and self-play reinforcement learning. On FinGuard-Bench, FinGuard substantially outperforms all baselines, including dedicated guard models and much larger general-purpose LLMs such as Qwen3.5-397B-A17B and GPT-5.1. Furthermore, FinGuard also preserves general safety capabilities and adapts to unseen institution-specific policies using policy documents alone. We will publicly release the code, prompts, and resources used in this work on GitHub."
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<abstract>As large language models (LLMs) are increasingly deployed in financial services, a single non-compliant interaction can expose institutions to regulatory penalties and direct consumer harm. Existing guard models are built around general harm taxonomies and overlook violations grounded in specific financial regulations. We address this gap with a regulation-driven pipeline that operates directly on regulatory documents, inducing a financial compliance risk taxonomy and synthesizing grounded training data without any predefined violation categories. Instantiating the pipeline on Chinese financial regulations, we release FinGuard-Bench, to our knowledge the first benchmark for financial regulatory compliance detection, with expert-annotated labels at both the query and response levels. We further train FinGuard, a financial compliance detection model built on Qwen3-8B and trained on the regulation-grounded data via supervised fine-tuning and self-play reinforcement learning. On FinGuard-Bench, FinGuard substantially outperforms all baselines, including dedicated guard models and much larger general-purpose LLMs such as Qwen3.5-397B-A17B and GPT-5.1. Furthermore, FinGuard also preserves general safety capabilities and adapts to unseen institution-specific policies using policy documents alone. We will publicly release the code, prompts, and resources used in this work on GitHub.</abstract>
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%0 Conference Proceedings
%T FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions
%A Dou, Huaixia
%A Zhu, Jie
%A Wu, Minghao
%A Jiang, Shuo
%A Li, Junhui
%A Guo, Lifan
%A Chen, Feng
%A Zhang, Chi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F dou-etal-2026-finguard
%X As large language models (LLMs) are increasingly deployed in financial services, a single non-compliant interaction can expose institutions to regulatory penalties and direct consumer harm. Existing guard models are built around general harm taxonomies and overlook violations grounded in specific financial regulations. We address this gap with a regulation-driven pipeline that operates directly on regulatory documents, inducing a financial compliance risk taxonomy and synthesizing grounded training data without any predefined violation categories. Instantiating the pipeline on Chinese financial regulations, we release FinGuard-Bench, to our knowledge the first benchmark for financial regulatory compliance detection, with expert-annotated labels at both the query and response levels. We further train FinGuard, a financial compliance detection model built on Qwen3-8B and trained on the regulation-grounded data via supervised fine-tuning and self-play reinforcement learning. On FinGuard-Bench, FinGuard substantially outperforms all baselines, including dedicated guard models and much larger general-purpose LLMs such as Qwen3.5-397B-A17B and GPT-5.1. Furthermore, FinGuard also preserves general safety capabilities and adapts to unseen institution-specific policies using policy documents alone. We will publicly release the code, prompts, and resources used in this work on GitHub.
%R 10.18653/2026.findings-emnlp.629
%U https://aclanthology.org/2026.findings-emnlp.629/
%U https://doi.org/10.18653/2026.findings-emnlp.629
%P 13213-13232
Markdown (Informal)
[FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions](https://aclanthology.org/2026.findings-emnlp.629/) (Dou et al., Findings 2026)
ACL
- Huaixia Dou, Jie Zhu, Minghao Wu, Shuo Jiang, Junhui Li, Lifan Guo, Feng Chen, and Chi Zhang. 2026. FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 13213–13232, Budapest, Hungary. Association for Computational Linguistics.