@inproceedings{xie-etal-2026-finchain,
title = "{F}in{C}hain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning",
author = "Xie, Zhuohan and
Orel, Daniil and
Thareja, Rushil and
Sahnan, Dhruv and
Madmoun, Hachem and
Zhang, Fan and
Banerjee, Debopriyo and
Georgiev, Georgi Nenkov and
Peng, Xueqing and
Qian, Lingfei and
Huang, Jimin and
Su, Jinyan and
Singh, Aaryamonvikram and
Xing, Rui and
Elbadry, Rania and
Xu, Chen and
Li, Haonan and
Koto, Fajri and
Koychev, Ivan and
Chakraborty, Tanmoy and
Wang, Yuxia and
Lahlou, Salem and
Stoyanov, Veselin and
Ananiadou, Sophia and
Nakov, Preslav",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.662/",
pages = "14529--14553",
ISBN = "979-8-89176-390-6",
abstract = "Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing datasets such as FinQA and ConvFinQA emphasize final numerical answers while neglecting the intermediate reasoning steps required for transparency and verification. To address this gap, we introduce FinChain, the first benchmark specifically designed for verifiable Chain-of-Thought evaluation in finance. FinChain spans 58 topics across 12 financial domains, each represented by parameterized symbolic templates with executable Python code that enable fully machine-verifiable reasoning and scalable, contamination-free data generation.To assess reasoning capacity, we propose ChainEval, a dynamic alignment measure that jointly evaluates both the final-answer correctness and the step-level reasoning consistency. Our evaluation of 26 leading LLMs reveals that even frontier LLMs exhibit clear limitations in symbolic financial reasoning, while domain-adapted and math-enhanced fine-tuned models can substantially narrow this gap.Overall, FinChain exposes persistent weaknesses in multi-step financial reasoning and provides a foundation for developing trustworthy, interpretable, and verifiable financial AI. This project is available at https://github.com/mbzuai-nlp/finchain.git."
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<abstract>Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing datasets such as FinQA and ConvFinQA emphasize final numerical answers while neglecting the intermediate reasoning steps required for transparency and verification. To address this gap, we introduce FinChain, the first benchmark specifically designed for verifiable Chain-of-Thought evaluation in finance. FinChain spans 58 topics across 12 financial domains, each represented by parameterized symbolic templates with executable Python code that enable fully machine-verifiable reasoning and scalable, contamination-free data generation.To assess reasoning capacity, we propose ChainEval, a dynamic alignment measure that jointly evaluates both the final-answer correctness and the step-level reasoning consistency. Our evaluation of 26 leading LLMs reveals that even frontier LLMs exhibit clear limitations in symbolic financial reasoning, while domain-adapted and math-enhanced fine-tuned models can substantially narrow this gap.Overall, FinChain exposes persistent weaknesses in multi-step financial reasoning and provides a foundation for developing trustworthy, interpretable, and verifiable financial AI. This project is available at https://github.com/mbzuai-nlp/finchain.git.</abstract>
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%0 Conference Proceedings
%T FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning
%A Xie, Zhuohan
%A Orel, Daniil
%A Thareja, Rushil
%A Sahnan, Dhruv
%A Madmoun, Hachem
%A Zhang, Fan
%A Banerjee, Debopriyo
%A Georgiev, Georgi Nenkov
%A Peng, Xueqing
%A Qian, Lingfei
%A Huang, Jimin
%A Su, Jinyan
%A Singh, Aaryamonvikram
%A Xing, Rui
%A Elbadry, Rania
%A Xu, Chen
%A Li, Haonan
%A Koto, Fajri
%A Koychev, Ivan
%A Chakraborty, Tanmoy
%A Wang, Yuxia
%A Lahlou, Salem
%A Stoyanov, Veselin
%A Ananiadou, Sophia
%A Nakov, Preslav
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F xie-etal-2026-finchain
%X Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing datasets such as FinQA and ConvFinQA emphasize final numerical answers while neglecting the intermediate reasoning steps required for transparency and verification. To address this gap, we introduce FinChain, the first benchmark specifically designed for verifiable Chain-of-Thought evaluation in finance. FinChain spans 58 topics across 12 financial domains, each represented by parameterized symbolic templates with executable Python code that enable fully machine-verifiable reasoning and scalable, contamination-free data generation.To assess reasoning capacity, we propose ChainEval, a dynamic alignment measure that jointly evaluates both the final-answer correctness and the step-level reasoning consistency. Our evaluation of 26 leading LLMs reveals that even frontier LLMs exhibit clear limitations in symbolic financial reasoning, while domain-adapted and math-enhanced fine-tuned models can substantially narrow this gap.Overall, FinChain exposes persistent weaknesses in multi-step financial reasoning and provides a foundation for developing trustworthy, interpretable, and verifiable financial AI. This project is available at https://github.com/mbzuai-nlp/finchain.git.
%U https://aclanthology.org/2026.acl-long.662/
%P 14529-14553
Markdown (Informal)
[FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning](https://aclanthology.org/2026.acl-long.662/) (Xie et al., ACL 2026)
ACL
- Zhuohan Xie, Daniil Orel, Rushil Thareja, Dhruv Sahnan, Hachem Madmoun, Fan Zhang, Debopriyo Banerjee, Georgi Nenkov Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Jinyan Su, Aaryamonvikram Singh, Rui Xing, Rania Elbadry, Chen Xu, Haonan Li, Fajri Koto, Ivan Koychev, Tanmoy Chakraborty, Yuxia Wang, Salem Lahlou, Veselin Stoyanov, Sophia Ananiadou, and Preslav Nakov. 2026. FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14529–14553, San Diego, California, United States. Association for Computational Linguistics.