@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://preview.aclanthology.org/ingest-acl/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."
}