@inproceedings{chen-etal-2026-risk,
    title = "{RISK}: A Framework for {GUI} Agents in {E}-commerce Risk Management",
    author = "Chen, Renqi  and
      Tao, Zeyin  and
      Guo, Jianming  and
      Zhu, Jingzhe  and
      Peng, Yiheng  and
      Sun, Qingqing  and
      Zhang, Tianyi  and
      Chen, Shuai",
    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.1720/",
    pages = "37104--37120",
    ISBN = "979-8-89176-390-6",
    abstract = "E-commerce risk management requires aggregating diverse, deeply embedded web data through multi-step, stateful interactions, which traditional scraping methods and most existing Graphical User Interface (GUI) agents cannot handle. These agents are typically limited to single-step tasks and lack the ability to manage dynamic, interactive content critical for effective risk assessment. To address this challenge, we introduce RISK, a novel framework designed to build and deploy GUI agents for this domain. RISK integrates three components: (1) RISK-Data, a dataset of 8,492 single-step and 2,386 multi-step interaction trajectories, collected through a high-fidelity browser framework and a meticulous data curation process; (2) RISK-Bench, a benchmark with 802 single-step and 320 multi-step trajectories across three difficulty levels for standardized evaluation; and (3) RISK-R1, a R1-style reinforcement fine-tuning framework considering four aspects: (i) Output Format Constraint, (ii) Single-step and (iii) Multi-step Level Reward, and (iv) Task Level Reweight. Experiments show that RISK-R1 achieves a 6.8{\%} improvement in offline single-step and an 8.8{\%} improvement in offline multi-step, using only 7.2{\%} of the parameters of the SOTA baseline. Moreover, it attains a top task success rate of 70.5{\%} in online evaluation. RISK provides a scalable, domain-specific solution for automating complex web interactions in e-commerce risk management. The code is available at https://github.com/RenqiChen/RISK-GUI."
}