@inproceedings{liu-etal-2026-acrm,
    title = "{ACRM}: Multi-Agent Trajectory Learning for Automated Credit Risk Model Refreshing in Production",
    author = "Liu, Liangzu  and
      Ruan, Mengzhe  and
      Chen, Xiaotian  and
      HaonanChen  and
      XudongNiu  and
      Yuan, Wendi  and
      YuechenLi  and
      Liu, Yang  and
      Wang, Guanjun",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.65/",
    pages = "942--956",
    ISBN = "979-8-89176-394-4",
    abstract = "Credit risk models suffer from rapid performance decay due to distribution shifts, requiring frequent updates to meet strict operational guardrails. However, manual refreshing takes weeks of trial-and-error across upstream data engineering and downstream training. We present ACRM, a deployed multi-agent framework that automates the end-to-end credit modeling workflow by treating it as a learnable trajectory of agent interactions. Unlike AutoML, which optimizes hyperparameters on fixed datasets, ACRM{'}s action space extends to upstream data semantics{---}cohort selection, observation windowing, feature screening{---}where the majority of performance recovery occurs. A central Orchestrator coordinates specialist agents through a three-stream decision stack: rule-based safety guardrails, retrieval-augmented grounding from historical workflows, and preference alignment via DPO on expert-labeled trajectories. Deployed at a major fintech institution for three months across six business scenarios, ACRM reduced the average model refresh cycle from weeks to 1.1 days and iteration rounds by 65{\%}, while maintaining superior stability metrics."
}