@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T ACRM: Multi-Agent Trajectory Learning for Automated Credit Risk Model Refreshing in Production
%A Liu, Liangzu
%A Ruan, Mengzhe
%A Chen, Xiaotian
%A Yuan, Wendi
%A Liu, Yang
%A Wang, Guanjun
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%A HaonanChen
%A XudongNiu
%A YuechenLi
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F liu-etal-2026-acrm
%X 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.
%U https://aclanthology.org/2026.acl-industry.65/
%P 942-956
Markdown (Informal)
[ACRM: Multi-Agent Trajectory Learning for Automated Credit Risk Model Refreshing in Production](https://aclanthology.org/2026.acl-industry.65/) (Liu et al., ACL 2026)
ACL
- Liangzu Liu, Mengzhe Ruan, Xiaotian Chen, HaonanChen, XudongNiu, Wendi Yuan, YuechenLi, Yang Liu, and Guanjun Wang. 2026. ACRM: Multi-Agent Trajectory Learning for Automated Credit Risk Model Refreshing in Production. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 942–956, San Diego, California, USA. Association for Computational Linguistics.