用智能体团队自动完成金融建模与风险管控，提升效率与合规性。
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
- 构建人类协同的智能体团队，分工完成建模全流程任务。
- 在欺诈检测、信贷审批等数据上验证了流程有效性与鲁棒性。
- 适合金融建模、模型风险管理及AI自动化落地的研究者参考。
大型语言模型的出现开启了智能体系统的新时代，人工智能程序在多个领域展现出卓越的自主决策能力。本文探讨了智能体系统在金融服务中的工作流程。具体而言，我们构建了包含人类参与模块的智能体团队，能够有效协作完成复杂的建模与模型风险管理（MRM）任务。建模团队由一名裁判智能体和多名执行特定任务的智能体组成，包括探索性数据分析、特征工程、模型选择/超参数调优、模型训练、模型评估及文档撰写。模型风险管理团队则由一名裁判智能体和多个专业智能体构成，负责检查建模文档合规性、模型复现、概念合理性、结果分析及文档编写。通过一系列数值案例，我们在信用卡欺诈检测、信用卡审批及组合信用风险建模数据集上展示了建模与MRM团队的有效性与稳健性。
原文摘要 · Abstract (English)
The advent of large language models has ushered in a new era of agentic systems, where artificial intelligence programs exhibit remarkable autonomous decision-making capabilities across diverse domains. This paper explores agentic system workflows in the financial services industry. In particular, we build agentic crews with human-in-the-loop module that can effectively collaborate to perform complex modeling and model risk management (MRM) tasks. The modeling crew consists of a judge agent and multiple agents who perform specific tasks such as exploratory data analysis, feature engineering, model selection/hyperparameter tuning, model training, model evaluation, and writing documentation. The MRM crew consists of a judge agent along with specialized agents who perform tasks such as checking compliance of modeling documentation, model replication, conceptual soundness, analysis of outcomes, and writing documentation. We demonstrate the effectiveness and robustness of modeling and MRM crews by presenting a series of numerical examples applied to credit card fraud detection, credit card approval, and portfolio credit risk modeling datasets.
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