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 that can effectively collaborate to perform complex modeling and model risk management (MRM) tasks. The modeling crew consists of a manager 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 manager 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.

翻译：大型语言模型的出现开启了代理式系统的新纪元，人工智能程序在多个领域展现出卓越的自主决策能力。本文探讨金融服务行业中的代理式系统工作流程。具体而言，我们构建了能够有效协作以执行复杂建模与模型风险管理任务的代理团队。建模团队由一名经理和多个执行特定任务的代理组成，这些任务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、模型评估以及撰写文档。模型风险管理团队由一名经理和若干专业代理组成，这些代理执行诸如检查建模文档合规性、模型复现、概念合理性分析、结果分析以及撰写文档等任务。我们通过应用于信用卡欺诈检测、信用卡审批以及投资组合信用风险建模数据集的一系列数值示例，展示了建模团队与模型风险管理团队的有效性和鲁棒性。