Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
This work addresses the problem of efficient modeling and risk management for financial institutions, providing an incremental solution by leveraging large language models and human-in-the-loop modules.
The authors tackled the problem of modeling and model risk management in financial services by developing agentic AI systems, achieving effective collaboration between human and artificial intelligence agents. The systems demonstrated effectiveness and robustness in various numerical examples, including credit card fraud detection and portfolio credit risk modeling.
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.