Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
- URL: http://arxiv.org/abs/2502.05439v1
- Date: Sat, 08 Feb 2025 04:03:47 GMT
- Title: Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
- Authors: Izunna Okpala, Ashkan Golgoon, Arjun Ravi Kannan,
- Abstract summary: This paper explores agentic system in the financial services industry.
We build agentic crews that can effectively collaborate to perform complex modeling and model risk management tasks.
We demonstrate the effectiveness and robustness of modeling and MRM crews by presenting a series of numerical examples.
- Score: 0.0
- License:
- Abstract: 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.
Related papers
- Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities [0.0]
Large language models (LLMs) in enterprise modeling have recently started to shift from academic research to that of industrial applications.
In this paper we employ a knowledge graph-based approach for enterprise modeling and investigate the potential benefits of LLMs.
arXiv Detail & Related papers (2025-01-07T06:34:17Z) - GUI Agents with Foundation Models: A Comprehensive Survey [91.97447457550703]
This survey consolidates recent research on (M)LLM-based GUI agents.
We identify key challenges and propose future research directions.
We hope this survey will inspire further advancements in the field of (M)LLM-based GUI agents.
arXiv Detail & Related papers (2024-11-07T17:28:10Z) - CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments [90.29937153770835]
We introduce CRMArena, a benchmark designed to evaluate AI agents on realistic tasks grounded in professional work environments.
We show that state-of-the-art LLM agents succeed in less than 40% of the tasks with ReAct prompting, and less than 55% even with function-calling abilities.
Our findings highlight the need for enhanced agent capabilities in function-calling and rule-following to be deployed in real-world work environments.
arXiv Detail & Related papers (2024-11-04T17:30:51Z) - FinVision: A Multi-Agent Framework for Stock Market Prediction [0.0]
This research introduces a multi-modal multi-agent system designed specifically for financial trading tasks.
A key feature of our approach is the integration of a reflection module, which conducts analyses of historical trading signals and their outcomes.
arXiv Detail & Related papers (2024-10-29T06:02:28Z) - Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning Approach [1.8874331450711404]
We propose a conceptual framework that combines modeling event logs, intelligent modeling assistants, and the generation of modeling operations.
In particular, the architecture comprises modeling components that help the designer specify the system, record its operation within a graphical modeling environment, and automatically recommend relevant operations.
arXiv Detail & Related papers (2024-08-26T13:26:44Z) - An Interactive Agent Foundation Model [49.77861810045509]
We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents.
Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction.
We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
arXiv Detail & Related papers (2024-02-08T18:58:02Z) - TrainerAgent: Customizable and Efficient Model Training through
LLM-Powered Multi-Agent System [14.019244136838017]
TrainerAgent is a multi-agent framework including Task, Data, Model and Server agents.
These agents analyze user-defined tasks, input data, and requirements (e.g., accuracy, speed), optimizing them from both data and model perspectives to obtain satisfactory models, and finally deploy these models as online service.
This research presents a significant advancement in achieving desired models with increased efficiency and quality as compared to traditional model development.
arXiv Detail & Related papers (2023-11-11T17:39:24Z) - Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM
Agents [0.0]
We present a novel framework for enhancing the capabilities of large language models (LLMs) by leveraging the power of multi-agent systems.
Our framework introduces a collaborative environment where multiple intelligent agent components, each with distinctive attributes and roles, work together to handle complex tasks more efficiently and effectively.
arXiv Detail & Related papers (2023-06-05T23:55:37Z) - Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning [65.268245109828]
In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models.
Deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning.
Model reprogramming enables resource-efficient cross-domain machine learning by repurposing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning.
arXiv Detail & Related papers (2022-02-22T02:33:54Z) - Multi-Agent Imitation Learning with Copulas [102.27052968901894]
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions.
In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems.
Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
arXiv Detail & Related papers (2021-07-10T03:49:41Z) - Quantitatively Assessing the Benefits of Model-driven Development in
Agent-based Modeling and Simulation [80.49040344355431]
This paper compares the use of MDD and ABMS platforms in terms of effort and developer mistakes.
The obtained results show that MDD4ABMS requires less effort to develop simulations with similar (sometimes better) design quality than NetLogo.
arXiv Detail & Related papers (2020-06-15T23:29:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.