Bringing Generative AI to the Enterprise
Key Takeaways:- How to take an iterative and outcome-focused approach for building GenAI solutions
- How to balance centralized vs. federated activities for GenAI
- How to drive visibility & transparency on GenAI activities
Description
Enterprise adoption of Large Language Models (LLMs) presents many risks and challenges. However, the business value that is unlocked will be significant.
In this webinar, Steve Holden, Senior Vice President and Head of Single-Family Analytics at Fannie Mae, will discuss the ingredients for successful introduction of LLMs at a large U.S. Financial company and some of the early opportunities to create business value.
Topics will include: where to start, how to navigate the many risks/hazards, ways to engage and empower your workforce, spur innovation, and find the right pace of adoption. This talk will relay the lessons learned from GenAI adoption at Fannie Mae, our progress to-date and where we are headed next.
Presenter Bio
Steve Holden leads a team of data science professionals at Fannie Mae, supporting loan underwriting, pricing and acquisition, securitization, loss mitigation, and loan liquidation for the company’s multi-trillion-dollar Single-Family mortgage portfolio. He is also responsible for all Generative AI initiatives across the enterprise. His team provides real-time analytic solutions that guide thousands of daily business decisions necessary to manage this extensive mortgage portfolio. The team comprises experts in econometric models, machine learning, data engineering, data visualization, software engineering, and analytic infrastructure design.
Holden previously served as Vice President of Credit Portfolio Management Analytics at Fannie Mae. Before joining Fannie Mae in 1999, he held several analytic leadership roles and worked on economic issues at the Economic Strategy Institute and the U.S. Bureau of Labor Statistics.
Holden holds a Bachelor of Arts in economics from the University of Toronto, a Master of Arts in economics from Queen's University, and a Doctorate in economics from The Johns Hopkins University.