Transforming Auto Captive Finance Lending: The Power of AI in Next Best Actions

This session explores how auto lenders can transform captive finance lending by augmenting traditional credit scoring with recommender systems and Agentic AI to drive Next Best Actions—improving decision outcomes, reducing lost opportunities, and delivering more personalized offers, particularly for initially declined applicants.

Key topics include:
  • Credit Decisioning Basics
    • Traditional scoring, rules, and underwriting
  • Opportunity in Declines
    • Why applicants are rejected and the impact on revenue and retention
  • Recommender Systems for Next Best Action (NBA)
    • Suggesting alternatives like adjusted terms, conditional approvals, or different products
  • Agentic Workflows
    • Using Agentic AI to gather data, verify details, and reassess risk dynamically
  • NBA in SAS Decision Management
    • Combining models, rules, and recommendations into one transparent, compliant flow
  • Conversion + Risk Balance
    • Increasing approvals with tailored offers while managing risk and optimizing performance
  • End-to-End Auto Loan/Lease Originations Walkthrough
    • Demonstration of credit scoring application

About the Presenter

Gene Grabowski, Jr. is an Advisory Solutions Architect and Data Scientist at SAS, bringing extensive experience in the automotive sector. He spent 13 years at Ford Motor Company and Ford Credit, where he led analytics initiatives across manufacturing, finance, and customer experience. In his current role, Gene partners with clients to design and deploy scalable machine learning solutions, seamlessly integrating open-source technologies with SAS platforms. He is also focused on advancing enterprise applications of Natural Language Processing (NLP), Large Language Models (LLMs), and Agentic AI to enhance decision-making.

In addition to his industry work, Gene has served as a lecturer at Oakland University, Wayne State University, Carnegie Mellon University and the University of Alabama. He holds a master’s degree in Economics from Michigan State University and a bachelor’s degree in Economics from Oakland University. He has also earned both a Machine Learning Certification and AI Professional Certification from Stanford University.