Profitably grow your auto portfolio using “what-if” scenario modeling and simulation tools | May 13, 2021
The auto industry has been driven by a dramatic change in consumer behavior, not the least of which is an exponential rise in the use of digital and mobile channels for banking and financial services. This digital economy’s acceleration makes data silos, complexity, and ambiguity the worst enemies of auto lenders. Speed, precision, and agility are now table stakes.
Do you want to learn how to meet customers’ shifting expectations and keep a healthy and growing auto portfolio despite volatile conditions?
Join the FICO team for an educational session where we will discuss scenario modeling and other tools that will help you to project, prioritize, and invest in the right strategies and outcomes.
You will learn how to:
• Compare and analyze multiple possible credit decisioning scenarios using simulation and forecasting;
• Simulate likely outcomes of credit decisioning policies before deciding on a course of action;
• Evaluate trade-offs by using business outcome simulation;
• Stress-test data input and simulate business outcomes;
• Identify key parameters that affect a decision;
• Shift from making decisions based solely on historical data to explore “what-if” scenarios
Presenter: Matt Stanley, VP, Decision Sciences, FICO
As the Decision Sciences Segment Leader for FICO, Matt Stanley has global responsibility for the sales and marketing of FICO’s applied optimization, IFRS-9/CECL, and Data Science practices. Matt joined FICO in 1999 as a data scientist. His first 4 years at FICO were split between developing new analytic products and delivering custom analytics for clients. His client work includes risk, attrition, and profitability modeling as well as optimization projects spanning decision areas such as credit line management, pricing, marketing, collections, fraud, and retention. Matt holds a Master’s degree in Economics with an emphasis in econometrics and game theory. Prior to joining FICO, Matt taught economics at San Francisco State University.