12:00 pm – 1:30 pm PT
1:00 pm – 2:30 pm MT
2:00 pm – 3:30 pm CT
3:00 pm – 4:30 pm ET
Learn how to go beyond the basic default risk and matching competitors’ loan pricing with machine-learning models.
AFTER THIS WEBINAR YOU’LL BE ABLE TO:
- Understand how modern data architecture is simplifying analytics decisions
- Assess what data is required to reduce your loan portfolio risk
- Understand how to benchmark machine-learning-based pricing calculations
- Decide if your institution is ready to implement improved loan decision-making
This webinar will demystify AI and explain how modern data tools can improve loan pricing and profitability for financial institutions of all sizes. Fundamentally, the accessibility of big data and machine learning has vastly decreased the cost of making accurate predictions. For lending, this means that you can go beyond basic default risk and matching competitors when pricing loans. Machine-learning models reduce lending risk and can even account for loan profitability and borrower lifetime value considerations at the time of issuance.
This program will also focus on implementation and address specific ways for institutions to apply modern data architecture and machine-learning-derived loan pricing expertise in-house, without being eternally reliant on data vendors. Understanding how to unify and take control of your data is the first step toward more effective loan decisions.
Attendance certificate provided to self-report CE credits.
WHO SHOULD ATTEND?
This webinar will benefit loan officers and executives looking for a practical guide to improving product pricing and technology officers who want to understand the investment required to decrease loan risk and improve quality loan volume.
- Step-by-step whitepaper detailing the path from disparate data to predictive loan pricing
- Employee training log
- Interactive quiz
NOTE: All materials are subject to copyright. Transmission, retransmission, or republishing of any webinar to other institutions or those not employed by your financial institution is prohibited. Print materials may be copied for eligible participants only.