Managing risk is crucial for all businesses, especially during economically uncertain times. Auto Lender Exeter Finance leveraged industry knowledge and subject matter expertise to generate features from the vast amounts of transactional data related to the performance of its auto loans. But, it felt that it needed a more powerful model for this purpose.
Join Exeter Finance SVP of Decision Science Karthik Chandrasekhar and dotData Director of Product Marketing and PRMIA member Stuart Kozola to discuss how Exeter leveraged the power of their own data and Machine Learning to identify new risk insights that resulted in lower risks from payment defaults.
Our expert speakers will share their knowledge and experience on how to identify, analyze, and manage risks using data-driven insights:
- Why third-party data is insufficient for an effective “predictive” risk profile.
- Why Exeter used data-centric discovery to build more effective predictive models.
- The role of automation in building predictive models in-house, without adding staff.
