Kay Giesecke: Then, there are the individual loans and credits. The individual mortgage loans are not traded. It’s the same problem at the individual loan level. What can we say about this borrower? These are different verticals that we can expand into. One interesting initiative that we’re focusing on is trying to understand the impact
Sramana Mitra: Do you price per analyst or some other way? Kay Giesecke: We have a matrix for the pricing scheme. One layer of this is size of the company. If you have an asset manager like mutual fund, how big is the asset under management? That’s one factor. Another one would be the use
Sramana Mitra: You built this model at Stanford and then you spun out a company on top of that model. What is going on with bringing this to market like commercializing this? What is the business model? What is the go-to-market strategy? Is it selling to banks? Kay Giesecke: If you take that mortgage slice,
Sramana Mitra: What are some of the nuggets that you’ve learned? Kay Giesecke: We learned that the behavioral patterns are very complex. Let’s just focus on homeowners. There are the lenders. They look at applications for mortgage loans. They need to decide if this person is creditworthy enough for a home mortgage that’s backed by
Kay is a Stanford professor. He has applied Deep Learning models to various use cases within the Mortgage and Mortgage-backed Securities space to build Decision Support tools for Traders and Portfolio Managers. The general principle applies to other forms of credit as well, besides Mortgage. Sramana Mitra: Let’s start by introducing our audience to yourself