Thought Leaders in Artificial Intelligence: Workiz CEO Didi Azaria (Part 1)

AI and ML is largely being applied to problems within the enterprise. Here is an exception where a solution to an SMB problem is being achieved through AI. Very cool story!

Thought Leaders in Financial Technology: Infima Founder and Chief Scientist Kay Giesecke (Part 5)

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 of climate on these markets. If you have a flood in Kentucky, then there’s an impact on the homes. That’s very unfortunate.

Thought Leaders in Financial Technology: Infima Founder and Chief Scientist Kay Giesecke (Part 4)

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 case. Are they using it for research purposes? Are they using it for trading purposes? Are they using it for reporting purposes? We have a per use case charge. The more use cases, the higher the annual charge. It’s a subscription model, typically with multi-year contracts.

Thought Leaders in Financial Technology: Infima Founder and Chief Scientist Kay Giesecke (Part 3)

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, you can divide it into different customer segments. If you want to start at the lifecycle of the mortgage, it always is the bank. What happens then is that the banks collect bundles of mortgages, take them over to a government agency, and they turn these bundle of mortgages into securities that are then bought up by investors and banks. That’s the full lifecycle.

Thought Leaders in Financial Technology: Infima Founder and Chief Scientist Kay Giesecke (Part 2)

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 a specific home that they’d like to purchase. They have to assess the chance that this person is going to be able to repay the mortgage on time over the term of the mortgage.

Thought Leaders in Financial Technology: Infima Founder and Chief Scientist Kay Giesecke (Part 1)

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 as well as to Infima.

Thought Leaders in Artificial Intelligence: Squirro CEO Dorian Selz (Part 4)

Sramana Mitra: Do you have a bunch of customers who are using you across three of your use cases?

Dorian Selz: Across two and, in one case, across three use cases.

Sramana Mitra: That’s all in financial services?

Dorian Selz: Predominantly in financial services.

Thought Leaders in Artificial Intelligence: Squirro CEO Dorian Selz (Part 3)

Sramana Mitra: I am going to ask you a couple of questions from the point of view of how you built this business. How do you scope the sizing of the market? How big is the TAM in financial services?

Dorian Selz: I do not have a single number to give to you. Whoever you ask gives you a different TAM perspective.

Sramana Mitra: Let me put it in the way we frame it. We look at average deal size and then multiply that by how many customers are out there. If you were to own 100% of the market, how many of these deals are you going to be selling?