Trustworthy AI For High Stakes Decisions

Evan Kirstel (0:02)

Hey, everybody, fascinating topic and guest today. We're talking about FICO and how AI and blockchain and stronger governance could help make critical business decisions more transparent and trustworthy. Scott, how are you?

Scott Zoldi (0:15)

I'm doing great, Evan. How are you?

Evan Kirstel (0:17)

I'm doing great. Thanks for joining. Really intrigued by the topic. And of course, everyone's heard of FICO here in the US, and mainly for credit scores, of course, but maybe introduce yourself and what the software side of the business at FICO does.

Scott Zoldi (0:33)

Yeah, pleased to do so. So FICO is well known for the FICO score, but a larger part of our business is actually the fact that we're an analytic software company. We were one of the early companies to pioneer the use of machine learning and AI in software applications. The very first one of these applications was one called Falcon in the year 1992, which put a profile-based machine learning model at the center of making decisions about whether or not fraud was occurring on a credit card. And since that time, we've continued to advance AI and machine learning associated with the markets that we operate in, primarily financial services.

My role is I'm Chief Analytics Officer at FICO. I'm responsible for our AI direction, the types of algorithms we use, and how we make sure that when we build AI to make decisions with our customers that it's the right AI and things that we can explain and audit.

Evan Kirstel (1:28)

Brilliant.

And beyond AI, we'll dive into your approach. What are the business problems that you're helping customers solve?

Scott Zoldi (1:39)

So we specialize in really understanding the customer at a very fine-grained level. And this goes back to our pedigree around things like understanding payment card transactions and what would be likely fraud or not fraud. But we are involved in essentially any sort of decision around a customer life cycle. Think about things like a banking application. If you're a customer of a bank, you originate, you need to be protected from fraud, scams, you apply for more credit, you have to manage that credit line. FICO is generally involved in many of those decisions to the application of models and then decision logic. So to help banks get more intelligence around what is the next likely outcome for this customer? How can they best step in and help assist in that customer's life cycle?

Evan Kirstel (2:28)

Brilliant. Of course, every software company is deeply engaged with an AI strategy. Maybe talk about your approach, your strategy, your philosophy when it comes to AI. How is it different?

Scott Zoldi (2:39)

So at FICO, we very much focus on what is the right AI for our customers. And these customers that we serve are in highly regulated markets. And so they need to make sure that when they make a decision, that it's explainable, that it's ethical, that it's robust, and that it's auditable. And that puts very, very strong constraints on the types of AI approaches and machine learning approaches that we may use to solve different types of problems. And so you've heard of and the industry's focused on this concept of responsible AI.

I was one of the first to talk about these more than 10 years back. And we've actually built out both types of AI algorithms that will provide that level of robustness, explainability, interpretability, ethical testing, and then auditability around these methods. And so very much our approach is make sure that we understand the environment that we're operating underneath. Understand how our customers take the use of AI very, very seriously because they have a covenant with their customers to make the right decisions in a responsible, measured way. And so we develop our AI around those techniques, which is a big reason why a lot of the things that we do, we built from scratch. I myself have more than 100 patents in AI, primarily because the generic AI that's out there today is not appropriate for many types of decisions that we make on customers, particularly in the financial service of the state.

Evan Kirstel (4:08)

Great insight there. And your comment and your talk about the trust problem in AI over the last decade is so timely. What are some of the problems that show up most often when it comes to trust?

Scott Zoldi (4:25)

So I think one of the biggest challenges with trust is that both organizations that use these AI models and also consumers don't have a really good understanding of how that model was built and whether it's appropriate to be used for them. We've forgotten the basics of data science, being so enamored with a lot of these new technologies that are out there.