How Stratyfy used a vendor-agnostic AI risk guide to generate trust with buyers who weren't ready to purchase | Laura Kornhauser

**Laura Kornhauser** (0:00)
I think one of the worst strategies that an organization can have right now is say we need to use AI.

**Brett** (0:08)
Welcome back to another episode of Builders. As always, this show is brought to you by frontlines.io, Silicon Valley's leading B2B podcast production studio. If you're bringing technology to market and want to learn from your peers, we have a library of more than 1200 interviews with venture-backed founders and marketers where they talk all things go to market. Of course, if you want to launch your own podcast, we offer podcasts as a service to more than 80 tech startups. The idea there is very simple. You show up and host and we do everything else. Now with all that said, let's jump into today's episode.
Our guest today is Laura Kornhauser, co-founder and CEO of Stratyfy. Laura, welcome to the show.

**Laura Kornhauser** (0:47)
So happy to be here, Brett.

**Brett** (0:48)
Excited to have you back, I should say, actually, because you've been here before. You were here a couple years ago. So always fun to bring guests back on. A lot of the people listening, or most of the people listening, probably weren't listening a couple years ago, and we did have you on. So maybe let's just start with an introduction about the business, the products, and what you do.

**Laura Kornhauser** (1:03)
Absolutely. So I'm the co-founder and CEO of Stratyfy. We help financial institutions better understand and make decisions based on different risk factors. So that's across credit, decisioning, fraud detection, and bias detection and removal. And then we have a number of new product offerings that we've launched definitely since the last time we've talked, based on a lot of new exciting things that I know we'll get into during this discussion. But at our heart of hearts, we're here to help community-based financial institutions better serve their customer base and find ways to unlock opportunities that more traditional methods and metrics of measuring risk can hide or obfuscate.

**Brett** (1:41)
And I know this can sometimes be tricky to do, but if you reflect on the history of the business and you were to break it up into chapters, what would be the core chapters?

**Laura Kornhauser** (1:49)
Oh, great question. So we just recently celebrated our 10th birthday. So I've actually been thinking quite deeply about these types of reflections and these types of things. So I would say that our initial chapter was very much fueled by early onset of machine learning in the financial services industry. Folks really trying to leverage that technology and finding out that they were running into certain problems due to the black box nature of a lot of machine learning based solutions at that time. So what that meant is certain use cases that were more heavily regulated. Our customers, financial institutions really couldn't leverage this technology due to regulatory concerns and also I'll say human concerns. So trying to get folks to trust something they didn't understand was really challenging. That was very much kind of chapter one. Then I would say chapter two moved into a phase where there was more leveraging of machine learning and folks were getting more and more aware of the challenges that a purely data based solution or data fed, if you will, solution, the challenges that could arise from that. A lot of the challenges that we were particularly focused on are challenges around biases. If you feed a machine learning system historical data, that data is biased in a wide variety of ways. I often joke it's not if data is biased, it's how.
Finding ways and opportunities to both uncover those biases and then mitigate it was the next chapter.
Now, we're in a whole brave new world, a world where AI is absolutely everywhere. I would say the term is thrown around, I don't know, in between every other sentence. I think now I'm supposed to refer to us as an AI native company even, Brad. I should have put that in my introduction. But a lot of the still the very same, from phase one and phase two or chapter one and chapter two, concerns still exist out there in the industry. Financial institutions are looking to leverage more and more types of AI technology, but still really concerned about lack of transparency, concerned about biases, and concerned about ensuring that they keep humans in the driver's seat of this technology, as opposed to out of the loop or out of the information set of how this technology is being used and adopted.

**Brett** (4:09)
When you think about six years before November 2022, when I feel like ChatGPT was really unleashed into the consumer world at least, was there a totally different go-to-market motion that you switched to? A lot of founders I've brought on, they've described it as those early years before that big moment, it was hard and they had to try to create demand and then capture the demand. But then it totally shifted to, there's demand out there, but there's also way more people trying to capture that demand and the go-to-market motion completely had to change.