Tom Burnside

They were just getting established.

And nobody could really kind of put all the other kind of API and data information together to be able to tell their story.

So there's a lot of other things that you would look at outside of just credit.

You might look at phone bills.

You might look at rent history.

You might look at some other things to tell the story of their willingness to pay, right?

Or their ability to pay.

And so those are the things we, you know, we really focused on.

We focused on, I mean, one of the problems you always have is fraud.

And we focused a lot on KYC, know your customer.

Uh, and we were able to get a very predictive outcome on those predictor scores.

And so, you know, this was typically somebody that was either on the way back up, you know, I had gone through a dip or just had a very light credit footprint.

Well, really what you're doing is the AI models have done an amazing job of predicting risk.

And so the way that the AI models work today is it predicts risk, puts you in a category, and then tells me, okay, basically, here's what the risks are going to be.

But then pricing is the next kind of optimization tool that we use.

And we have about five different buckets of credit grades or risk, right?

And we now are up to 400 different pricing points with inside of those grids.

So we are getting really, really good at giving you the right product at the right time at the right place with the right terms and conditions that you can understand how affordable it is for you to finish a project or to resolve some consolidation of bills or whatever it is that you need to do.

Yeah, typically the average price back in those days was about 22, 23% weighted average coupon, if you think about it in that particular way.

And it was typically over three to four years is what we were doing.