And where I found that problem statement, that focus, was actually when I started working with community finance lenders, specifically, an experienced that really exposed the problem, to me that exists within consumer lending. And that is that very little is given to the customer management side of the credit lifecycle.

And I've had the opportunity to sit within the community finance lenders office, I mean, this was right on the front line. And I remember there was a lady that came in, and she had lots of children with a, she was stressed because she was in arrears. And she come into this lending office to arrange an arrangement with the lender to ensure that she could stay on track with her payments. And I just sat there observing, and she sat there and she was getting more and more stressed, as the advisor was saying, Well, can you afford this much a week? Can you afford this much a week, and having the awareness as to all the data behind the lending decision, and everything that they had about it, I just felt like, there's got to be a better way to engage this customer and use this information to help her make sounder financial choices.

That was my lightbulb moment.

It's another excellent question, because I think there's a lot of discussion about big data and AI and machine learning. And they go together well, big data and machine learning, but they're not the same thing.

A lot of what gets done with machine learning in our industry is applying very nonlinear methods to the same old data. In fact, everything I've talked about so far has been more intelligent use of the data you've always had, Building Better models of your business and of your product.

If you have unique data, that's great. And often we find unique datasets in finance companies, where they're doing some kind of specialty lending. You know, one of my favourites for a long time was a group that was looking at point of sale loans for cruise ship tickets.

So, in as much as I was a salesman who was always supposed to be pushy at all times, what I needed to also understand what was the problem that I was solving for them, right and try to solve that for them.

Now, wearing this new hat for me as a salesperson, I'd already prepared me for what it was going to look like when I moved to a company like Fair Money. And if you look at all three co founders, we actually were all at Fair Money at the time. And we moved out just to go to this. When I got into Fair Money, and then the COVID era just hits, we basically began to see one new thing, which was that the guys who were doing collections for us at the time, were still doing collections, like it was 2018 - where the customer just needed a call for them to remember to go to make the payments.

They did not need you to help them structure payments, you know, create a payment plan; they did not need you to help them with any educational of any sorts. They were not in any mess whatsoever.

If you have to remember what we talked machine learning AI today is not something that's come around today, right? What has changed today is our ability to store enormous amount of data economically. Number two is the processing speeds we have today. You know, you want a search bar before you type, your third word is already telling you what it should be. So think about it. And there are millions of people doing it at the same time, any second. And the third thing is the speed of transmission of information.

I think those three in combination literally are the most fertile ground to bring AI to life.

And that's what we've essentially done. But be mindful that when you're doing things at that speed, there are things that could happen which go out of your control.

The learning aspect is probably the most important we eat volatility for breakfast, we make love to volatility!

That right there describes our structural - and I think unassailable - advantage in a world that has suddenly become quite a bit more volatile than it has been for the last number of decades, under which my friends in the conventional 1.0 version of the bureau's operate. And global interconnectedness or the globalisation of economies means that things happening in the Ukraine, from which my ancestors hail, to the gas pumps in North America is a pretty direct connection. And so whether it's gas pumps or groceries that are becoming much more expensive, you have consumers feeling it.

And therefore, to my lender customers, those same consumers need to be scored properly in the fullness of all of the environmental macro factors, as well as the micro factors down at the borrower's level.

So I think in the beginning of this business, we realised that we have to put in some real time in order to prove that with potential customers. So we've been at it for just slightly over two years. And we're able to not only back tests from source data, but actually test or run our models on real data.

And the results have been quite impressive, if I say so myself, we're happy with that confidence of 86%. We do provide then a boolean results where you know, it's a Yay or Nay, We realise we don't want to reinvent the wheel with another score that one has to contend with.