I agree, I think is a game changer.

And I think what's interesting as we as we looked at the financial inclusion score more we realised how important lifestyle was as well as behaviour. And that sort of led us down to something were developing to the moment which is really based on geographical havior and customer blueprints for more targeted marketing strategies, we derive a lot of this information directly from the mobile, someone's behaviour on that phone and their choices and their lifestyle patterns and gleaning all that information from the mobile, which is all anonymized data at one point.

It's still dominated by your credit card issuers.

So most people if they have Apple Pay or Google Pay, they have loaded their credit card and that's probably the most popular form of payments, but these QR payment providers who have their own mobile apps is very, very popular. And it's accepted everywhere. And while they still represent a very small share in terms of total purchase volumes, they are by far the fastest growing, and that is why issuers are very, very concerned.

And these QR payment providers are also going into that credit space, where they're offering a small credit of maybe $500 to $1,000. But they're using very basic information - just your name, phone number, email - so it's much, much faster and easier to apply for that new QR payment credit versus a traditional credit card.

First of all, most of the brick and mortar banks just don't understand, at a granular view, which products and which segments are really making them money.

In general, they can tell us, sure, but they have a lot of different customer segments, right, different products and sometimes they struggle to understand where they need to adjust.

So in this sense, our advanced analytics is helping banks to understand how does existing products work, and then give a different insight and basically all of these just allow them to be more agile, and to run business in the more well controlled and data driven way.

"I don't understand why I have to take credit out to get credit. Why don't you just look at my bank statement, you can see how I'm behaving?"

Well, that's great. That's exactly what we do. At Direct ID we focus on categorising transactions that have particular interest risk decision makers.

So what we were able to do is to bridge the gap from unsupervised learning, where we don't have any labels, to supervised learning, where we do have a label.

There is an adversarial goal here: if you increase the limit, you have a potential profit from the person using the credit limit but you have a very real immediate hit to your provisions.

So now we needed some sort of modelling that didn't just give us whether to increase or not, but which would also give us the optimal value of that increase. We don't just give you a limit increase, we give you the one that minimises the value at risk and also the one that has, in terms of expected value, a profitable margin.

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.