CreditPy is including some functionalities regarding to develop credit risk scorecards, a PD model, basic data analysis, and it checks the informative variables in an automated way to determine which features is going to be passed to the predictive model. It also generates an automated model framework that is actually searching for the best predictive model across the different feature sets that potentially can be used during the model development.

And after this, there are actually many functions that has been defined to create the rating scale. And also, after creating the rating scale, its offers to do some validation, like univariate gini check, information value checks, basic multicollinearity checks, stability checks on the futures to see if there will be any drift on the predictions on the auto sample set bit applies a basic rate of evidence transformation on the data.

And finally, it allows the user to validate the created rating scale, predictive power of the model and the calibration.

But effectively, if you think about it, there is no company in the world, maybe just a few, whose whole businesses is data, noone really just creates data, noone really just handles data. However, every company, whether you're logistics, retailer, bank insurance, your mom and pop shops on a corner, they all deal with data - you've got prices, you've got sales, you've got measurements, if you are building a house.

However, as you said, often in organisations, they try to put a silo around data, they say, I have a Chief Data Office, I have a data function, I have specifically data processing, and data is a bit like the blood of an organisation. It goes everywhere, however, because it goes everywhere, you cannot just silo it somewhere. Of course, you need to have some patterns, some standards around data, but every part of the of a business has to be responsible for the data.

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.

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.

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.

I mean, this is not just doing data science, but actually looking to bring together and harness really advanced analytics, modern methodologies to really bring forward business strategy on that side. And that is something specifically on the credit risk side, which I'm seeing more and more, where if you actually start at the board level thinking about why do certain things not quite work? Why why are we losing market share? Why are we not growing as fast as we can? I mean, once you actually get into the engine room, you open the door, very often you find data related topics, modelling related topics, infrastructure process topics are really at the heart of what's not working.

We're able to bring in this reliable view on the world, that growth is actually still there and very important, but I would strongly recommend not just to continue in an undifferentiated way, what you've been doing over the last 10 years, characterised by low interest, low inflation, and so on and so forth, the environment is definitely changing. We see our clients adopt to that very quickly.

But adopting to it does not mean slamming on the brakes, it actually means getting more sophisticated in the analytic space, getting more sophisticated in terms of how can I assess the affordability of a loan for a specific retail customer, for a specific SME customer on a case by case basis, in a scalable fashion. That is really where we see really a lot of interest, and a lot of movement over the last six months.