What happened was that, when we started providing the service, we guarantee the bad debts in the service. And that's why we start actively use different kinds of analytics to develop the different kinds of scoring systems. And these scoring systems will work very fast on the fly will make the scoring on the fly. We start from the basic one, and then we start to improve it till we now use machine learning for our scoring.

The technological approach for this task grows year by year.

In the first three days we provided over 700 credits in Azerbaijan with our first operator - we were not expecting such success.

We're talking in India of about over 50 million customers. So numbers are huge.

And this is only in India we're talking about. But even here, in Canada, also, you have some microfinance, a lot of people are involved in this. And there's some very large institutions in India, which does this.

And I happened to be in a room where there was, I guess, $3 billionaires. And I said to myself, 'why am I a room full of Nigerian billionaires celebrating Nigeria, and the only thing I have to offer them is other people's businesses to invest in, right? Like, why don't I have a business for them to invest in? Why don't I have more equity in the things I'm doing?

I was very young. I was very hungry. So, knowing that, I wanted to have a deeper and richer relationship, I then presented an opportunity to the firm I was working with and to other investors, and I said, hey, I want to do this thing, this is a business.

What we do see is, working with larger providers, a real growth in adoption. If I think about the three months worth of data, live data, that we've got in the bureau - so December into January and Feb - interest free, predominantly online, for a term of no more than three months. There we see 3.8 million unique customers.

A big number, right? And they've made 50 million transactions, spending almost 20 million pounds. And it's not just your Gen Z. It's not just your Millennials, it's across all demographics.

And actually the fastest growing demographic is the 35 to 44 age band. Suddenly, I'm now just outside of that, but it's reflecting the point of becoming more mainstream, right? This is established, right? It's here to stay.

The market changed completely.

Certainly the processing time for applications, whether it's cash loan mortgages is reduced significantly - I'm talking about Poland, but in fact, these are the processes that I'm seeing across Europe, it's pretty much the same way that everybody's taking but some countries it takes longer time than others to to do that - but the market is now undoubtly very heavily regulated by the Polish regulator and most of the sector is subject to supervision.

A broader philosophy could be formulated as working together in order to achieve better decisions. And as Jonathan mentioned earlier, these decisions should lead to fairer and more inclusive financial services and the world in general.

I think the topics of the past conferences and the forthcoming talks really reflect this focus on the final objective.

Yeah, of course they are are many purely technical talks about the new machine learning methods and new sources of data. And of course, you can't build credit scoring models without technology and without data. But there will be also papers focusing on fairness, my direction of research, financial vulnerability, affordability, over-indebtedness... climate risk is becoming a huge topic recently.

And what influenced the interest rate is really, first, the inflation and then the unemployment rate. The good news is in the US and Canada, the inflation has peaked from the six months ago, they gradually come down - inflation in the USA right now is around 6%; inflation in Canada, just above 5%. For the unemployment rate, both countries have the numbers at historically low. So good fundamentals. The interest rate hike period is probably near the end, that's the signal from the latest Fed meeting. Actually, Canada already paused once in the March meeting as well.

But obviously, we will see some credit normalizations which means the other risk metrics will go up because we saw that the early stage roll rate began to trend up. But again, when you look at the trajectory, it just goes back to three years ago, right, before the pandemic, so I think fundamentally it's still sound.

Custom Credit was really set up with three mission statements in mind - we always are asking ourselves, does this move us closer to this or further away - and that is (1) to become the most customer centric FinTech in the UK, (2) is to ensure our colleagues better reflect our customers, and (3) to improve financial literacy, both in terms of our customers and the broader community.

I think the product itself is tailored and custom. But to achieve that, the way you score and assess risk needs to be tailored and custom, that's often where the problem is with these other kind of flexible payment lenders, most lenders will make a decision on on affordability based purely on averages to estimate your your expenditure. They will use a combination of the information you've provided to them, and an indicator from the credit reference agencies.

People with very different spending profiles and very different income profiles effectively could be judged to having the same level of affordability. So we've actually taken a different approach in that, initially, all of our customers will have to provide open banking data.

And then the credit scoring engine started taking shape. And over, you know, Covid, after Covid, we started bringing defaults down from 50 to 40, 40 to 30, 30 to 20, 20 to 15. And then, you know, the tougher bit came because it was not only about the credit scoring, it had to be a lot of engineering, then it's about the experience of the consumer, how is the product structured, you know, the first interaction of the consumer with the company till after he or she has repaid, everything matters. How the lead generation happened, how is the customer support interacting with the customer, or what has been experienced in app, what is experienced at the point of repayment, you know.

We've seen many times if the customer faces challenges in repaying whether or not it was our issue or an issue at the wallet side, the customers could turn rogue.

There was so much to it that we learned over time. And you know, when we actually closed our lending book pre-acquisition, the latest cohort actually close it under three per cent default

As we grow Smarter Loans, both Raf and I we're also learning about the lending industry and understanding that there's a lot more to it than just personal unsecured loans, than just unsecured business loans. So we started developing relationships, and then that got turned into motorsports and farming and equipment financing and buses and coaches. And today we have people financing aeroplanes and aviation. So it all grew over time.

And now if you look at it, yes, that's how we're able to say 60 lenders because they represent different verticals, you know, they're not all in like personal, unsecured loans, they represent a spectrum of different types of products and services.

There's over 700 craft breweries in Australia, 700 craft breweries for a population of around 23.5 million. It's massive. We've just had the top 100 independent craft beer votes at the weekend - this is a nationwide competition - and of the top 10, I have two of them on tap at the moment.

So Larry pale ale is one of my favourites and BentSpoke's Crankshaft IPA, but some of them are just becoming crazy. I mean, we've got biscuit ales on, I've got mango sours, I've got raspberry salsas- what you can do with a beer now is just crazy. One of our best sellers which goes very well with the climate here, using all natural produce, is a local brewery, Catchment Brewery up on the mountain. They do it a hot brewed ginger beer, which is a gluten-free, sugar-free and there's a sensational, refreshing bit of lime in there perfect for our balmy, humid summer days.

This weekend, we've got we've got live DJs, Friday and Saturday night. In fact, I take to the next Friday night, I'm a bedroom DJ with a few few beers in my hand - although since I started the bar, I've put about 10 kilos on, we get about five reps a week dropping off samples that they is for us to try, they want us to put on tap, someone's got to drink them...

I mean, a lot of it's the same from a credit perspective. You're very much looking at the borrower and their underlying financial situation. So you know, as a lot of customers are in the boating space are directors or have their own businesses, there's a lot of reviewing, you know, financial information, profit and loss accounts and balance sheets and the business's performance, looking at the management team as well and their experience and how the business has performed - just as you would do if you were lending on a truck or a piece of machinery.

The difference then comes more around the use of the asset and obviously the asset itself - where lending to businesses for assets that are income generative or crucial to the operation of the business, a leisure yacht doesn't tend to tick that box.

And so there's a there's a nuance in the in the way that the asset is used in the way it's going to benefit the business. And then also there's the asset itself. So understanding the inherent value of the asset, its depreciation, the impact that that has as well.

And of course, as you mentioned, digital has played a massive role.

You know, back in the day, people were just happy to get credit. Now, people are much smarter, they want things like loyalty, they want things like rewards, it's not just about the access to credit, they've got more choice. So certainly the space have become more competitive. The banks are still not there, but certainly a lot more non bank lenders have come onto the scene, people that are able to service customers quicker, faster, more efficiently than the banks.

And of course, if you look, now you've got products like buy now pay later that attracts specific segments of customers.

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.

It really started in analytics when I first joined. And I think for me, as you know, if you want to learn, strong analytics really helps you to make better credit decisions. So we really worked with Experian on scorecards, but we built out a very strong analytics capability in the business. I guess that goes from the data warehouse through to the frontend with dashboarding and all the analytical tools that you use, as well as, like the actual people capability that we bought in.

And it's helped us to understand our loan portfolio performance at a very granular detail and be able to tweak our scorecards very, very, very quickly to respond to to any credit risk portfolios to be seen. And it's helped us from a margin perspective has certainly helped us a lot to grow the business sustainably.

I mean, it's all a matter of perspective. Actually Creditinfo, in relation to the Experian and TransUnion and so on and so forth, is not as large. However, Creditinfo is a very fast growing organisation, and their focus is very much on emerging markets. And that was the piece that was really attractive, you know, the over encompassing idea is Creditinfo's focus is on providing access to finance within the emerging countries.

That was the main driver for me. This call to work with countries, to work in the parts of the world where everything is so dynamic, everything is so fast-moving, you take an action and you see immediately the impact of that one, you roll up your sleeves and you just basically get on with it.

So we did this that at the beginning, the very first experiments, the very first beta test, is if you want to borrow from Credilikeme, you need to post on your wall and have 10 friends vouch for you.

And then, like that, we came up with this term of crowdsourcing your credit score.

Definitely, that was non-scalable.

It was challenging, operational wise, but we learned a lot of the willingness, we learned a lot about the willingness of people to prove they're credit worthy when there isn't enough information. And we learn that if we create this circle of trust with our user base, they would be willing to share stuff with us.

I started very idealistically, I think, with this mental model that people want and need to borrow money, right? Like, I think that's why many of us are drawn to this industry, right? Because we see that if you need to borrow money, it's good to have options.

That was my mental model coming into it.

Overtime, I started to see really that so much of what was happening to consumers wasn't driven by how much they wanted to borrow, it was really driven by how much the bank wanted to lend. And I really wanted to understand those dynamics more.

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.

It's a good point that you make because I think that that's a very key differentiation. Alternatives will make money by consumers not paying, we make money by consumers payng us back. It's about using it in the correct fashion -customer defaulting, right, we're not making interest, we're not compounding that interest. So for us, it is ensuring that the customer remains within their spending limit, budgeting properly and using the platform in a responsible manner. In that way, our our vision and what's what's healthy for the consumer is very much in line.