The innovation of our solution lies in a number of areas, but I'll speak about two key areas: and that is our awareness of the importance that AI plays today in risk decision making; and the importance that data plays in making informed decisions.

On the AI front, Provenir's auto ML product allows our customers to quickly easily and affordably develop new or improved credit risk scores, which are instantly deployable into those credit decisioning workflows on the Provenir platform.

So you will say that there's lots of ESG scores out there that can be a blackbox, the company will be given a score, but you don't necessarily know that the Inklings behind it.

What we're bringing with our green score is effectively that transparency, bringing in SME climate experts for a single institution and try to build that score around what their views are and how they view it and align it to their pathway to net zero or ESG, or climate risk within within their institution.

Let's just say I find Small Business Finance probably the most exciting topic in the world. And the reason being, if you look at Liberis, we support small businesses with working capital, that directly impacts their livelihoods, it directly impacts their revenues directly impact how many people they actually hire.

So the impact on the wider economy is enormous. And the way we do that, with Libris is as an embedded finance platform, we integrate with big partners. And by integrating into those platforms, we see the actual data, and we underwrite on the basis of yesterday's data, or even last hours data, depending on how you know up to date their data is. And by doing that, we have an 83% accept rate at the moment. And that's enormous.

I mean, it's, it's difficult, because you've had cost of living crisis, you know, we've had COVID, you've had these sort of huge macroeconomic conditions that have made things really tough. But the thing I always struggle with is that when we have this conversation with businesses, you know, arguably, in many ways, your software's free to them. Because ultimately, it's all about their ability to collect their ability to return. And actually their ability to, you know, bring forward working capital improve their customers position.

And the thing that always blows my mind a little bit is that what part of this doesn't make sense to a business? Because if you're a business who've lent £100 million, right, and you've got customers who are in financial difficulties, it never makes sense to write that customer off? Right? It just doesn't make sense.

Everything I wrote in innovation, lab excellence is valid for AI deployment today, you asked a fundamental question. And it was one of the big points in my book, which is buy versus bill. And this is the same advice that I would give for an AI team today, you have to buy this technology, all but the very largest global banks like the JP Morgan's of the world, only a few of them are able to actually build their own technology.

So if you're looking at an innovation programme, or an AI programme, their job is to prove that this stuff can work. All right, their job is not to deliver ready to use code ready to use a AI that is ready for production. Because you really expect them to build their own large language model and know they prove it works.

And then you need to hire, particularly for the likes of AI, one of these larger firms is going to come in and hopefully have enough liability and insurance so that when your chat GPT style chatbot comes off the rails and give somebody the wrong answer. You can you can blame them with the losses.

.I think it was partly due to a chance that we eventually built Taran DM because it was at the beginning of 2020 and we had two interesting projects. So it was like a lot of potential and one of them was a decision management platform, like some customised one with scorecards for a new fintech. And the other one was a platform for a large German automotive company, which was supposed to optimise their supply chain. Middle of February we were flying to the German company, to the exporter. And we had a really good session with the management team, getting a lot of ideas on how to move it further. We were super excited about it, but as I said, like it was February 2020.

So when COVID was spreading within a few weeks, those guys stopped answering our emails and then we got back to them later, they said sorry, but our supply chains have gone haywire because of Covid, we cannot do anything a few months or maybe even a few years!

So that project got killed. And then we ended up with the other second big project, which honestly was, I think, a better fit for us because most of the team was coming from the finance, we had like the experience with infrastructures doing like real time decisions, whether it was credit risk, whether it was the high frequency trading was a large part of the team came from actual credit risk teams that may have been using the tools such as FICO Blaze or Experian Power Curve. We know the the strong points, the weak points, so big up to the I would say drawing board and we thought maybe there is like a market opportunity here or let's you know, let's basically build something.

Whereby from the report we see that 7.5 million people in Tanzania do have bank accounts. And with the presence of mobile money operators, we have 24.4 million mobile money wallets currently opened by these telcos talking into the lending space.

In particular, the banks still dominate the biggest share in terms of value, but the market has seen a new narrative of digital loans, which is mostly dominated by MMOs and fintech players through their micro lending services, which still requires banks collaborations by funding for regulatory approval, as well as managing the provision side with the help of the FinTech players will bring onto the table, the scoring and big data capabilities.

So that's why we work with Airtel to say, let's bridge this gap. Let's try to be the boundary between the lenders and the borrowers. And the only way you can do that is having standardised credit score.

So Airtel provides all the transactions when you buy airtime on your phone, when you buy data, pay a bill, you know, that information tells us probably you have some responsibility in your house, you're moving around, probably you have some kind of mobility, are you having so many people different one sending to you, tells you you're making sales.

But if you're just receiving from one person, we can can infer probably your law student getting some update from the parents.

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.

And I said, come on, what are you talking about? Everybody in Mexico has a mobile phone and in the region is the same, the same scenario.

So at the end, they are producing the little prints, I will say every second every minute of their day. So with alternative data, now they have the opportunity to go after this population.

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.

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.

But from our side of things, I think the main thing that we're seeing is however clever you want to be about it, however many propensity scores you want to use, however you want to segment your own collections portfolio and look to engage with people, if you're not able to contact that customer, it's going to be very difficult to get a good outcome for either you or for the customer themselves.

So because of those front-end online application journeys that we're capturing data from, and because of the recency - we're updating our full database - we already have a significant coverage of UK contact channel information, which we're able to help financial services firms get access to and to engage with their consumer, so they actually can get that conversation started. And they can try and get that resolution for them. But then if they if they can't have that first conversation, the rest of the clever stuff that they can do kind of goes out the window a little bit.

… one of the reasons that immigration is so essential to the US economy and to the US labour force is that our domestic population is not replenishing, our birth rate in this country is no longer outpacing the demographic shift as the older generation exits the labour force. And what that means in terms of US population growth is that today, immigration drives over 50% of the US population growth...

There are more people today that move to the US than there are people who turn 18 and enter the financial system… and so not having a dedicated strategy for how to attract and retain the recent immigrant segment is a formula to demographically lose market share over time.

"The risk of giving into temptation is as old as humanity. But there are reasons to think that people today are having to work harder to resist it, particularly when it comes to consumer behaviour. Digital technology has made it easier and faster to buy goods and services in an instant, without the delays of processing that once comprised an inbuilt cooling off period". This might sound like a headline from today's papers, but in fact it was from an article in The Financial Times published seven years ago, almost to the day - at a time when Klarna was around, yes, but only just beginning its global expansion, Affirm was only two years old, and AfterPay only a few months old. Welcome back to How to Lend Money to Strangers, the podcast about consumer lending strategies across the credit lifecycle and around the world.