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.

Now, why did I make the choice to go to Kigali or to East Africa? One, the sister of my grandma lived in South Africa and in Zimbabwe, and my grandparents were there to visit them. And they came back with all these beautiful stories. And that sparked my imagination to the fullest, I would say. So in the past years, I went there on the holidays, and I decided to make that move, and to go there. And I really wanted to do something where I could bring my expertise and my experience, and having worked with the biggest banks in Europe, some of them even in America, gave me a good foundation to join Simbuka and to make lending simple, also for the people here in East Africa.

And so when we compare it against consumers that don't monitor their credit, we do see materially higher score improvements for those credit monitoring consumers, relative to the others, and does the active credit monitoring actually result in your score improving now there's nothing about looking at your credit score that causes your score to improve, it's not like every time I hit refresh my score ticks up.

But it's the same idea with joining a gym or a health club, the act of joining the health club doesn't cause you to improve, you actually have to show up and do the work, lift the weights and stretch and do the sweat stuff.

Marquee investors like Warren Buffet, Jack Ma, Masayoshi Son are aggressively investing in FinTech ecosystem and fintech startup.

And the reason it has happened is just one: 10 years ago, the dream of every person after completing the Master's in finance, was to get a job and private equity or hedge fund or investment banking. Now, it is not the case, every person who graduates or comes out of masters wants to get into startup ecosystem, or want to start a new startup.

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.

Ultimately what is important for us is to serve the merchant. So as we hear from our micro small business borrowers and learn a few things, we would love to share that again with the community.

Our realisation and build is the opportunity to serve this cashless commerce segment is so huge, and there's just a massive gap between how commerce has become digitised the recovered how many millions of merchants went online, but at the same time that digital credit hasn't really caught up.

So there is still a huge gap between what is a Shopify merchants ability to go online and sell in like, three, four hours. But what happens to that version, if she wants credit, it's not going to happen every 24 days, this this gap is huge. And you know, we just want to be part of a solution by no means any single entity can solve all of it.

So I think one of the problems is there's not a very clear cause and effect when people are making this argument. It's not like the high cost credit lending market halaves and four weeks later, there are twice as many loan sharks.

One of the things we know from our victims is an awful lot of them haven't tried to get credit from elsewhere. It's not that they've gone down the credit ladder and ended up at the bottom, they actually haven't tried to get credit for elsewhere, because it's not for the "likes of me" or they've tried 10 years ago and would declined, and nothing's changed in their situation, so they don't think they would lend now.

But sometimes people have got access to credit. And they still borrow from loan sharks, because nearly half the people we helped last year thought the loan sharks weretheir friend.

YAt Gaia, our mission is to make the process of IVF, more straightforward, more accessible, and more affordable. So the work we're doing is driving towards enabling more families to have children, and means we have the opportunity to make this truly tangible impact on our members lives.

And so it really is this mission, I think that elevates things beyond just another plain vanila financial services company.

It is a niche focus, but it is a focus that I think goes a long way towards solving this kind of very tangible problem of infertility and helping families build from the ground up. And so our product really is organised all around trying to focus on providing additional accessibility and affordability for IVF through a combination of a loan and an insurance product that allow for an easier entry point for our members to get access to treatment.

You're unifying data from different countries into one single hub, we are standardising that, we are analysing the data and presenting it to a bank's over one single API. So for a bank, it doesn't matter if the customer is coming from Poland, Germany, Spain, Italy.

Typically banks have share of foreign customers 10% to 15%. Okay, 10 to 15% is good enough to care about, but the problem is that this 10% to 15% are not coming from one single country. They are coming from the twenty seven or even more since they outside of EU like the UK, Switzerland, etc.

So quite a long list of a country - this is a problem. And it's not feasible for one single bank. They will build the data pipelines and try to build the knowledge and standardise now it's just too expensive.

But in our case, it is okay, because if we are building this infrastructure, we can sell it to different banks, and each bank is paying their share. But of course, there are plenty of hurdles not only technology hurdle, various legal hurdles. Also we have European Union one same, same legal framework, as it said, there really is in the details. So in different countries, it's still a bit different than we are solving these hurdles.

Digital marketing was not really a thing. It started by just going out and networking, listening to people, asking them what worked, what didn't work, you know, ringing up estate agents saying, look, I'd love to do your mortgages and walking up and down the high street with my briefcase, really. I just recently went with my cousin who works for Virgin Atlantic on a trip to Austin, and I ended up doing both of the pilots mortgages!

Like all businesses, it's about understanding the customer's needs, their pain points, and trying to work out where we can stand out and make a difference and have an incredible customer experience and good customer outcomes where others can't.

Just to rewind a little bit, this whole BNPL craze was exploding. I hadn't seen anything like it: interest free credit didn't really make sense to me and was very excited about the prospects of this business model. But at the same time, a lot of the people I spoke to in banking and lending were just like, the last thing South African needs is just more unsecured credit being piled on top. This isn't Australia, isn't Sweden where Klarna is from.

So I did take that to heart and looking into the BNPL space realised that in countries like Brazil, Mexico being two significant examples, point of sale interest free instalments have been there for ages offered by most retailers. Yes, it's now become tech enabled. But in those markets, it's done very much off the back of a credit card. And the bank is the key issuer of their credit, they understand the consumer and what they're earning. And the thesis was that the South African use case was far more similar to those markets, developing markets, high interest rates, very disparate levels of income. In some places, you'd argue over indebtedness of the middle class, as opposed to a massive need for financial inclusion, which is the narrative.

Yeah, and I just thought that that product would fit so well here, we just have to find a way to technically adapted and created here.

And then the other piece of the training that I do is I work with senior analysts as they begin to step into their first leadership roles. Because the other thing that is hugely bought by I found very relevant to my world was that when you start leading, it's as if someone goes here, the keys to the car, if you go, you mean you want driving lessons to beat after school drive.

And a lot of us analysts types are super introverted and really fact oriented. And we like our processes. And we don't necessarily do the soft skills particularly well. Yet we all respond really well to those being done well, but they don't necessarily come naturally to us. And I include myself in that. But it's something that we need to learn and be aware of how do we communicate with people up the chain? How do we take analytics and when someone says, but I don't understand. As an analyst, our tendency is to double down on the detail. And when you know what you're looking for, you can literally see people lose the will to live because they don't understand and that isn't helping, and they're not number oriented. So just make it stop.

.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.

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.

Australia for whatever reason, is unnecessarily divergent and complex, not in the pricing aspect of the lending, that is fairly competitive, but when it comes to the approval part of the process, there is a huge amount of customer confusion as a result of that.

And one, if not the highest penetration of mortgage broker deals as opposed to direct lender deals. The way a traditional broker would solve that level of complexity is through deep knowledge and expertise and experience in having done lots of deals, and usually a close working relationship with a small number of banks. And so the reality is, and industry data supports this, that most brokers will typically use one, two, maybe three lenders for overwhelmingly 80% of their loans.

When we looked at online mortgage broking we looked at how might you make that better and different investing in incredibly high quality tooling for the broker to turn that broker to a superstar?