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 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.

And where I found that problem statement, that focus, was actually when I started working with community finance lenders, specifically, an experienced that really exposed the problem, to me that exists within consumer lending. And that is that very little is given to the customer management side of the credit lifecycle.

And I've had the opportunity to sit within the community finance lenders office, I mean, this was right on the front line. And I remember there was a lady that came in, and she had lots of children with a, she was stressed because she was in arrears. And she come into this lending office to arrange an arrangement with the lender to ensure that she could stay on track with her payments. And I just sat there observing, and she sat there and she was getting more and more stressed, as the advisor was saying, Well, can you afford this much a week? Can you afford this much a week, and having the awareness as to all the data behind the lending decision, and everything that they had about it, I just felt like, there's got to be a better way to engage this customer and use this information to help her make sounder financial choices.

That was my lightbulb moment.

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.

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.

Okay, so I knew that if Nigeria was going to grow and the middle class was going to emerge, there has to be a credit culture, right?

And I knew that one person wouldn't be able to do it. One lender wouldn't be able to do it.

Because when you look at Nigeria and look at why credit doesn't work, you need to understand that is a lack of consequences that killed credit. In Nigeria today, if you took money, and you don't pay it back afterwards, nothing happens to you. Now, one of the things that Lendsqr is doing is that, by having a technology driven consequences, then that problem is going to go away.

So going live with Taktile is, I would say, very smooth and can happen rapidly.

Once we sign a customer, our customer success team provides training and support for the platform. However, many of our customers prefer to just building and figuring it out on their own on the platform.

On the other hand, on top of the software, we also have customers to identify the right data providers for their use case and for that customer segment. And we do share best practices from our experiences in the industry. By now we've seen so many products and use cases that we can tell you if you want to launch a lending product in the UK. For that type of segment, I think we have a very good idea of what are the possibilities out there of data source that you can actually use in the end.

Most decisioning systems rely on an opaque patchwork of siloed teams and data streams with insufficient oversight and control.

Many decisions, therefore, back to the tech line that you just mentioned, rely on guesswork and instinct. And this leads to bad decisions, and costly mistakes and disappointed customers.

This is why we founded Taktile in 2020 to change that.

Taktile has offices in New York City, London, and Berlin and serves as the backbone for risk, for pricing, and fraud teams across financial services. It enables decision authors to enrich internal signals with data from our rapidly growing data marketplace and flexibly express their desired decision logic - and all of that without actually requiring engineering support.

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.