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?

So if you were to think of some of these African countries and then go into Rwanda, you will understand that the credit growth and maturity is fairly new outside of South Africa, most of these African countries had predominantly been cash-driven markets, it's not to say there wouldn't have been a form of lending or products that would have been there. But if you think of the South African credit market or maturity curve, you know, retail, for instance, people go and buy clothes on credit, you have never seen that in any of the African markets, they get a shock to hear that you buy clothes on credit. So there's still a lot of whitespace, there's still a lot of opportunities for better products to come into play. So personal loans have predominantly been the default lending product that we've seen in the market across all the financial institutions in Rwanda.

But now mortgage, your typical mortgage, right? It's starting to emerge as a product, that historically people would probably just get a personal loan and go buy a plot of land and try to, you know, use their own income to build a house for themselves. What you have started to see off the lead is vehicle financing starting to come up in Rwanda. I'm not sure how close you are to the VW project that kicked off, I think, a couple of years back where they had a factory and wonder and assembling factory and wonder, because outside of South Africa, maybe with the exception of Botswana and Namibia, most of these countries do a lot of great imports. Right? So you find a lot of Japanese cars out here, right? So with that said, vehicle finance, the process of it has been much of a challenge because the bank doesn't know the vehicle, they're financing, they don't have confidence on the quality of the vehicle, right?

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.

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.

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.

Each time where I think that this cross border space between Brazil and the world has reached its maximum I'm wrong: every year it just keeps growing steadily and steadily and steadily.

79% of Brazilian consumers typically would divide their purchases into instalments. So this is a reality has been a reality. Now, we are also integrated with NuPay, which offers the user the capability to make a purchase with an offshore motion, and have the interest rate defined by no bank itself in the checkout process.

What we're focused on is to make the financial products available for merchants that don't have a local entity and a local structure in Brazil, so that they can great for the consumer base the same experience that our local ecommerce player can provide.

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.

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.

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.

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, 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.

We use the same standard of care across the credit spectrum, and we actively seek new ways to innovate the scoring process. It's the architecture of our model that allows us to score approximately 37 million more individuals than conventional models. And interestingly, to note about that 37 million, approximately 10.7 million are members of the Black and Latino communities who have historically often been underserved. And approximately 3 million of those have scores that are above 620, which is generally considered those that will be eligible for traditional lending products.

So inclusion and broadening access to credit and being predictive have been guiding principles for VantageScore since its inception.

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.

The mobile wallets information I see, in a way, as a parallel option to Open Banking. It's really where somebody's having their financial transactions give us an indication of their income or their ability to spend, as well as some indication of the consistency of salary over time and the income. So I think what we've seen is combining that with the credit bureau data, you know, really provides a really strong indication of risk.

And we talked about the different data sources, some of the data sources we may not be holding within the the credit bureau, but what we're trying to do is actually facilitate so we can provide decision modules where we're bringing together that data that may be held by the telco or the bank in the mobile wallet and combining that with the credit bureau data so it's easier to use to generate those decisions for the lenders.