.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.

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.

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.

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.

We are at the station in where the market is making a shift. And this is an interesting time. It's great for us to be part of that shift. And I think we're really seeing it where you know, US regulators pushing for fraud today email integration for Model Management for how do you take the unknown risk the ethical All parts have all those false positives that are being generated.

So it's a great shift where the market needs to move away from the old school: I've seen a fraud so now I think I know all fraudsters to, I've seen a good customer now I know what a good customer looks like.

That's a great conversation that we want to be part of. And we want to hopefully lead but definitely be an active member.

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, 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.