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.

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.

So we've created a first-world infrastructure, but it's been an uphill battle to get lenders to embrace the use of bureau data and credit scores and bureau scores to make mass decisions. In Nigeria, it's a legal requirement that you perform two credit inquiries, but that's not to say that you must make good use of the data you're given, as long as you know, you've met your legal obligations. So there hasn't really been an appreciation of how that data can help you improve your decision making. So that's the journey that I've been trying to help my clients to realise - it's been as an educational upskilling, a sort of training project as much as much as a hands-on rebuild the bureau score project.

I think one of the cultural changes that's required is the willingness to invest money in order to save money, that thinking hasn't really been that prevalent in Nigeria, in the past at least. Curiously, that's the first time I've ever encountered a situation where lenders have questioned, well, why are we paying for a credit inquiry if we ended up rejecting that customer? Why would we want to pay for that? Which kind of misses the point of the protection that screening for risk gives you.

There's a whole bunch of people out there where the traditional model doesn't work, there simply isn't enough information on these people to make a reasonable credit call...

This is a space where lots of people are working, but very few people can claim results. Because this is also the sort of space where lots of AI propellerheads think they can crack the problem. To some extent, that's true. This is also the classic case where progress is both hindered and aided with experience. It's actually good that some youngster on a beanbag, with long hair, thinks about this stuff completely unhindered by any previous industry knowledge, because that's anyone with too much experience probably thinks too much inside the box. At the same time, with something like credit, you do need to have some other people in there who can say, 'well, yeah, that's cool but you need to take these following five things in'.

That doesn't mean that the thinking needs to be restrained, but someone needs to make it practical in the end. To simply let the same space cadets go mad on this is likely to land you in a heap of problems, if you don't actually understand the lending industry.

So that was my focus point is, if you've got nothing, that's where we start… for existing clients, you can just go with the Chief Operating Officer to a branch, have your scoring, talk to the loan officers about the clients, they know them, right, you'd be surprised by how many they have but they know them by name, and test the scoring.

The common feature here was that, like banks were slow to be on the take up of the scoring methodologies, FICO was slow to see the value of bureau information. And for that matter, the credit bureau saw FICO as a competitor, they didn't see FICO as somebody that they could collaborate with. And yet nowadays, a FICO score is synonymous with a bureau score.