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.

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.

It's still dominated by your credit card issuers.

So most people if they have Apple Pay or Google Pay, they have loaded their credit card and that's probably the most popular form of payments, but these QR payment providers who have their own mobile apps is very, very popular. And it's accepted everywhere. And while they still represent a very small share in terms of total purchase volumes, they are by far the fastest growing, and that is why issuers are very, very concerned.

And these QR payment providers are also going into that credit space, where they're offering a small credit of maybe $500 to $1,000. But they're using very basic information - just your name, phone number, email - so it's much, much faster and easier to apply for that new QR payment credit versus a traditional credit card.

All it takes is one bad egg to throw a business off, especially in what we're doing where it's small startups and scale ups - the impact that that person has is so broad that when it goes well, it's such a great feeling, but equally, if it goes the wrong way, and you hire the wrong person, they don't fit into your company values, your ethos, your approach to how you work, the effect goes just as much the other way.

It's not just around here's the CVs and putting bums on seats. For us. It's really about embedding ourselves in the business.

There's definitely this talent shortage. But I think there's a lot of people trying out new markets, a lot of movers and shakers, you know, you've got a global workforce, you can have people in Asia, in Africa or in North America, we're not speaking to people and saying if you can double my salary, I'll go there is people saying I need to have remote working, I need to spend more time my families and that purpose.

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.

The market changed completely.

Certainly the processing time for applications, whether it's cash loan mortgages is reduced significantly - I'm talking about Poland, but in fact, these are the processes that I'm seeing across Europe, it's pretty much the same way that everybody's taking but some countries it takes longer time than others to to do that - but the market is now undoubtly very heavily regulated by the Polish regulator and most of the sector is subject to supervision.

There's over 700 craft breweries in Australia, 700 craft breweries for a population of around 23.5 million. It's massive. We've just had the top 100 independent craft beer votes at the weekend - this is a nationwide competition - and of the top 10, I have two of them on tap at the moment.

So Larry pale ale is one of my favourites and BentSpoke's Crankshaft IPA, but some of them are just becoming crazy. I mean, we've got biscuit ales on, I've got mango sours, I've got raspberry salsas- what you can do with a beer now is just crazy. One of our best sellers which goes very well with the climate here, using all natural produce, is a local brewery, Catchment Brewery up on the mountain. They do it a hot brewed ginger beer, which is a gluten-free, sugar-free and there's a sensational, refreshing bit of lime in there perfect for our balmy, humid summer days.

This weekend, we've got we've got live DJs, Friday and Saturday night. In fact, I take to the next Friday night, I'm a bedroom DJ with a few few beers in my hand - although since I started the bar, I've put about 10 kilos on, we get about five reps a week dropping off samples that they is for us to try, they want us to put on tap, someone's got to drink them...

And of course, as you mentioned, digital has played a massive role.

You know, back in the day, people were just happy to get credit. Now, people are much smarter, they want things like loyalty, they want things like rewards, it's not just about the access to credit, they've got more choice. So certainly the space have become more competitive. The banks are still not there, but certainly a lot more non bank lenders have come onto the scene, people that are able to service customers quicker, faster, more efficiently than the banks.

And of course, if you look, now you've got products like buy now pay later that attracts specific segments of customers.

It's a risk, yes, but it's also a risk not to do it.

As examples, in a number of Asian countries customers will not pick up the phone if the call comes in as an unidentified number - or if they know it's the collection department number. SMS has worked very well in India. In China, for example, a lot of the interactions are via mobile phone applications, like WeChat. SMS is something antiquated, like Facebook, for many young people in Asia, because they've all got mobile phones now.

In Mexico, a personal touch is still required, so if the customer can't pay, in most cases they turn to their family to get them help and so the solutions take a bit longer. In Hong Kong time is money, so if the customer picks up the call the duration is a lot shorter, as is the resolution. And in a place like Australia, mentioning the word 'hardship' means the customer gets preferential treatment due to the customer protections in place. So that usually it comes up early in the call, if the customer is aware, which most of them are.

And therefore, you know, the situation changes and the people handling the call need to move into that mode of understanding the customer situation a lot more.

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.

Well, let me paint a picture of the banking landscape at that time, the banking landscape in the 90s in Ghana, West Africa, we were just beginning to wake up to the advantages that digitization could bring in terms of transforming from manual processes to technology-based processes. And more of automating the manual process, rather than looking at the processes end-to-end and transforming them. Very, very basic accounting, very basic bookkeeping, and all of that.

And nothing really digital as we know it today, because still if you wanted a banking service, you had to go to the bank physically, all that really had to change was that we're able to do a lot of transactions in a shorter period of time. And our books were more accurate.

But by the mid-1990s, I had started getting a little adventurous with what we could do with the technology stack that we had.

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.

You have to operate in the realms of the known and not in the realms of ‘well, we think this happened’. And that's why you absolutely have to have a champion/ challenger strategy, as difficult as it may be.

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.