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.

… one of the reasons that immigration is so essential to the US economy and to the US labour force is that our domestic population is not replenishing, our birth rate in this country is no longer outpacing the demographic shift as the older generation exits the labour force. And what that means in terms of US population growth is that today, immigration drives over 50% of the US population growth...

There are more people today that move to the US than there are people who turn 18 and enter the financial system… and so not having a dedicated strategy for how to attract and retain the recent immigrant segment is a formula to demographically lose market share over time.

"The risk of giving into temptation is as old as humanity. But there are reasons to think that people today are having to work harder to resist it, particularly when it comes to consumer behaviour. Digital technology has made it easier and faster to buy goods and services in an instant, without the delays of processing that once comprised an inbuilt cooling off period". This might sound like a headline from today's papers, but in fact it was from an article in The Financial Times published seven years ago, almost to the day - at a time when Klarna was around, yes, but only just beginning its global expansion, Affirm was only two years old, and AfterPay only a few months old. Welcome back to How to Lend Money to Strangers, the podcast about consumer lending strategies across the credit lifecycle and around the world.

Yeah, it was about helping a friend to get on the property ladder. She's done very well in her career now, but at that point, she needed some help. And she was like, I really want to stay in my local community, and I really want to buy with my husband here. And she just asked us on WhatsApp if we'd lend, but it was just a lot of money to a group of people at that time. And it was like, how are we going to get paid back because we were all recently graduated. So although that loan didn't happen, that's kind of what inspired the idea for JustLend. I wanted to provide opportunities to people could do what they wish to do.

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.

…there had to be controls that were appropriate, but manageable in an environment where you've now got people working from home, and speaking to customers on a daily basis. The peripheral technology around the collection systems needed to be able to distribute into those people's homes... What I found really fascinating is, there had already been a shift to digital, and to using digital interaction points – we’ve seen it a lot in the acquisition space, we've seen a lot in the management space, but, historically, the collection space have been very slow to follow up. But what we're really seeing now is a more significant shift to allowing customers to interact through digital portals, and to set up payment plans, and to be able to access information about their accounts so that they can make an informed decision...

we have spent a lot of time recently looking at the infrastructure between data, applying analytics, and we're applying machine learning through our data-driven decision engine, to then differentiate how treatments are applied in the operational systems. And the real key for me is that ecosystem should be fully integrated. And it should be a continuous loop so that you continually are learning from the outcomes that you get from the actions that you apply to those customers, whichever segment they fall into whatever treatment you apply, understanding what's been successful, what hasn't been successful, introducing champion-challengers wherever you can, to test new options, but also to ensure that the quality of the data that you're pulling in to help with those decisions is at the highest standard…

I think the unprecedented level of government support and the way that lenders and consumers alike have been able to help keep the ship afloat, mean that it definitely could have been a lot worse than what we've seen up to date. But to your point, there are still plenty of consumers, hurting out there, and hopefully are getting that assistance as they need it.

So we actually as home prices started coming back up, we saw that consumer started reprioritizing their mortgage ahead of credit card. And what we saw, you know, actually starting back in like the first quarter of 2017, we first see that mortgage overtakes auto as the primary payment. So the phenomenon of mortgage becoming the highest ranked actually started well before this pandemic. And then what we saw in the pandemic, was the separation between auto and mortgage delinquency got even bigger. It's likely due to a number of factors, as we talked about the accommodations, for sure, you know, suppressing that delinquency, but also, you had so many people now working from home that they had to protect their home, they might be willing to maybe let one of their autos go, because they weren't going anywhere, you know, and people weren't taking road trips, and they weren't worried about their car, they had to make sure that they had a place to work that coupled with the home price index in the US.

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.

It's not the only that the banks won't give the card to them, because they are too young, they don't have enough income, or things like that, they just don't want to go through the more tedious process to get a credit card… now you don't need to go to any banks, you don't need to produce any documents, and you can get a loan in maybe a couple of seconds.

We are always on the lookout for new data sources, or external providers, and whenever we see something that's interesting, we test it… In the end, it comes down to what can we pay per gini point of lift?

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.