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.