Explainable AI and a new style of credit bureau, with Evan Chrapko

We eat volatility for breakfast, we make love to volatility!

Which is handy, because we’ve all heard it, and, well, most of us have probably all said it, the world is very volatile at the moment. So when Evan Chrapko of Trust Science told me how their models embrace volatility, I knew we were onto something interesting. Even more so, because I spent a decade working in the credit bureaus myself, and that’s the industry that Trust Science is disrupting right now with its volatility-eating models.

As mentioned in our chat, your first port of call for further information is https://www.trustscience.com/ (though they are also on LinkedIn at https://www.linkedin.com/company/trust-science/)

Or you can jump straight to the news articles about their work https://www.trustscience.com/articles-page (including the Mail and Globe article we spoke about)

Evan was also recently at LendIt USA, and you catch a video clip behind the scenes here: https://www.youtube.com/watch?v=34PrP7Ak4aw

You can learn more about myself, Brendan le Grange, on my LinkedIn page (feel free to connect), my action-adventure novels are on Amazon, some versions even for free, and my work with ConfirmU and our gamified psychometric scores is at https://confirmu.com/ and on episode 24 of this very show https://www.howtolendmoneytostrangers.show/episodes/episode-24

If you have any feedback, questions, or if you would like to participate in the show, please feel free to reach out to me via the contact page on this site.

Regards,

Brendan

The full written transcript, with timestamps, is below:

Evan Chrapko 0:00

You've taken me back to the farm and dad and advocating for knowing what you're ingesting - being able to certify that what you say is there, is there, or what you say isn't there, isn't, right!

Prove it, prove it, prove it prove it. Data science has advanced far enough that there are really reliable platinum grade ways to make some of these assessments of models or scorecards.

Brendan Le Grange 0:28

I have worked in consumer lending for 21 years. I work in FinTech now - check out ConfirmU and our gamified psychometric scores in Episode 24 of this show - but before that, I spent a good decade working with the credit bureaus in Copenhagen and Hong Kong and Leeds. But as it turns out, those may have just been credit bureaus version 1.0... welcome to How to Lend Money to Strangers, with Brendan le Grange.

Evan Chrapko, welcome to How to Lend Money to Strangers - it's an absolute honour to have you on. You're the founder and CEO of Trust Science, an evolution of the traditional credit bureau that seems to have a particular focus on identifying the 'invisible prime', those low risk borrowers who simply lack the data to make them obvious to traditional models.

As someone who spent eight years in one of those traditional credit bureaus myself, I'm really looking forward to learning more about what you're doing there, but before we get to that point, you are first and foremost an entrepreneur - so what was your background prior to founding trust science?

Evan Chrapko 1:49

Hi, Brendan. Thank you, I really appreciate you having me on the show.

I'm a farm boy, I grew up in a remote region of Western Canada and so my forefathers and myself are necessarily self-sufficient and inventive. We were on a mixed farm that my dad thought should be purely certifiably organic - so no chemicals, fertilisers, pesticides, or herbicides or anything like that. It earned him a lot of teasing or derision, and left people in the neighbourhood wondering if he actually knew how to farm!

And he turned that back on them and suggested that there was a right and a wrong way of doing things, which is also part of my upbringing, and had me become a standard bearer for innovation. So alternative crops, and doing things in a way that were not necessarily considered conventional or considered at all. And then dad ended up president of the certifying body for organic food production in the region.

And so it just comes natural to me also to be a standard bearer for consumer protection or having things in a way that can be proven as being good for you, or as advertised. I then got a CPA and a law degree because I felt that where I grew up was to my disadvantage - so I wanted to make sure I had lots of credentials/ a good education in the outside world on route to becoming, or maybe staying self-sufficient or being in a self employment mode. The farmer is the epitome of being self sufficient.

I had my mind my eyes set on business, or taking something and optimising it: an early win was, at the time a record setter, now I believe still in the top five or top ten in Canada as a startup that went from less than zero (I emphasised the less than, starting on credit cards and that whole kind of story) to an exit of $568 million US in 30 months.

Brendan Le Grange 4:05

Yeah, I love that it goes back to your father and carrying that booty own career. It's a lovely, lovely line there of the family tradition, and then in 2007, so a full 15 years ago, which in FinTech terms, or in general technology terms, is a real lifetime ago, you founded Trust Science on the theory that AI and alternative data could be used for credit scoring.

What was the inspiration behind that move? And what was the founding story there?

Evan Chrapko 4:32

You're digging at some old wounds there the founding, the inspiration or the genesis was having been burned for the third time in a row by business partners (different incidents each of those three) - after the third one, I was exasperated and feeling sorry for myself of being cheated in business and determined that maybe I just didn't have a very good judge of character or imparted my trust too readily in into others: and being a technology commercialization guy or innovator in technology, I felt there must be a way to get my gut out of the equation systematise or digitise that effort.

And I knew instinctively that that had to take advantage of what was then much less well understood, but not new, technology: AI. AI has in fact been around for over 40 years now, AI and big data was not necessarily a term that would have been well understood at the time either, but those those two married together, I thought, could help deliver the science of trustworthiness.

It's so happens that I was in Edmonton at the time, and that still is in the top three or five, or if people think that's an exaggeration, absolutely in the top ten cities in the world for AI expertise and prowess. And a fish doesn't know it lives in water, I was swimming in a very AI savvy environment. So so it did come naturally. But not because I'm as smart as all those smart people, I just knew that it was possible.

We got going, as you say, in '06/ '07. But in technology commercialization terms, I recognised that we were way too early for our own good - there is such a thing, you can be too far ahead of the market - and that's basically summed up by not getting any purchase orders. And so I got all the meetings I wanted, but I was mostly I think getting those meetings with C-level officers of banks and lending companies because they wanted an education on AI. Unfortunately for me, the meetings devolved into joking around about HAL and Skynet and what they knew of AI from the movies. So no, no purchase orders means it is a technology commercialization effort, you must not bleed out, you must not bang your head against the brick wall and spend all your money trying to educate the market.

What we did was to go into a form of cold storage, we continued lots and lots of very intense, hardcore inventiveness. Maybe my my legal background helped with the following, we also very aggressively protected those inventions by pursuing patents all around the world is not just in our backyard, but globally, many countries now, that kind of stopped counting after 35 or 40, we're approaching 50. Now, including a successful offence use of these instruments. My patents are not merely you know, wallpaper.

And then the world tuned in. Gartner talks in terms of intelligent applications being passed the trough of despair, we we are happily cited by Gartner as an example of intelligent applications past the hype and now coming into delivering transformative value.

Brendan Le Grange 7:56

You've been spending this time investing heavily in terms of money and hours to build out your product and bringing what you're calling Credit Bureau + to the world. I'm from a traditional credit bureau space, you know, credit bureau 1.0, how does Trust Science view of the modern credit bureau look different to that of the credit bureau that we've all grown up with?

Evan Chrapko 8:19

Yeah, we had the supreme advantage of starting with a blank sheet of paper and looking at the incumbents who are decades and decades old are long in the tooth. And so we had our opportunity now to disrupt the space or, or essentially create a new category being foundationally. And then in a legitimate way built in a machine learning environment and infrastructure that can actually learn we're, we're very call it smart, we have logic.

On our side, we're not a passive library for a lot of data we don't see ourselves as as only a data company, we have an extraordinarily powerful, scalable and secure infrastructure that customers can interact with. We're lightning fast in terms of our infrastructure and delivering results as as well as in terms of being a nimble and small company designed to deliver software as a service. So we're not labouring under the weight of a, you know, an old infrastructure and a bygone way of doing things.

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 to answer your question differently, I'm built in a way that I can dig out any answers in the data that exists. So I'm not a one trick pony on delivering a credit score probability of defaults, I'm not even a one trick pony on credit for that matter. But that's that would be the subject of a whole different podcast in lending in the credit space, I can be a real partner in a bi directional way with your infrastructure and software, I can help you reach a recommended loan amount in an automated fashion, I can give you recommended interest rate for this consumer for this kind of loan at this dollar amount, I can help with direct marketing to get your material in front of the best borrowers who are the most likely to take up your kind of loan, following the borrower in their entire lifecycle from lead to loan, I am now being harnessed by the securitization folks, right as an alternative ratings agency.

And so we're landing now we're landing deliberately and very focused way in the lending space. But there's a lot more under the hood, the capabilities deliberately broad, but our execution is also deliberately laser focused.

Brendan Le Grange 11:41

And if we just think about the traditional model, so yeah, I'm in the UK, and there's price caps on utilities, I can reevaluate it from time to time. And if we imagine now, I think it's October that they're rising again. So in October, the gas I use in my house is more expensive, I'm going to use that for all of October, then in November they're going to send me a bill, I'm going to pay that with my credit card, that will get billed by the bank at some time in December, and then I'm going to pay that at some time in January, they're going to report it to the credit bureau sometime in February or March. And it's going to be rolled up into my credit score maybe in April.

And so an impact that hit me in October, and maybe has put me over the edge of affordability you're seeing in March or April. And usually that's alright, because usually we're at a pretty steady path, but the world's anything but steady at the moment.

So I think there's a lot of focus now of how quickly can things change? How quickly can we see information because if somebody gives me the right strategy, now in October, November, when the gas price goes up, I can fix the situation or control the situation in April, it's too late. So it's really interesting for me to hear that as well. And I think another key difference if the traditional credit bureaus were not owned by the banks, but built for the banks were have been really impressed reading through your website of your clear focus front and centre on revealing this true risk of this invisible prime this idea that the these consumers that are genuinely of low risk, but they simply don't have the traditional data that indicates that it doesn't mean they're any more risky, you just can't see them and your data, the extra ones you can extra fields you can bring in can simply reveal this, you're not changing it, he doesn't making it clear he has consumers that you could lend to - and that seems to me more of a 'how are we going to help the consumer, how are we going to really look at an individual level?' rather than 'let's start from the banks and and work outwards'?

So what is it that you can do so much different to what a traditional credit bureau can?

Evan Chrapko 13:35

You nailed it when you talked in terms of the freshness of the data. That's something like that alone, being able to get data into a form that can be harnessed more quickly is half the battle, especially in the so called subprime world, many things can dramatically change someone's life circumstance or capacity and ability or willingness to repay debt in two or three months, let alone the two or three quarters that you describe that it often takes to get data marshalled into position to then affect a score, let alone the model itself and the scorecards depend on and the old way of doing things depends on tomorrow and six months from now looking like yesterday and six months ago, and nothing could be further from the truth.

Russia's war on Ukraine just started less than six months ago and it's already affected dramatic aspects of the global economy and consumers you know, existence in the way they conduct themselves in the economy. And you have scorecards right now and conventional systems or credit bureau 1.0 infrastructure or in house infrastructure that were built three and four years ago or more.

Those models have never seen a Coronavirus, let alone a land war in Europe and are they learning on the bench sort of the latest, you know, all this volatility that I'm talking about. The other thing is who said it was a good idea to leave the consumer out of this equation now 50 and 80 years ago, when FICO got started, and so on. There were no mobile phones. The fact that a consumer could digitally enter or have a digital profile was not a thing. While it's definitely a thing now, and privacy laws acknowledge as much a consumer has the right to say what you know about them.

Oh, and by the way, the flip side of rights is opportunity, they have that opportunity to participate in being assessed. Now, if the infrastructure would allow it as we do as trust science does. It's optional. You don't have to use it as a lender, you don't have to ask for it from the borrowers. But we absolutely have that front and centre because our atomic unit and trust sciences mission.

In fact, while my customers are lenders, our mission is to allow deserving people to get what they deserve. We're manifesting that through lenders helping lenders decide who is deserving of their money. And the bell curve, or how consumers get assessed right now, has a lot of data points in the long tail that don't belong there, the invisible primes or the hidden primes, but for reasons of inability or a lack of capability or, putting it bluntly, the big bureaus are serving the big banks - or they're serving everybody or trying to but below a score of 700, they're on thin ice, they start running into the thin file issue, vis-a-vis their conventional infrastructure, a thin file or the no hit issue, vis-a-vis again, their conventional hub and spoke model for the way they gather data.

It was fantastically innovative in 1955. And FICO got going but there are many, many more ways now to gather data involve the consumer on a permissioned basis, be respectful of privacy law, make sure you're compliant with the FCRA and ECOA in the US and similar legislations and other countries and let's stop treating the unbanked or the invisible prime the wrong way you're you're hurting the economy actually, let alone your individual lender customers.

Brendan Le Grange 17:25

It's a message we've had a few times on the show but if you think about a super high risk portfolio in North America, if you're talking about a portfolio 20% bad rates, people are going to be terrified of that as lenders, but it still means four out of five people there are paying the loans back. So the natural conservatism within the lending space means that things that are seen as exceptionally high risk at a portfolio level if we were to humanise them is to show a lot of people getting left behind simply because they can't be identified. They're there. The math tells us in most people in their portfolio are good, but there's just not a way the traditional model to have done it at scale.

So they were left out.

And yeah, I always love to hear innovation, that's really acknowledging that but you're talking about different scores and talking about famously conservative lenders, I imagine when you're trying to sell a new score, you're trying to say here's something it's built on machine learning and AI, most of the people you're selling to would have grown up through the school of writing their their scorecards in SAS and regression models and probably been a little bit nervous of the idea. How have you seen those models perform? You obviously said they thrive or volatility and they've had all this volatility in the world now.

Evan Chrapko 18:34

Yeah, the Chief Risk Officer or the Chief Credit Officer dealing with an asymmetry on time where you have certain amount of information at the moment, you have to release your cash, you get other and different, you know, more information when someone who has taken your cash then performs later. And it's hard for them, they are a cost centre, they're the last line of defence on whether or not the money goes out the door. It's a hard job. They're doing great things and they're doing them with the best tools that they may have available.

But our favourite is the CRO who wants the best for the employer does not have not invented here kind of default. And so we have partnered our customers right now are the early adopters are the innovators themselves who, you know, they have a great degree of self confidence and they really just want the best answer and they will fearlessly overlay a +, a Credit Bureau + or anything else that's additive to see. And a lot of them, as early adopter and as innovative as they are themselves, are from Missouri - they say it's the 'show me' state, prove it, prove it, prove it, prove it.

And fortunately for all of us, fortunately for the consumers, data science has advanced far enough that there are really reliable platinum grade ways to make some of these assessments of models scorecards, if you will, or retros, and back tests are our time honoured time proven way.

Now here's where the volatility gets in the way and why you need something that's maybe a lot more capable of being responsive in real time as you move forward in time trying to do retros and back tests in a world that's just gone through two and a half or two years of the pandemic lock downs. Global economic distortion is also problematic. And so your alternative if you had it would be something that can track very, very quickly upon these events or changes in the data landscape. But COVID in that way helped it's hurting, it is toxic, dripping on the heads of conventional scorecards, it's helping me because as I said earlier, I am revelling in volatility a learning system takes input from it feeds on the edge cases and the changes and the directional moving data.

And so I've been heard to call COVID in the children's fable of the Emperor with no clothing I called COVID, the little child that was pointing to conventional systems and saying, You guys aren't wearing any clothing over here. You're prancing around, like you know what you're doing. But this is a whole different world than we've all come through. I think it's been a truth teller. In a certain sense, and how you prove it is the proof is in the pudding. And our customers are finding that out for the benefit of consumers who are formerly marginalised or, or in in the olden days in the before times the way they were being assessed, versus how we can assess them, now they're getting their money, the deserving people are getting the loans that they deserve.

Brendan Le Grange 21:40

And now, if there is anyone more conservative than lenders, it's regulators. And when I was in the space myself and our analytics teams would start saying, 'well, we're going to try out some machine learning and we're going to build a new model, it's going to be more accurate', we would often get a pushback from the client saying, 'we can't lead with this, we need to wait until the regulator understands that because the regulators doesn't like blackbox models. And all AI and machine learning models are black boxes'. But that's not the case with you, you've got a big focus on explainability.

So what is your philosophy when it comes to these models and the ability to be explained and the ability to be incorporated easily into well, I guess, lenders' workstreams, as well as the reporting up to any regulator who might be interested?

Evan Chrapko 22:25

Yeah, you've taken me back to the farm and dad and advocating for knowing what you're ingesting and being able to certify that what you say is there is there or what you say isn't there isn't right in the certified organics mode. In this case, the regulator's main mandate is consumer protection, you actually have regulators being properly concerned about blackbox models and lots and lots of literature and sad stories of how AI improperly trained or unsupervised or with bad data in comes, you know, really bad bias and unfairness out.

Maybe by virtue of where I grew up and what I already told you about Edmonton, or by virtue of the mission of the company, that the consumer is our North Star trust science was one of the very first commercial entities on the face of the earth that really went deep into explainable AI. So while AI is 45 years, plus or minus, old as a science, explainable AI is much, much newer, much more recent technology. Knowing that I had to give my lender customers reason codes and knowing that in order to be FCRA compliant, I had to as a bonafide full fledged legal consumer reporting agency aka credit bureau, I had to be able to marshal adverse action for consumers that were aggrieved, you can't do that. If everybody's throwing up their hands and saying, 'well, we don't know why you got the score, you got the model said so'. That's not good enough.

XAI or Explainable AI gives us the ability to do that it's an order of magnitude more difficult, especially if you're productizing it like especially if you're building it into a system and infrastructure that has to do this many, many 1000s of times per second generating scores for banks and lenders who are asking for a score on a file, but it is non negotiable. I say that as a lawyer. I say that as a consumer. I say that as a technologist and we have a I'm proud of our now engagement with the regulators. We're happily submitting to their calls for input, writing the white papers, even conducting seminars and sessions teaching on x ai and the use of machine learning in this field or this arena, the regulator's they're to be commended for their efforts on the protection and the driving of consumer choice. You know, whether it's a allowing consumers to make the choice to submit their data or not whether it's allowing them to choose bureaus even even have a choice in the bureau that they asked their lender to use where that is possible, we want that more of the regulator's imperative is taken to heart by the industry because it gives my industry the credit bureau industry, the social licence to operate, you start opening yourself up to having big, huge data breaches, you start opening yourself up to not using the latest data on consumers.

That's not great for any of us because it just, it taints me by association. And so yeah, we will certainly do our part to the best of our ability.

Brendan Le Grange 25:48

Yeah. And it really is a space that is in waiting for that. So I'm glad to hear that there's such a focus on that regulator probably gets a bit of gets used a bit as an excuse in that as well, where they can be escaped code to say, well, we're not going to bother doing it because the regulator and the poor regulators and given a say in this, they just cast as the bad guy. Even trust science is based in North America, APAC has a truly global outlook. So adding patterns and countries so fast, we had to adjust this in the preparation. But in the traditional credit bureau model, a rollout to a new country is a very slow process.

I helped roll out a credit bureau, I think it was employee 13. So there are people had already got the ball rolling there. But essentially, the big five banks got together, they agree they wanted a bureau, we set up an office there, we got everyone around the table for several workshops, what data do you have? What fields are shared, watch the role look like? How are we going to agree on the timings and the penalties and others and then that then we set up the database, and then we have to get the data in, send it all back, tell them where to fix things, then get the data back again, load up two years of history, then set an analyst the job of building a credit score, then go to the market. And finally years down the line, you're ready to go. And you've invested a lot of money in a team on site for a few years getting yourself in place.

But clearly, with the speed that you're taking on the world, that's not your approach.

Evan Chrapko 27:12

Yeah, we're definitely harnessing or leveraging the technology that there's no way to scale. Without that the imperative for us is to right now be very laser focused. So we are international by virtue of being in more than one country already. And our efforts at protecting our intellectual property are also a tangible piece of evidence as to the growth forward the plans for the future, we are not enemies of outright enemies, or were pseudo competitors of the Bureau's that may already be established a more conventional means on that hub and spoke model. But we'll come in and look at alternative data that doesn't require a big huge convention or conference in standard setting and mainframes or other styles of computing getting established.

We do take the Metro 2s, we do take the performance files from our lender partners, because we are a learning system. And so that's just a non negotiable aspect of our business relationships, which is what all the bureaus do. But our ability to kick start or be value added from day one has to do with being able to harness data, public data or data that the consumer consents us to get.

And so the mobile phone and that vector, particularly in markets, that we're expanding into new or for the first time, it's like bringing a machine gun to a knife fight, that is a way to get digital information about someone that is actually about that person. And that you can get up to the second information, let alone that, you know, lag that you described with your gas bill of many months or quarters, up to the second information that has proven to be very telling about the wisdom of whether or not you should lend money on a consumer by consumer basis. So our global ambitions are material, they're not trivial. And we will do them in order in a strategic way, starting where we have now in both Canada and the US, Mexico and the UK are next. And then it goes from there. We have patents from China and Japan to Israel and other countries that are not part of the first four that I just mentioned,

Brendan Le Grange 29:28

Staying on the idea of global ambitions. You're a Canadian and have lived and worked in America, which is not too surprising, but I saw an old speech of yours were a little unknown or interesting fact about yourself was that you'd spent a year living in Sumatra, Indonesia as well.

So before we get back to more serious matters, what was the story there about your own jungle adventure?

Evan Chrapko 29:51

Thank you for that. That's a trip down memory lane.

I took a year off of university and I lived in a jungle village on the island of Sumatra and I came to learn and have my eyes really, you know, widely snapped open that people are people everywhere, even if at first you can't speak their language. I mean, we got a lot of that on the farm as as mentioned, we're reliant on your neighbours and be a good citizen. And you know what the motivations of people toward one another, when there's a spirit of goodwill, and you're being helpful, a contributing member of the village or of the community that you're in, it was so life affirming and gratifying.

For me, given where I had grown up exposure, immersion, not just exposure, immersion in a whole different religion that I hadn't known anything about prior to that it was a very, very valuable hiatus from school from a professional education in a in a business sense or business degree, I would highly recommend it to anyone from there, it came back to finish and, you know, rapidly even more rapidly because now I had a mission or a purpose, the CPA that I ended up getting and the law degree after that. And you know, the rest of the story.

Brendan Le Grange 31:09

I guess there is a sort of a language that farmers understand. And I guess it's a connection to the land, but I think there's suddenly I can see in each other, so I'm sure there was a little bit there that helped bridge that's sort of one of the biggest global gaps they can be from distance culture, weather conditions. That's yeah, about as far as you could go.

If we bring it back to Trust Science and to Credit Bureau +. In 2018, I saw you on a stage in money 2020. And there you are talking about launching your new six cores, which was enhanced by this customer voluntarily added data. So I'm not going to name names. But you know, some of the very biggest names in credit bureaus are now bringing products to the market where you can add your volunteer data on top of their credit scores a full four years later.

Now catching up, as you said, you'd come from a long tradition of this from the business itself being so far ahead of the market to your dad and his farming the head of the market. Clearly, that's a position you're very comfortable being in if we talk about what's next, obviously not giving away anything to secret, but why should we be looking at for

Evan Chrapko 32:12

Sure, thanks. I smile at the analogy or the story there because the it's the scouts that take all the arrows in the chest, but being fearless and persistent about that lets you also enjoy the biggest vista first, or stake the most lucrative claims first as well.

And I think we're where we have positioned ourselves in this science of trustworthiness and having a proper enterprise class highly secure and scalable infrastructure. That's a learning system.

So as I did mention, I'm not a one trick pony on credit scoring - my infrastructure my machinery can answer any question for which the data harbours the answer. I think what you'll see from us next, it'll have as much as anything to do with expanding up the credit quality ladder, we are not incapable we are highly capable of assessing prime and super prime as we are subprime. We've chosen subprime because of our mission is really well served by helping the underbanked or people who are marginalised or otherwise considered no hits and thin files. And so we are naturally in that realm of finding the hidden primes. But I am just as easily going to end already him being taken into by some of our customers prime and super prime arena.

We are also the adjacent vertical to underwriting credit. Obviously, as underwriting insurance. There are different kinds of arenas in which trustworthiness and being able to assess it matters a lot. And then it gets really out there. From the standpoint of this conversation. There are assessments to be made in hiring or in law enforcement, Homeland Security and the war on human trafficking or things having to do with online e commerce or the gig economy and two people coming together to get something done.

Whether that's, you know, Craigslist in the US or Kijiji in Canada and your imagination is your only limitation on where you can apply a platform that has this ability. But to keep it you know, really back to your focus, I think that the next high value use of what we're doing is going to be in what's called subprime. I say wrongly, many people are wrongly scored as subprime, or they are seen as subprime, if you you know, hew to FICO, or Vantage, or Beacon.

So we want to solve that issue in many more countries.

Brendan Le Grange 34:39

And as you say, you talk about the models not being able to use the last few years to predict the next few years. It's the same with our own sort of experience. We've had a long period of basically free lending for big asset purchases as consumers with good credit scores. It's not going to be sustainable and I think it is going to be a space where lenders are going to say okay, really who's super prime here, because my capital is getting expensive!

Evan Chrapko 35:01

You already see people slipping. Now what had been considered a consumer who had been safely or happily would have been scored as prime slipping, you know, they're now near prime or just below 700, or just below 680, as the stresses and the underpinnings of their financial life to this point are changing, and as their groceries are getting dramatically more expensive and their fuel costs and even you know, we're specialised now in an automobile, both direct and indirect auto lending as well as instalment loans, so unsecured consumer loans or lines of credit, you're watching the fact that you better be taking into account the asset or the purpose of the loan, what's the purpose of the model you there is no God's eye, there is no such thing as a one size fits all credit score, purpose matters, collateral matters, what are the loan terms?

What is the deal matters, and if your models are not capable of taking those things in you as a lender and real trouble, real trouble,

Brendan Le Grange 36:04

I think also to the models that can react to their lifestyle change as we move more to work from home or to Geek work more to maybe people doing two or three little side hustles and still being able to be called Super prime, because, you know, the world's no longer the corporate only approach.

But yeah, I think it's a place where a lot of consumers are going to see the impacts going forward. And it's great to hear that there's people sort of on their side pulling models with them in mind.

Evan Chrapko 36:30

I wish I could afford the same Superbowl ads that happened for you know, the so called way to bring data in from the consumer and asking them to do so or download an app on their mobile phone to do so my Superbowl ad would say, hey, consumer, make sure when you're talking to your bank, that they're using an actual system that can look at your data that that legitimately incorporates that data that you're submitting, or that can look at what happened with you and the promotion you got or the big contract you landed yesterday, I would say to the consumers, maybe too much like and this is not a great analogy, but like the pharmaceutical companies advertising to the consumer saying go to your doctor and tell them about us.

My Superbowl ad would say go to your lender and tell them about Trust Science!

Brendan Le Grange 37:17

Yeah, I love that. It's always swimming. Every time I visit America, one of the take homes, you turn on the TV in the hotel room, and then you see that they're there to tell your doctor to try these. But no, I love it. I love that idea that as a consumer driven thing. Like there's benefits to us, can you please start taking these in? If consumers if people listening want to learn more about trust science, they want to learn what it is they should be telling their banks to be doing? Where can they go to learn more about what trust science is doing what credit bureau plus means, and potentially contact you to learn how they can bring in your models,

Evan Chrapko 37:51

www.trustscience.com

Everything is there. We're there for right now the consumers in North America. But as Canadian as that having that influence we sit between Europe and North America. So we've got a really neat vantage point and what's happening with the laws and what's permitted in the two somewhat different jurisdictions Europe versus the US, but it is a .com address www.trustscience.com

And we're looking forward to some pretty big announcements coming up with a combination of what Gartner is saying about us, as well as some patents that looks like they're nearly being issued that will tip our hand as to some of the things they were doing in the distributed ledger world and harnessing that aspect of the global technology infrastructure to really give consumers agency and also empower lenders to know who they're dealing with in real time.

We're very, very excited about what the future holds and our position in it and taking our Isay rightful place as a full fledge credit bureau on the face of the earth.

Brendan Le Grange 38:58

Yeah, I'm excited to and I encourage everyone to go and have a look at trust science.com There's some great blog articles. But there's videos there, you've got snippets of speeches, you've got some newspaper articles. And so more than just the normal contact details on that website, lots to keep anybody interested.

Evan Chrapko 39:14

I always say make sure they don't miss the Globe and Mail article, the Globe is Canada's equivalent of the Wall Street Journal nd they did an investigative report deep dive piece really well written really, really well written then it started as something that I think was going to be a critique or criticism of the big bureaus and if they were interviewing me as as part of being, you know, the bureau community, but it turned into a compare and contrast of trust science and credit bureau plus, quote unquote, versus the old way of doing things so that that one is a feather in our cap as a piece of high quality journalism.

We have the other really something that I'm proud of my whole team for on the Real Leaders nod as one of the 200 top companies on earth that have succeeded at being socially valuable and forward on the whole issue of our social contract to be leaving the world a better place than we found it. And that's that is straight from growing up on the farm leave this for the next generation and of way that's much better than you than you inherited it.

Brendan Le Grange 40:23

Yeah, I think this is a really great feedback to getting and yet again, thank you so much for time. It's been fantastic catching up. I think it was the article I was thinking about. Yeah, I will put that on because I read it. I was really impressed both either writing and even just the whole combination of the piece, which is rare to see in there in the space to to get a really enjoyable article as lots of material there. Obviously, you're doing some really exciting work at the moment.

Evan Chrapko 40:48

Thanks, Brendan. Appreciate your interest. And you did a lot of great homework. I'm very impressed. Bravo.

Brendan Le Grange 40:55

And thank you all for listening. If you enjoyed that, please do rate and review on your preferred podcast platform and share widely including on LinkedIn. And while you're there, send me a connection request. The show is written and recorded by myself Brendan le Grange in Brighton, England and edited with assistance by Kane Hunter. Show music is by Iam_Wake and you can find full written transcripts, show notes and more content at www.HowtoLendMoneytoStrangers.show

And I'll see you again next Thurs