Podcast 150: Frederic Nze of Oakam

What do you do when most of your potential customers base has no financial data whatsoever? When it comes to expanding access to credit for low income earners, who are often unbanked, new approaches are needed. New groundbreaking work is being done by a few enterprising entrepreneurs who are using psychometric testing as a way to underwrite risk.

Our guest on episode 150 of the Lend Academy Podcast is Frederic Nze, the CEO and founder or Oakam. Based in the UK, but originally from Central Africa, he saw how difficult it was for new immigrants to get access to credit. So, he created Oakam, a UK micro-lender, to serve the credit needs of these immigrant communities. But in doing so he found there was demand across the UK in many demographics.

In this podcast you will learn:

  • What was the catalyst that led to the founding of Oakam.
  • The loan products that Oakam offers.
  • What a doorstep lender is and how micro-lending operates in the UK today.
  • A profile of the typical Oakam customer.
  • The difference between small dollar loans in the US and the UK.
  • How Oakam is able to find their customers.
  • What percentage of their customers apply for a loan on a smartphone.
  • Why they still have physical stores where people can apply for loans in person.
  • How they have refined their underwriting models over the years.
  • How their psychometric testing works.
  • What their relationship is like with the FCA, the primary regulator in the UK.
  • How they are dealing with increased fraud via their online channels.
  • The interest rates they charge.
  • How their customers are able to reduce these rates for future loans.
  • Who is providing the capital for these loans.
  • What the future holds for Oakam.

This episode of the Lend Academy Podcast is sponsored by Wunder Capital: where impact investing meets capitalism.

Download a PDF of the transcription of Podcast 150 – Frederic Nze.

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PODCAST TRANSCRIPTION SESSION NO. 150/FREDERIC NZE

Welcome to the sesquicentenary episode of the Lend Academy podcast. Yes, we are at episode 150. This is your host, Peter Renton, Founder of Lend Academy and Co-Founder of LendIt Fintech.

(music)

Support for Lend Academy comes from Wunder Capital, the easiest way to invest in large scale solar energy projects across the US. With Wunder, you can earn up to 7.5% annually while helping to finance renewable energy projects. Wunder Capital, where impact investing meets capitalism.

Peter Renton: Today on the show, I am delighted to welcome Frederic Nze. He is the CEO and Founder of Oakam. Oakam is an interesting company, they are based in the UK, they are a microlender, but they’re doing it all pretty much on the mobile phone these days. So we talk about how they got started, they have been around for over a decade, we talk about how they are underwriting these customers that are applying for loans, these reasonably high risk customers that are applying for loans on mobile phones and we talk about their unique psychometric testing that they use to help them underwrite. That’s really a fascinating piece that we delve into in some detail. We talk about loan performance, we talk about who is funding the loans and much more. I hope you enjoy the show!

Welcome to the podcast. Frederic!

Frederic Nze: Thank you, Peter, happy to be on the show with you today.

Peter: Great, happy to have you. I like to start these things off by giving the listeners a little bit of background about yourself, what you’ve done in your career to date.

Frederic: Sure, so if I go back all the way back to the beginning.  My background has been in the decision science, not in the financial services sector so I started in the defense sector in France working for a company called (inaudible). It was the second wave of AI development across the industry so before the last year I worked there, I was involved in developing an electronic co-pilot for a fighter jet and so that was the early days of looking at how data can be used to replace human decisioning.

On the back of that, I joined American Express first in France and then in the UK, and after that in the US where I was working on fraud prevention software, everything to do with lending, with consumers and SMEs. Finally, the last part to move away from decision science into how…joined General Electric, GE Capital, where I worked on the development of their lending business outside of the US so in emerging markets mostly and the integration of businesses we were acquiring into the GE Capital stable.

That was the (inaudible) part of my career, I came back to the UK and did a last stance with Barclays where I was heading the consumer lending business here and this is where probably the final encounter that has become Oakam started and I decided to jump from large corporates to go for a startup so I created the first startup Euristix which was a big data consultancy and on the back of it I created Oakam so this is my second startup.

Peter: Okay, so what led then to the creation of Oakam? What was the thing that you saw that really was a catalyst for starting the company?

Frederic: The main one was probably…I had worked in my career on how to improve, you know, decisioning in risk so credit decisions. When I worked at GE outside mature markets in places like Brazil and other places, I encountered consumers that were completely off the grid so I encountered consumers that were difficult to KYC to prove their identity or their address, who most of the time didn’t have payslips and all the time didn’t have a credit history.

So it was an interesting thing coming from a model which was building data by FICO score and Experian credit bureaus, a little bit of a circle model where you have to be in the model to able to progress in the model, to go to a place where you didn’t have a starting point. If you have no credit history, how can you have a score and vice versa.

So I returned to the UK and realized the way (inaudible) was coming and the issue this created for the banks, I thought it was an interesting opportunity to attack…that gave me also the link back to home. I’m originally from Central Africa, in Congo, and I always wanted to find some part of the financial services where I could find a link back to home so being able to open a business in Sub-Saharan Africa and being able to have an impact in areas where the percentage of unbanked and underbanked was significant.

Peter: Okay, so then maybe you could step back and tell us about Oakam. What exactly you offer, what are the products you offer and where you operate?

Frederic: Sure, for most people listening to the show we are from a product perspective, a very typical microlender so we lend to the bottom of the pyramid. So in the UK, we lend to mostly the bottom two deciles of income, but we go as far as the fourth decile and to migrants so people who haven’t got any credit history. Most customers want small loans, that’s the definition of the microlending spectrum, so we generally offer loans that will start at $150 and we can go all the way to $7,500 for terms that will be mostly six months going all the way to three years.

So if you look at that as a percentage, the loan as a percentage of income, it is very similar to what you would see in Mexico, in Uganda or India so we are a very typical microlender when it comes to the amount and terms, we’re very atypical in the way we approach it.

Microlending has been a dominant model across the world, you know, in the UK it is a doorstep lending model where you have someone with cash knocking on peoples’ doors, making a face-to-face human underwriting decision and collecting as well as disbursing the cash. What we’ve done is we gradually looked at how we could challenge every piece of that model and move it to a smartphone. So we are a smartphone based digital microlender.

Peter: I just want to step back. Do people still today knock on peoples’ doors and offer them microloans, is that still happening in 2018?

Frederic: Actually, there’s a lot…it was a surprise when I was thinking of setting up Oakam, Originally, I thought we will deal mostly with migrants coming to the UK, helping them build credit history and move to the mainstream. As we were doing that we had consumers knocking on our doors saying why are you helping Polish or Nigerian customers and you’re not helping the UK born. I assumed that they had everything they needed with credit and they have very little choice.

The market here, the doorstep market, is quite a significant market, very concentrated, the leader in the market has close to 80% market share, the second one is around 10% of the market share and we’re just behind that. So you know, in about a year’s time we should be number two in this market. Number one and number two have been operating for more than a 130 years with a business model that hasn’t changed much.

What is interesting is that sector is a little bit of what you had at the end of the Industrial Revolution where people were paid weekly as banks were not offering consumer lending and what you have is you have people coming to your home and selling you anything on the knock. So you had traders who would come to peoples’ homes and sell garments on a weekly collect. Some of that has led to the creation of the catalogue business in the UK, very successful, and they have translated to digital businesses so most of the old catalogue businesses in the UK are not completely digital, but the doorstep lender, two years ago, still had something close to 13,000 agents in the UK.

Peter: Wow, that’s quite amazing.

Frederic: Yes, so it is a big surprise, yes and super high maintenance.

Peter: (laughs) Yes, that’s a costly way to go and find customers, particularly when it comes to small dollar loans. So maybe…I mean you talked about migrants, you talked about people in the UK, can you maybe give us some perspective on the typical Oakam customer, tell us a little bit about them.

Frederic: So now it’s probably around two thirds/one third, so one third of our customers will be thin file so what I mean by that is people who have been in the United Kingdom for anywhere from less than a year to six months plus, to three years. It’s very difficult to apply for most financial products, even having a contract, a mobile phone contract, pay as you go, if you have less than three years of proven addresses here. The reality is that a lot of the migrants when they come here the first year because they’re not too sure how successful their migration process would be, they are sharing a flat with five others and they have no utility bill, no way to prove their address so that extends a little bit further down.

So we have people who have been in the UK five years, they still have a very thin file. For them everything is about creating a credit footprint and being able…when they feel comfortable that this has become their home, that they can move their family and access a loan for the deposit on the house, then a car loan, then for some a SME loan, then mortgages so they want to get on the bureau and they use Oakam to get access to the mainstream.

The other part is the low income UK consumer who are excluded, not because they haven’t got a bank account, but because they are permanently low income so they don’t have enough work trajectory and what they need is the flexibility of being able to borrow frequently small amounts.

So this is your typical small dollar loan that you see in the US with a big difference between a small dollar loan in the US is very oriented to single payment like payday loans where here we have a thriving microlending sector going from the not for profit all the way to the doorstep that’s listed on the stock market and very profitable.

Peter: So then these people are sort of…I think the biggest challenge you have and obviously you’ve cracked it to some extent, at least, is finding these customers because you can’t just get a file from the credit bureaus and go market to these people, so how do you find these customers?

Frederic: So we started the business by doing a lot of community marketing so if you think of our business because we are attacking a dominant player that has very long term relationships with customers, we started by territory. We looked at people that were underserved so if you take anybody who’s from a community where credit is not widely distributed, but also not widely marketed, it is actually very cheap to go on TV programs watched by a certain community.

So you have a lot of communities in the US, you have a Korean community, they have their own TV channel and they’re not getting as targeted as the mainstream so you end up advertising at a fraction of the cost that you would have to get the same eyeballs if you were doing it on a mainstream channel. So what we found is community marketing has been number one for us.

Then because distribution is very tightly linked, referrals has been a very good source of customers and also a source of good customers so referrals that has helped us in risk underwriting. So you know that it’s very difficult for you, as a Congan for example, to apply for a loan, you get a good service with Oakam so next time somebody in your family wants to apply, you recommend Oakam. So we found this is our second source.

Now for the fourth, what our competitors are doing, we’re doing mainstream TV, but we’re going very far with it. We say, if you’re dealing with Company ABC, you can save money by moving to us so we are now frontally attacking the doorstep lenders.

Peter: Got it, okay.

Frederic: So majority of our advertising is TV, community channels and mainstream channels, a little bit of social media and then historically, we have guerilla marketing, you know, sort of go back five years ago, we were advertising in community centers, we were doing training for people from certain communities, once they apply for a job, helping them to do a CV, we were very into churches, we were doing like, you know, field marketing. We stopped doing that. We have created a brand and a reputation and we don’t need to do that as much.

Peter: Right, got it. Okay, so then when these customers are actually applying for a loan is this….you mentioned smartphones, I mean, like what percentage of the customers are coming in and applying for the loan on their phone?

Frederic: This is the biggest shift we’ve seen over the last five years. Even four years ago, we had something like 40% of our applications were coming from people walking into a store on the back of a TV ad or something. Then we have something like the other 60 were coming on the web or either calling us, but it was coming from the web using a combination of desktop from an internet cafe, for example, tablets or phones. This year we have 95% of the customers are coming from mobile phones, 92% and then the rest is like mostly tablets and 4% only are walking into a store.

Peter: So how do they walk into a store, do you have physical locations around the UK?

Frederic: Yeah, we have physical locations, but we have scaled much more aggressively on the smartphone and mobile apps than we have on retail. We have used retail to gain the knowledge about underwriting and to develop our psychometric underwriting and now that we have the data on how to do that, we’re now doing everything automatically through the smartphone.

Peter: Right, right. Okay, so let’s talk about that, how you are underwriting these loans. As you’ve said yourself, there’s not a whole lot of data available on a lot of these people. What are some of the tools you’re using to kind of predict risk when you don’t have the data you want?

Frederic: If you think the traditional the credit model was…you look at somebody with collateral capital, credit capacity and character and in our situation customers don’t have collateral, they don’t have collateral capital and they don’t have credit history so we’re left with character and capacity.

So when we started it was very much about first, I’m going to establish your ability to repay so if you want our version one of Oakam which was very much time-intensive, you know, interview to understand your existing budget because people have uncertain incomes. For instance, they are an Uber driver and they don’t know how much they earn in two weeks so we try to set their capacity to service the loan and the second piece was, as I said, the character.

It was very interesting when we…we were doing mostly data analysis about our underwriters. In our first model…we thought you know what, I already know how Peter is deciding that Courtney is a good risk, but what I want to do is how do I find more Peters so we were looking at all our underwriters and we were classifying them with how well the customers they were recruiting would pay. So our first level of underwriting was how do I select people who are very good decision makers when they’re in their community, you know, facing people.

Then we started to interview the best underwriters, we said okay, you’re the experts. It’s a bit like you’re a pilot, I’m going to look at how you react in different situations so I can program the simulator. So we went to all the Peters who had very low loss rates and said, what do you do when you’re in front of a consumer and they told us they have their own heuristics.

They were saying, you know, if I have an appointment at 10:00, that says they rise early, that’s a good point, I see what brands they have and where they do their shopping, if they go to like super discount grocery stores that’s positive so they were looking at signs of being thrifty, signs of being organized, if they were coming in and had a very clear view of their budget. So in their heads they start to pick the characteristics that were very positive and so we asked them to capture this in a little text at the end of each decision.

The second approach, so Oakam version 2 is we start to do some text mining so we said, okay, we have a lot of instruction data and we’ve got to try to find what are the answers that consumers are needing to certain questions and can we put these questions online and see if we get the same final answers, then we can automate it. That was tricky because, as I mentioned earlier, we’re dealing with migrants, you also have the element of language. So we tried that and we stumbled across an approach that we’re using psychometrics through pictures.

So we approached 50 universities and we asked them to sign up with us, a three-year contract, where we do some R&D together, we’re supporting PHD students and we went about saying, these are the characteristics that we’re looking at, is there another way to find them by asking customers to play a game or to pick choices. So we put four pictures in front of people and say, when you’re stressed, what do you do, and we give a choice of like going outdoors and doing some exercise, going home and spending time with the family, going to the pub or the bar and drink and people have a short time to respond. What we found was that there was a very, very strong correlation to the choices they were making and certain characters that were linked to fraud and good payment behavior. So that’s version three of Oakam.

So we moved from getting experts to make decisions and experimenting so we were happy to take losses on people. It was very much, you’re the underwriter, you make the decision, we’re going to figure out how you pick it and see if we can automate it so we’re trying to train the machine, observing experts. Second, we use text mining and third, which is what we are at now, based on pictures, completely automated.

Peter: Right, so they’re doing this on the phone. You’ve got like a psychometric test that…

Frederic: Not on the phone, completely automated.

Peter: Right.

Frederic: The customer does it on the app or on the web.

Peter: How long does it take?

Frederic: Not very long, you know, when we started we needed 25 questions, to get a profile and now around 13/16 questions we can get a very excellent profile for what we’re looking for and because the questions are picture based the idea is the user experience is quite quick. We don’t want them to think too much that they’re trying to reverse engineer so it is very much what would you do and you have to click.

Then you get the next one and based on the first question you have, the second question might be slightly different so we have been able…but it’s like, you know, hundreds and hundreds of thousands of data points to see…first experiment how well they are paying, how much were we getting then we have a second experiment. We’ve been doing a lot of A/B tests for the last years to get where we are now and investing some R&D with academia.

Peter: Right, that’s really interesting. Are you using this really just to make a binary choice around fraud or are you using this also to put them on a risk spectrum? How are you using the results of this psychometric testing?

Frederic: We put them into a risk spectrum and it is a spectrum with two dimensions so I’ll give you a simple example. Let’s say that at the end of the questionnaire we’re comfortable with your risk profile in terms of you’re not a fraudster. Now we need to decide if we’re lending to you 10% of your monthly income or 15% of your monthly income, how much can you service. To do this, we’re looking at self-declared information. You’re saying, I think I can service the debt of $50 a month, but if we know that you have a tendency to be over enthusiastic and if you’re a risk taker, we know that you have higher chances to also do some occasional gambling.

So if you say you can service $50, we’re going to say actually to be safe $30. So we’re using psychometrics to rank people on the element of affordability, the element of what risk they will be exposed to in the future. If you have three kids, you’re a single man there will be more unexpected expenses and how you would react to this. For example, some customers we see are very resilient and they’re happy to shrink their budget when they have unexpected expenses; others will want to continue to keep up with the Joneses when there is a problem and end up in arrears. So that’s the types of things we pick up.

Peter: Right, so I’ve got to ask you about regulation now. I mean, I presume you’re regulated by the FCA, but some of the things that you’re talking about there, I’m just wondering how comfortable the regulators are around psychometric testing. So maybe you can just talk a little bit about how you’re regulated and how your relationship with the regulators is.

Frederic: So we are fully regulated by the FCA. The microlending sector is actually one that has more rules and regulations than the mainstream consumer lending on treating customers fairly so there’s some extra rules to make sure that customers are protected. So in a way that makes operating in microlending in the UK harder than operating microlending let’s say in Mexico or in India, but the favorable part of that is raising the barriers to entry in a sense.
Our experience of the regulators, I think the FCA is one of the most commercial regulators I came across in the different jurisdictions I’ve worked with and they are extremely data oriented so what we find is that they are happy to listen to an argument as long as you are fact based. So if you say that you can underwrite and have a little less fraud [inaudible] by not doing sort of an element of a fraud check but by using another method and alternative data. As long as it is evidenced by the outcome from the consumer, they are happy with it. So you have to engage and you have to demonstrate.

Occasionally, we have long letters and presentations, and things like that, but they’ve always been open to listen and very supportive. Also, they know that we are providing competition in a sector that they haven’t seen much for many, many years. The players, there hardly has been any new entrants. There are a few new entrants on the charity side, but for profits, we are the only new entrant you will find in the last 20 years.

Peter: Interesting.

Frederic: So they know that when you have one market, one of the elements is to protect the consumer, the other one is to encourage competition to make sure that the consumers are getting choices. Our main competitor has 80% share of the market and has been successful, they’re also supporting one of the agenda items of the FCA.

Peter: Right, got it. Okay, so I want to talk a little bit about loan performance. You’ve been around for more than a decade, I believe, so you’ve gone through many, many cycles of your loan book, so tell us something about loan performance and how this sort of new approach compares to what you were doing five years ago.

Frederic: Two dimensions on loan performance; one is how it has rolled over time and the relationships with the customer and the other dimension is how it varies across communities and channels. So what we have is…when we see a customer face-to-face versus when you see people online, face-to face provides a natural protection on fraud since fraudsters aren’t as comfortable sitting down and having a 20 minute conversation.

So we see that fraud attempts are significantly higher online so we needed to be more sophisticated at the start. When we moved the majority online, the fraud level went up. The first thing is there is the reason why this market hasn’t been cracked before through normal credit scores, online you have more fraud. So that’s the reason why we have to use alternative data. We’re now at a point where…if you think of ranking all our underwriters, psychometric methods is beating 80% of the underwriters we have, remember we still have 20% of human who can make better decisions than any scorecard or any psychometric we’ve been able to develop.

Peter: Interesting.

Frederic: What we found is…it’s sort of AI versus chess, or AI playing Go. There was a point when this was cracked and even the world champion and grandmaster were beaten, but today we’re still in the space where something that some of our underwriters, 20% of them, can do that enables them to grant a loan to more people than when we do it automatically online and still have a lower level of default than the machine generates.

So some of it is just the relationships they’ve built, if I meet you face-to-face and you think I was very friendly, it is no longer an impersonal loan. You feel like you borrowed money from Frederic and Frederic was there to help you at the time of distress which is very different from going online, there was nobody you talked to and you go through the process and the money went into your account. So there’s a psychological element that is very difficult to replicate online.

So we’re still trying different methods to see how we can pick up the last 20%, but the calculation of course was very simple. It’s very difficult to replicate 20% super performers so when you have to scale…last year our application volume went up by 300% we could not recruit fast enough people with that level of quality and expertise to follow the size of the business.

Peter: Right,

Frederic: So it was not a choice, we had to go more online and accept that we will face more fraud and we will not be able to beat the best performers so that’s the first thing. Online versus face-to-face. The other one is doing this model….you know, if you think of the fact that for our customers there’s a big transaction cost of actually applying. Most of our customers will tell you that the first thing they had was their fear of applying because most of the time they are declined. Once they’re declined, it makes the chance of being approved the next time even worse because you have an extra search on the bureau.

Every time you try and get declined, the next one you apply for you have a less chance to be approved. So our customers don’t want to apply to too many places at once. Having a reputation for being a business that is more flexible because we have a scorecard that takes into account other dimensions, not just what is on the credit bureau, has a benefit that people will try with you first because they have a high chance of getting through the door.

When they move with you, if they’re happy with the service there’s a high level of repeat so customers three months/six months later say, oh, I need another loan or I have another emergency and they will come. They know that now they have been good payers with us, not only the chance of being approved is significantly higher. You’re now in the 90% of being approved and also you can borrow at a cheaper rate and a higher amount. So that’s the main difference behind our business model.

We’ve created what we call the Oakam Ladder that gives you progressive pricing. Every single one of our customers start at a rate that is very similar to the competition, but after 12 months, the best payers can go as low as 75% cheaper than what the competition offers.

Peter: Interesting. Can we just touch on the rates for a second. I know that you are not competing obviously with the Zopas’ and RateSetters’ of the world, what are the rates that you charge typically?

Frederic: In the UK, we have what we call rate caps which is the maximum you can charge for a microloan is 0.8 per day and for a new customer that is a higher risk, our highest rate is 0.76 per day. After a year, the range in terms of annual rate will be between 36% to, from memory, 288% but the range is annual rates. Again, when you think of it customers in the mainstream lending would say 100%, that’s very high.

The reality for our customers is that it is a small loan, if you’re borrowing 200 pounds, the choice you have sometimes is borrowing or being faced with an insufficient charge on your bank or being disconnected from service on some other services like utilities so a lot of our customers actually make up the difference in cost, If I don’t pay my utility bill, they will charge me 30 pounds extra and if I borrow from you, you’re going to charge me 12 to 20 pounds so I’m actually saving money. So the way we look at it is the rate is in comparison with the charges they would have been charged otherwise.

Peter: Right, right. We’re almost out of time, but I’ve got a couple more questions I want to get in here. I want to talk about the funding side of your business. Obviously, you are providing capital to these customers, where are you getting your capital from? Who are the providers for you?

Frederic: We have an equity provider, Cabot Square Capital, a private equity based here in the UK, but they’re backed by (inaudible) LP, a US company so university endowments, etc. On the debt side, we are partners with Victory Park Capital, a Chicago based debt fund operating in the US together with a lot of tech lenders, fintech players, but also now more and more in Europe and emerging markets so these are the two capital providers we have.

Peter: Right, okay and so are you reporting the payments on your loans to the credit bureaus in the UK?

Frederic: Yeah, this is the most important thing we’ve done is from day one when we opened our business, we reported. Actually, we lobbied to make sure all the doorstep lenders would also report, because some of the large ones were not reporting which created a bit of a captive market. They were the only ones who knew how well people were paying and they were not sharing that information so we fully report and we use multiple bureaus.

Peter: Okay, so where are you taking this, what’s your goal here? It sounds like you’ve still got a lot runway ahead in this space, but are you looking internationally, are you looking at just focusing on the UK getting more market share there or adding new products? What does the future hold?

Frederic:  In the UK, there is a very exciting runway ahead of us because we…as I mentioned earlier, this market hasn’t seen many entrants and we’ve moved from attacking the territory. So if you think of it, when we started the business we only stayed with consumers that didn’t have another choice and over the last two years, we’ve been attacking the core of it so people who are actually borrowing from the doorstep established businesses and taking market from them, thanks to the pricing, the app, etc.

We are not a startup, but we are high growth and we do share for example, the last few months we’ve been growing month on month more than double digits. In February and March, we grew by 70% in one month. We’re really gathering a lot of market share, it’s working strongly. We have a few product expansions we are planning to launch in the UK, but a lot of our effort is to make the app go deeper in terms of what customers can do with mobile. So we’re not just about lending, we’re about nudging you know, the right financial behavior, distributing nudges of financial education, trying to get people to budget better.

So we’re trying to turn our mobile app into a platform where customers can access cheaper products, non-lending products. So we are looking at opening APIs to others, remittances, etc. to come and collaborate with us. That’s the plan for the UK and….because what we’ve been able to do is to look at how we underwrite in places where people have no footprints, we believe that the most exciting growth is in the area where credit bureaus have not established the dominant model, the FICO and Experian.

There’s a lot of places today in the world where you have large demographics, mostly underbanked with very strong smartphone penetration, where we can do what we’ve done in the UK and that’s what we are looking at now with our capital providers to see how we can replicate what we’ve done here in these other markets.

Peter: Okay, well, it’s fascinating. I wish you all the best, Frederic, and thanks for coming on the show today.

Frederic: Thank you very much for having me.

Peter: Okay, see you.

This whole idea of psychometric testing is really interesting to me, particularly when it is applied to underwriting. It means you can go into populations, you can go into markets where there is no financial data or even no data of any kind and you can still do fraud detection, you can do risk analysis, based on a simple test on a smartphone.

This will allow, I think, more and more people, the billions of people who are still underserved today…over the next ten years, I can see us bringing pretty much all of those people into sort of the mainstream financial system and that is going to be a huge opportunity. Companies like Oakam are at the cutting edge of that and there are obviously others around the world that are also working on this, but I find it personally fascinating that we can make these predictive decisions based on models built with this psychometric testing.

Anyway on that note, I will sign off. I very much appreciate you listening and I’ll catch you next time. Bye.

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