The large credit bureaus do an excellent job of aggregating data and providing credit scores for the majority of the population. But there are still tens of millions of people who remain unscored and one credit bureau is doing their best to try to change that.
My next guest on the Lend Academy Podcast is Alex Lintner, Group President of Experian. Alex runs the US consumer credit bureau for Experian and, as you will hear in this conversation, he is passionate about expanding the universe of people who have credit scores.
In this podcast you will learn:
Why Alex decided to join Experian.
Alex’s responsibilities at Experian.
What Experian gained in their acquisition of Clarity Services.
Why they are passionate about alternative data at Experian.
How Experian Boost works to incorporate consumer monthly bill payments.
The typical change in credit score that comes from Experian Boost.
An explanation of the new UltraFICO score.
How their Clear Early Risk Score works and how it predicts first payment default.
How they work with rental payments in building a credit score.
How Experian thinks about privacy today and keeping data safe.
How they are using machine learning in the development of credit scores.
PODCAST TRANSCRIPTION SESSION NO. 205 – ALEX LINTNER
Welcome to the Lend Academy Podcast, Episode No. 205. This is your host, Peter Renton, Founder of Lend Academy and Co-Founder of the LendIt Fintech Conference.
Today’s show is sponsored by LendIt Fintech Europe 2019, Europe’s leading event for innovation and financial services. It’s coming up on the 26th and 27th of September in London at the Business Design Centre. We’ve recently opened registration as well as speaker applications. You can find out more by going to lendit.com/europe.
Peter Renton: Today on the show, I am delighted to welcome Alex Lintner, he is the Group President at Experian. Now I wanted to get Alex on the show because Experian has released a number of new products in recent times and I wanted to sort of delve into some depth into these.
We talk about the new Experian Boost product, we talked about their acquisition of Clarity Services, we talk about UltraFICO and how Experian is working to really include more and more people in the credit system and their approach to alternative data and machine learning and how they’re really leveraging everything they can to bring more consumers so you can accurately score as many people in this country as possible and globally, for that matter. It was a fascinating interview, I hope you enjoy the show.
Welcome to the podcast, Alex!
Alex Lintner: It’s my pleasure, Peter, thank you for having me.
Peter: Okay, so I like to get these things started by giving the listeners a little bit of background…I know you’re not from this country, same as me, so maybe you can tell us a little bit about how to came to this country and what you’ve done in your career before you got to Experian.
Alex: Yeah, I’m German born and raised and I got to this country by having the privilege of doing my undergrad and graduate schooling here. After that, I started…I did go back to Germany, but started working for US corporations. I worked for a consultancy that was then purchased by Ernst & Young and then migrated eventually to BCG, the Boston Consulting Group who transferred me to their San Francisco office and as is not untypical of consultants, one of my clients eventually decided to hire me and that was a company called Intuit in Silicon Valley.
After my career as a consultant, I had nine very fruitful years working with Intuit and being part of a journey that I still consider one of the most exciting ones that I’ve had in my professional career.
Peter: Okay, okay. So then what was it that led you to Experian, what was sort of the opportunity, and why did you join the company?
Alex: Well if you think about what Intuit does, they are the makers of QuickBooks, which is a small business accounting software suite and the makers of TurboTax which helps individuals file their income taxes. So I really was in a fintech company, one of the largest, you know, if you will, and Experian called me proactively and really said, well, we’re looking for somebody who sort of understands the sector and has a strong background in technology.
I benefited sort of from the halo effect that during the time while I was at Intuit, we went through a technology transition that I played an active role in and that experience was attractive to Experian who was seeking to themselves evolve and become a leader in the space of delivering data with the most secure and tech-forward technology available. So I now play the role of leading part of that effort and here I am being part of America’s largest credit bureau.
Peter: Okay, so then what exactly are your responsibilities at Experian, what are you focused on?
Alex: I am Group President for Consumer Information Services here, I am operationally responsible for the credit bureau here in the US and recently, also responsible for all of our credit bureaus around the world in terms of knowledge sharing, best practice sharing etc., etc.. So operationally, you should look at me as, you know, the business lead for everything that the US Experian Credit Bureau does.
Peter: Okay, okay. So one of the things that Experian Credit Bureau did recently was, you know, acquire Clarity Services. Actually, I heard you say that it has been a very successful acquisition and obviously, we’ve heard you say many times and others at Experian are interested in expanding the credit box. We’ll talk about Experian Boost in a little bit, but firstly, I want to talk about that acquisition and what have you gained from the acquisition of Clarity Services.
Alex: Yeah, the acquisition of Clarity allowed us to bring credit prediction to consumers who were viewed as unscorable by sort of the traditional credit bureau operations so us and our competition. Clarity Services has files on 62 million people who are thin file or no file and on people who are, you know, near prime and prime and super prime, but who engage in short term lending.
It’s interesting, in the US people think about short term lending as sort of being a type of lending that’s definitely not part of mainstream lending. I would agree with that, but the myth is that they are at the fringe of the population, they live paycheck to paycheck or something like that. That is actually a misnomer, that is not correct.
To give you an idea, one in five Americans gets a hit on the Clarity data, or to tell you the same story in a different way, if you look at the past four years, 100 of the 247 million adult Americans, so people 18 years or older, have actually, at some point, taken out a short term loan of one type or the other, the loan type that’s traditionally not covered by the credit bureaus, but when you get to 100 million people that’s active there, we believe, with our mission of being the consumers’ bureau covering everybody, so we acquired Clarity in order to capture the activity in that type of lending.
Peter: Right, right. So I’m curious…I mean, I don’t know if you know the answer to this, but is there….what was the overlap between Experian’s database, because obviously there were 62 million people at Clarity, some of those would have been already scored by you, but…was there very little overlap? Can you tell us some sense of that?
Alex: Yeah I do have the answer to that question, there was definitely overlap. So, the way you should think about it is the 62 million people who are captured by Clarity, of the 100 million total engaged in short term lending in the last four years, about 60% of them are overlapping with a traditional file. So they were…we already knew the name and there was at least one tradeline.
Alex: …most of those would be thin file. The other 40% are names where we obviously knew the name, the address, date of birth, the kind of stuff that we get from public records, there was no tradeline and Clarity sort of provides the only tradeline at the time. That’s how you should look at it.
Peter: Right, right, okay, got it. Another initiative, I know you talked about this at LendIt earlier this year, Experian Boost is another kind of initiative you to have to really help bring more people into the credit system. Can you just explain what it is and how it works?
Alex: It’d be my pleasure, Peter. Both of these initiatives that you are asking about are basically under the umbrella of, you know, our alternative data initiative. Let me say a few things about that before I answer your immediate question.
The reason why we’re passionate about and determined that we are going to be the lead in alternative data is because we have learned that lending history, which is the traditional data that credit bureaus use, is certainly a proven method to predict a consumer’s/an applicant’s likely ability when it comes to taking out a certain type of loan. But sometimes there isn’t enough data for all people.
In order to address people who may not have a lending history or not sufficient lending history, let me give you some examples, so it could be the super wealthy, those could be people who just turned 18 years old, those could be immigrants, those could be [inaudible] where it was a single income household taking the spouse’s name who didn’t have the income.
It could be heirs who, you know, live off the wealth of somebody who had passed away but who in the past didn’t handle financial dealings so there are populations that could be very similar to me, Peter and not people living on the fringe for whom lending history data is not available. And then we turn to what we call in this country alternative data. By the way, around the world, we refer to alternative data as data…
Peter: Right (laughs).
Alex: …because very few people in emerging markets actually have a real lending history.
So if you look at the fastest growing part of the world, the Asia Pacific markets, 5% of people there have any lending history, 95% need to be scored in other ways. So we have asked ourselves, how can we add data about people to our file if they’re comfortable with us having that to allow us to correctly and accurately represent their credit reputation. Could they pay back the type of loan they’re applying for, what type of risk do they represent because we do stand for fair lending, we want to help the lender and we want to help the consumer.
So, Boost is the outcome of more than three years of effort that we had here at Experian to find a way to incorporate consumers’ bill payment history; bill payments about utilities, mobile phones, cable bills, etc. into their credit report. The most popular scores out there like FICO, that was always foreseen [inaudible] that those would be part…we just had trouble having the utility companies and the mobile phone companies and the cable companies report that data. They didn’t.
So we have opened the door for consumers by allowing them to contribute that data and virtually all consumers have utility payments, mobile phone payments, many of us have cable TV payments and it has not been factored into the credit score. We’re thrilled that we have found a way to do that with Experian Boost and it basically works by consumers giving us their online banking credentials. Through that, we get access to the bank’s last statement for that consumer and then we have what is called a categorization engine that identifies those type of payments and then they get incorporated into the existing scores.
This is, you know, risk-free because there is no impact to the consumer, if they so choose. So we show the consumer their current score, then we look if there is a tradeline that we could add to their file. If there is, we tell them what are the tradelines, we show them what the tradelines would do to their score and then they can choose whether to add the tradeline or not. And we are delighted to say that after, you know, what is now ten weeks or something like that…we have 700,000 people…
Alex: …who have signed up for Boost, just to tell you from my own history, Mint which is an Intuit product and this is the most popular personal financial management software in the US with now 24 million users. It took Mint one year to get to a million users, we are ten weeks in, we’re at 700,000 so the velocity of adoption here is fantastic.
Peter: And so then what is a typical…you’ve got that 700,000, what is the average or typical increase in score that they’re seeing?
Alex: Yeah, so let me give you a couple of statistics. About 70% of all consumers do get a boost to their scores so their score increases. The average increase that people get is 13 points and coincidentally, 13% of the people who do get an increase jump risk bands.
Alex: So, you know, obviously this skews heavily to the no file and thin file. These consumers benefit the most because if you jump a risk band that makes you eligible for a certain type of loan, or if you were already eligible, it will qualify you for lower interest.
Peter: Right, right, okay, got it. So that’s super interesting. One thing I heard you say at LendIt as well is the consumer can opt out at anytime, right, so you opt in for this. Say someone decides for whatever reason they’re not comfortable anymore, they can just log in and opt out, is that how it works?
Alex: Absolutely, it is consumer permissioned and consumer permissioned means exactly what you have just said which is, you, the consumer, are in control. You can opt in and use it and if you don’t want to use it anymore, you do opt out.
You know, of course, we do encourage people to stay “boostable”, if you will, because if they keep Experian Boost activated, consumers can build a stronger payment history over time which would be to their benefit and we encourage it, but if they elect not to, they are 100% in control and can opt out at anytime.
Peter: Right, right. Okay, so I want to talk also about a new score that you are involved with and that’s UltraFICO. I want to be clear and we talked about this again at LendIt in depth, but how does that interface? What is UltraFICO, tell us that, and how is it different to Experian Boost?
Alex: Yeah, so up to now a positive behavior from consumers in terms of meeting their monthly financial obligations in relation to their income was hard for an underbanked consumer to demonstrate. The UltraFICO score is essentially a new score that incorporates transactional data from consumers’ checking, savings and money markets accounts, thus providing the opportunity for an underbanked consumer to access credit.
UltraFICO as opposed to Boost, which I talked about earlier, they will have attributes created around cash flow of the consumers whereas Boost incorporates the utility, mobile phone and cable bills. This does a cash flow analysis and then comes up with a totally new score. We estimate that that score will apply to about 15 million consumers that were previously unscorable which, you know, us and FICO together we hope that millions of households will have the opportunity to gain access to mainstream banking products which is, you know, a less expensive alternative financial product than the means that they turn to today and our collaborations with FICO and also with Finicity, who is the company that provides the account aggregation technology that’s required to make this possible.
Peter: Right, right. And so I know it’s pretty new in the game, do you have pilot lenders out there using UltraFICO today? Is there anything you can share on that?
Alex: No, UltraFICO is going to launch this summer so, Peter, we’re probably…I would say six weeks too early to give you data on that, but if we chat again in a couple of months then I’m happy to share.
Peter: Okay, fair enough. So something else, while we’re talking about scoring here…this was done a little while ago and you introduced the Clear Early Risk Score. We actually wrote about it on Lend Academy and I think that was about 18 months ago now, can you explain what that is and how does that fit in with other things like UltraFICO?
Alex: Right, so earlier we spoke about Clarity data, the Clear Early Risk Score builds Clarity data and it allows lenders to gain a previously unavailable view of a consumer loan and payment activity spanning both the mainstream and alternative financing spaces. Thereby it provides the most comprehensive consumer insight in the industry whilst providing financial access to more consumers, as I explained earlier.
Alex: The score has some unique analytics inside and applies those to leverage both Experian’s national credit bureau data and Clarity Services specialty data. It predicts the consumers’ credit worthiness over a 12-month period, it focuses in particular, at scoring risk of the first payment default and why that’s important, in short term lending, the loans are not installment loans, they are single payment loans so you think about a title loan or something like that.
You know, people borrow a certain amount of money and then agree to pay it back at a certain time; our own definition is shorter than 12 months usually so sometimes they pay it back next week, sometimes in two months, sometimes in a year, depends on the type of loan, and it’s a single payment, not an installment. This score predicts, if they were to take that type of loan, what would be the propensity to fail that payment as agreed upon. That’s the Clear Early Risk Score.
Peter: Okay, okay. So then the companies that are doing these single payment loans, they’re reporting the data into Clarity, is that how it works?
Alex: Yes, that is how it works.
Peter: Okay, okay, interesting. What about rental payments? We haven’t talked about that yet and obviously, you know a good chunk of the country doesn’t have a mortgage and they’re renting. To me, that’s something that would be also a highly predictive data point. What are you working on and how are you working with rental payment data?
Alex: Yeah, so a subject close to my heart exactly for the reason that you have just named…if you don’t have a mortgage, your rent is often your largest payment every month. We have an asset here at Experian that’s called RentBureau which we launched in 2010.
Just a little bit of context, nearly a third of all Americans are renters and Experian RentBureau has data on more than 20 million residents. That’s a great asset, to be frank, I’d like it to be even larger and it is a positive rental payment reported by all those landlords who are added to the Experian credit database.
It’s very helpful to 60% of consumers, those who are in the near prime segment, in particular. When they make those payments, it helps them qualify for a mortgage, or it helps them get to a new risk band. Rental payment we attempted to also get those through account aggregation so, you know, give us your online banking credentials and we can look and through a categorization find them, but we have learned some very interesting things that kept us from doing that.
What we’ve learned is essentially, somewhere around 45% of all rental units in the country are owned by institutional renters, large landlords who have sometimes millions, or sometimes only in parenthesis, tens of rental units. We would recognize those because they have a corporate name. The other 55% of all rental units are owned by people, people like you and me, so if a payment goes to Peter Renton or Alex Lintner, it is hard for us to recognize that it’s a rent payment.
Alex: Therefore, the categorization engine technology doesn’t work, like it would work when you pay to a company, a utility company, your mobile phone company, etc., etc. So we are still dependent on the reporting of renters and we work with our partners to grow that stake, our plan is to double it in the next three years and we’ll see whether we achieve that.
Peter: Okay. So I want to talk about privacy for a minute because, obviously, it’s a real concern with credit bureau data and even, I am sure, some of the customer contributed data that we’ve already talked about. What is your approach to privacy and how do you make your customers comfortable with that?
Alex: To state the obvious, I’m a consumer too…
Peter: Right (laughs)
Alex: …I am concerned about my own privacy and I respect that our clients and our end-users are concerned about their privacy, they should be and we want to help them with that concern. Having said that, our mindset is that for consumers contributing data, it’s not such a new thing. If you think about data that we contribute to social media, we contribute all kinds of data so why not make a contribution, something that helps boost your credit score.
That’s why we like account aggregation technology, the consumer permits us to do that, they can choose when they want to do it, they can choose when they don’t want to do it which is privacy. I choose what I do when I want to do it and I only show the people who I want to show. Hopefully we earn their trust, we do everything that we can to keep the data safe, we do everything that we can to make the experience, you know, frictionless so it’s easy for them to do that, and we do everything we can to present them with that opportunity at the time that’s most opportune for them.
For example, when they apply for a loan, or for example, when we know they have slipped credit bands and, you know, are working themselves out of a tough situation so Experian feels responsible for this data that was contributed. We will never charge for it, it’s under control of the consumer, available at all times and it’s free and we, nevertheless, but we don’t make money on it, we give it the same security treatment as we do with all our large data furnishers, the lenders in the country.
Peter: Sure, sure. So then you’re working with massive datasets at Experian, you’ve got billions of data points, millions of daily transactions and you know, it screams out for the use of machine learning and artificial intelligence because the datasets are so large, so maybe could you just comment on how you’re using machine learning in the development of some of these new scores?
Alex: Yeah, so we have an Extended View score here at Experian that we’ve had for a while that was generated using machine learning. You know, machine learning is one of those things that not many people understand so let me tell you a little anecdote that helps illustrate how we think about machine learning.
I have a parent of a very dear friend who was diagnosed with brain cancer, actually, and I asked her…I’m sorry to hear about your diagnosis, what are you going to do? Her answer was interesting to me, her answer was, I’m going to a doctor who is an oncologist, who uses machine learning. Why machine learning? She goes, well it’s very simple, this person is a mathematician, so she said, well, the average oncologist with over 10-years of experience gets the diagnosis right in 97.3%. The average oncologist with ten years experience who leverages machine learning gets the diagnosis right 99.8%. Alex, if you had brain cancer, what would you do?
Peter: Right, right.
Alex: Obviously we all know the answer. The point of the story is that the human thought leadership in putting together a score doesn’t get lost, but we have such vast amounts of data available today for a certain type of scenarios, for certain segments of the population, would there be other data elements that better describe the individual so that the individual gets reflected to the potential lender in a fair and accurate manner. That’s what we aim for.
If we think back about our own founding days, we were founded by a man called Si Ramo, the story that I tell about Si Ramo is very simple. He was a pharmacist in Nottingham, England who had a big heart, he gave pharmaceuticals to people who couldn’t pay for them immediately, he kept manual books, who did he give pharmaceuticals to so that they pay him back at some point.
He became the most popular pharmacist and keeping manual books didn’t do it anymore so he created a scoring system so he knew who he would get his money back, who to do that for so he could get his money back and who not to do that for, where he would likely not see his money. That evolved into the credit system.
Si Ramo initially knew people, he asked, well, who is your mom and dad, where do you work, and so he knew the local community understood the individual. What we do now with artificial intelligence…when we look across 147 million American adults we want to make sure that we, as Experian, represent the individual as who they are so that the lender understands the person sitting across from them is not some anonymous individual, it’s a real human being.
And that is what machine learning can help with so to give it more “gestalt”, I can use one German word (Peter laughs), more “gestalt” of who is sitting across from them and what should I take into consideration as I consider whether I give a loan and if so, at what interest rate which represents the risk the applicant represents.
Peter: Right, right. Okay, we’re running out of time, but I’ve got a couple more questions that I really want to get your thoughts on. First one is just the state of the American consumer because you obviously have great insight into that and, you know, we’ve been on this economic expansion for a long, long time and the consumer still seems to be doing pretty well, but there’s definitely been talk in the media over the last couple of months that maybe delinquencies are ticking up in certain segments so what’s your take on the American consumer today?
Alex: Peter, we just published our latest State of Credit Report, as we call it, it was released a little more than a week ago, and it takes a 10-year look back at consumer credit behaviors from Q2 2008 until Q2 2018. If you remember, Q2 2008 was the year when we headed into the financial crisis. If you take a look at this past decade, the report shows that over the past ten years we’ve seen a decrease in delinquencies.
My interpretation of that is we also see the average debt load for the consumer increasing, but delinquency is decreasing which means that I believe consumers are using credit more responsibly and that is reflected in their credit scores. The average score now is 680 on the Vantage scale and that’s a prime score so I think that’s good news for the American consumer.
Peter: Okay, so last question then. We’ve talked a lot about the thin file consumer and I’d like to get an idea of your goals there and what’s it going to take to get these tens of millions of people who are still unscorable, what’s it going to take to get that much closer to zero?
Alex: Yeah, we talked about it briefly when we touched on the topic of alternative data. Our goal is, you know, we regard ourselves as the consumers’ bureau, is to determine the credit worthiness of any consumer seeking access to credit. To move those tens of millions of people into mainstream credits will take innovation and alternative data is one of the ways which we can do that.
We spoke about Experian Boost where we get certain type of bills included and we spoke about UltraFICO which looks at the cash flow of the consumer, but we’re not done. Those are not the only things that we’re going to do. Improving access to credit is part of our fundamental mission and our innovation aims at finding more ways, more alternative data to continue to improve the financial health of consumers around the globe. That’s all of our charter and I think if we talk again in six months, in addition to Boost and UltraFICO, you’ll hear more news about how that can be accomplished.
Peter: Okay, it’s a great mission and I really appreciate you coming on the show today, Alex.
Alex: It’s my privilege, thank you very much for having me.
Peter: Okay, see you.
Alex: Take care.
Peter: One of the things that I’ve talked a lot about on the show over the years and particularly, in recent times, is just the importance of bringing more data to bear, bringing more consumers who are unscored or who have a thin file into the credit system where they can…the bottom line is they can save money. I’ve said before, it’s expensive to be poor and you may have not much money, but you’re always making your payments on time and you have a very low credit score, but you would be a good credit risk for some of these products.
What Experian has been working on, I think, is fantastic in bringing more data to bear to give a more accurate picture of those consumers in the thin file type population. I really hope that we can continue to expand that and it’s going to make a huge difference for those people who can suddenly obtain less expensive credit than what they have been used to.
Anyway on that note, I will sign off. I very much appreciate you listening and I’ll catch you next time. Bye.
Today’s show was sponsored by LendIt Fintech Europe 2019, Europe’s leading event for innovation and financial services. It’s happening September 26th and 27th at the Business Design Centre in London. Registration is now open as well as speaker applications. Find out more by going to lendit.com/europe.[/expand]
You can subscribe to the Lend Academy Podcast via iTunes or Stitcher. To listen to this podcast episode there is an audio player directly below or you can download the MP3 file here.
Peter Renton is the chairman and co-founder of Fintech Nexus, the world’s first and largest digital media and events company focused on fintech. Peter has been writing about fintech since 2010 and he is the author and creator of the Fintech One-on-One Podcast, the first and longest-running fintech interview series. Peter has been interviewed by the Wall Street Journal, Bloomberg, The New York Times, CNBC, CNN, Fortune, NPR, Fox Business News, the Financial Times, and dozens of other publications.