Welcome to episode #014 of the SDS Podcast. Here we go!
Today's guest is Senior Vice President of Risk Management, Greg Poppe
If you have ever applied for a loan and thought about the role data science plays in credit scoring models, today’s episode will satisfy your curiosity.
Tune in to hear how Greg Poppe uses various skills from his data science toolkit to the real life application of credit decisions affecting millions of people.
You will also hear Greg’s catchy “3As”: his predictions for the future of data science as well as jobs and career paths.
All in all a fascinating and insightful episode that I can’t wait for you to get stuck into!
In this episode you will learn:
- Challenges of Credit Scoring Models (14:22)
- The Law of Large Numbers (18:10)
- Market Segmentation (19:34)
- Model Selection (27:37)
- Ensemble Methods (32:47)
- Model Building (35:26)
- Addressing Fraud With Credit Scoring Models (42:40)
- One Skill – Effective Communication (47:07)
- The Future: Automation, Artificial Intelligence and Autonomy (56:25)
Items mentioned in this podcast:
- I Will Teach You To Be Rich by Ramit Sethi
- Innumeracy: Mathematical Illiteracy and Its Consequences by John Allen Paulos
Kirill: This is episode number 14, with Senior Vice President of Risk Management, Greg Poppe.
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Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.
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Hello and welcome to this episode of the SuperDataScience podcast. Today joining me on the show, I've got Greg Poppe, who is the Senior Vice President of Risk Management for Veros Credit, which is a financial services company that provides loans to people who want to buy cars in the US. And so can you guess what we discussed in this episode? That's right, we mostly talked about credit risk scoring. So in this episode, you will find out some interesting techniques that Greg uses when creating credit scoring models. You will also find out some of the challenges that he faces and some of the recent wins that he's had.
We'll talk about linear models, we'll talk about decision trees, we'll talk about random forests, ensemble methods, and much, much more. We'll also go into the space of the law of large numbers, and that will help us understand a little bit better what the difference is between trying to predict the outcome for one person versus trying to predict the outcome for many people. And that will give us an understanding of how these businesses actually operate and what their risk models actually mean when they're dealing with large populations. Also, Greg will share his perspective on where the world is going in terms of data science and I think you will find some valuable insights there as well.
So let's get this show started. Without further ado, I bring to you Greg Poppe, Senior Vice President of Risk Management at
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Hello everybody, and welcome to the SuperDataScience podcast. Today I've got my friend Greg Poppe with me calling in from LA. Greg, how're you going today?
Greg: I'm doing wonderful, Kirill. How're you doing?
Kirill: I'm doing well as well, thank you. And we've got a very interesting story to kick us off. I promised Greg I would torture him about this a little bit. So Greg was supposed to be on the podcast with me a week ago initially, but something happened in his life and we had to move it. So Greg, what was the reason why you weren't able to literally speak a week ago?
Greg: I had an emergency dental procedure. I had to get my wisdom teeth removed a week and a half ago. And it was very unexpected, my teeth were fine. And then one night, it just from 0 to 100. And the next day, I was in there, and then they took them out.
Kirill: It's good that you're fine now, and it's good that it all worked out, but when I read it, I was like oh wow, you must have something really serious going on. And it probably was, probably was serious. But I just never heard of an emergency dental procedure. So there you go. Learned something new.
Greg: Yes. Happy it's over with.
Kirill: Yeah. Fantastic. Alright. So you're feeling fine, fit, and healthy for today?
Greg: Feel wonderful.
Kirill: Ok. Alright. So Greg has a very interesting story, and we're going to drill in to his background today. So Greg actually works in credit risk, and he uses data science techniques to fulfill his job. Quite an interesting podcast ahead. And to kick us off, Greg, can you tell us a bit about what industry you're in and how it works?
Greg: Sure. I work in auto finance. So we're a consumer lender, and what we do is we provide credit for people to purchase vehicles. We are an indirect auto lender, and I want to clarify what that means real quick. So traditionally speaking, if you want a loan for a car or for a house or for a boat, you would go to a bank and you would apply in person and they would approve you or decline you and then whatever happens, you go on your way. We are different in a sense that we are not reaching out to the customer directly. We rely on a network of dealerships to provide us with a customer base. So how it would work, Kirill, if you’re trying to go buy a vehicle, you don’t have vehicle financing, you go to a dealership, you see a car that you like and the dealer says “Hey, Kirill, I can get you financed. Just fill this application and we’ll go from there.”
And then what happens is the dealership will enter all of your information into some software and that software will blast out your application to about anywhere from 2 to 15 different lenders. All of the lenders will reply with their approval or denial plus whatever interest rate they deem necessary for the type of risk that you are and they also charge a fee to the dealership to take on that risk. So the higher the fee and the higher the interest rate, obviously the higher the risk the person is.
The relationship benefits both the dealership and the finance company because the dealership may not have the funds to finance this individual, nor do they want to take on the risk of financing this individual. So what they will do is we will step in as the lender and we will charge what we call a “discount” or a “fee”. For example, if you want to finance, say $10,000, we will finance the customer $10,000 but we will only give the dealership $9,000 and that $1,000 difference is our fee. So the dealership is happy – they have zero risk and then we take it on – and we’re happy because we’re buying the loan at a discount price.
Kirill: Okay, gotcha. That’s a good explanation of how it works. The other question that pops to mind when you start talking about credits and finance, and I’m assuming quite a few of our listeners will be the same way, is defence mechanisms go up, you know. I feel like oh wow, you’re working for one of those sharks that you see in the movies, when people don’t pay up, you go break their legs, that type of thing. Can you put us at ease and just explain why the existence of your organization and the work that you’re doing, why it’s actually good for people, what it helps them do and how you establish and cultivate this relationship with your clients?
Greg: Certainly. So it’s in everybody’s best interest – our interest, the dealership’s interest and the customer’s interest – to make their payments. I think sometimes there can be a narrative that lenders want a customer to default on their loan so they can repossess the vehicle and then sell it again and then have another person repossessed and sell it again and sell it again and sell it again. We do not want that to happen. In a perfect world, if I do my job correctly, nobody’s going to default, and everybody is going to make their payments, and everyone is going to be happy.
Now, we are a specialty lender in a sense that we’re not competing against the big banks in the United States like Wells Fargo or Bank of America. Those are traditionally for prime or near-prime customers, meaning their credit score is probably above 650 or so. We are in a lower segment, a riskier segment, of the population where those banks don’t want to get involved because it’s too much risk, so we fill a need for that market that’s there. And a lot of these people who are our customers, they need this vehicle to go to and from work, to go to and from school, and without it, they have to take the bus or walk or ride a bike and it can be a very stressful situation without a vehicle.
Kirill: Yeah, I totally understand. So at the end of the day, your company is coming up with ways to help out the population that the banks are not taking care of. Just because of their size, they can afford to deal with certain types of clients. You know, it can be a difficult job at times, and it can be sometimes difficult for people to repay the loans, but at the end of the day, the onus is on the person to find ways to manage their finances so they only take on credit that they can eventually pay off and they can afford at the end of the day. Would you say that is a correct sentiment?
Greg: Absolutely one hundred percent. So if a customer is having trouble making payments, we will do everything we can to help that customer out. We will grant an extension, which means we will waive this payment but we add it on to the end. We will rewrite the contract to get a lower interest rate if we can. We will take a partial payment, and we will stop the collection process if they promise to make an additional payment later. So we try the best that we can to help the customer out in a situation because, like I said, we do not want to repossess the vehicle. That takes time and it costs money for us as well.
That being said, we also do a lot of work on our dealership screening side. If we find dealerships are committing fraud, we terminate the relationship. We set underwriting boundaries in our buying program to make sure a customer can afford the vehicle they’re purchasing. You know, if they say they make $2,000, we won’t put them in a car where their payment is $600. We won’t let them take a loan out that’s worth a significant amount more than the car is worth as well. We do all of that to try to protect the customer as well.
Kirill: Yeah, and which is fair enough. Okay, thank you for that description. I think that hopefully explains better to our listeners what exactly your company does. And moving on to your role in the company, what exactly is your role and what kind of work do you do at this agency?
Greg: Sure. I’m the Senior Vice President of Risk Management. The risk management term is sort of a broad umbrella for data science and financial engineering, if you will. So what our department is in charge of is the computer program that gives the response back to the dealership. When the dealership submits an application, we get a whole set of attributes about that customer. We run our risk models against that data, the predictive models, and then it gives us a score. That score translates to either approval or a decline plus an interest rate and a fee that we charge. We send that back to the dealership, and then they choose whether or not they want to accept the terms of the loan. If they do, great, we fund the loan. If they don’t, then we don’t.
Kirill: And all of that happens very quickly, right?
Greg: Oh, yeah, within a second. It’s all automated. That is probably the largest burden that we have. Other parts are just general analytics within the company. You know, we’re building a model that will tell our collectors what is the best time to call a person based on their previous account history. So if we’re making calls every day at say, 3:00 p.m., and we don’t get a response, then we make a call at 6:00 p.m. We will build a predictive model that says “Okay, next time, call the person at 6:00 because they’re probably at work when we call at 3:00, that’s why they don’t answer.” And so we become more efficient that way. We do all sorts of analytics for the entire company.
Kirill: Okay, so optimization of your company operations, something like that?
Greg: Yes, correct.
Kirill: That’s pretty cool. And the financial engineering side of things that you mentioned, is that the model that spits out the credit result?
Greg: Yes, it’s related to that. So basically, we have to make sure that the interest rate that we approve a loan at and the fee that we charge will cover the anticipated risk of that loan given all the operating costs that we have involved with it and everything.
Kirill: Okay. That’s really cool. Greg, I didn’t know that you were a Senior Vice President. Wow! It’s a very impressive role. Do you have a team that you work with?
Greg: Right now it’s three people, including myself, and we are expanding. So we’re a fairly small company at the moment. Our portfolio is about $250 million, which I know sounds like a lot, but in our industry it’s on the verge of a medium-sized to a slightly larger medium-sized company. The previous company I came from, where I was Head of Risk there, was about $3 billion portfolio.
Kirill: Wow! Huge. But now you’re—from our conversations, now you don’t have to be at many locations at the same time. You’re happy in Los Angeles and that was the trade-off, right?
Greg: Yes. Absolutely.
Kirill: Definitely. Okay, let’s move on to data science. Like, the financial engineering side of things, you said that’s the heavier side of data science of your company. Let’s start with that. So how do you use data science and analytics and obviously building these models? What goes into building a credit risk model? How do you go about thinking about one, what kind of algorithms do you use, or maybe even tools? Can you talk us through that?
Greg: Sure. So at the end of the day, our basic goal is to estimate the amount of cash flow that will come in from our loan. So if we finance someone for $10,000, we will expect a certain amount of that to come back. It’s kind of an abstract thing to think about it as one loan, because one loan either pays or they don’t pay. But if you have a portfolio of thousands of loans, some pay, some don’t, and if you have a thousand loans that are $10,000 each, you’re getting 1,000 payments every month, you know, in a perfect world. But in reality, you might get 990 one month, and then 980 the next month. So the goal is to look at historical data and see sort of the timing of when those cash flows come in and how they come in. And that’s a data science issue, but it’s not like a predictive modelling issue. It’s more just “this is how it comes in.”
And then from there what we do is we get a general shape of a curve of how these cash flows come in. Let’s say it’s a non-decreasing curve, meaning it will never go down. You’re never going to get payments going backwards – it’s always going to be a cumulative effect, right? Hopefully. If you have payments going backwards, then someone is stealing from your accounting department. And that’s what we do, we apply a risk model to that, which is a likelihood of default. And we say for every single loan, we can adjust our cash flow expectation up or down based on the risk. And then what we do is we try to solve for what interest rate and what fee we should charge to sort of make all the loans have the same expected return.
Kirill: One loan you can’t really predict, but when there’s many of them you can have an expected result. You can adjust for it, right?
Greg: Exactly. So think of it like -- you know, I would have a hard time telling you with any high degree of certainty, “This loan will pay. This loan will pay. But this loan won’t.” However, if you give me a portfolio of a hundred loans, I should be able to say “15 aren’t going to pay. I don’t know which 15, but 15 won’t.” And then if you give me another portfolio that’s say riskier, I should be able to measure that risk and say “This is a riskier pool. 25 aren’t going to pay. And again, I don’t know which 25, but I’m estimating 25.” And that’s how we measure our accuracy. So it’s not so much on a loan-by-loan basis. It’s “If we just select a random sample, how many did not pay, and what was our expectation of that?” And if they’re very close, we consider our models to be accurate.
Kirill: Yeah. And the larger the sample that you pick, the more precisely you can evaluate your model, right? In a sample size of 10, there could be a huge random error. All 10 cannot pay just accidentally. But in a sample size of 1,000, it’s much more precise.
Greg: Absolutely. And we would stratify that among different risk metrics that we have. So, for example, like a credit score. If we take the lowest say, 25% of all loans we booked, the lowest credit scores, we’re going to expect a higher rate of default than say, the top 25%. And the question is, how accurate was our prediction?
Kirill: Okay. That’s very powerful. For those of our listeners out there listening to this podcast, this is a very fundamental principle of data science that Greg has brought up here. And it’s all based on the Law of Large Numbers which says exactly that – that as you increase your sample size, your average is going to converge to your expected value.
I’ll give you an example: If you flip a coin 10 times, we know that the expected result is 50/50. But if you flip it 10 times, you can easily get 7 heads and 3 tails. But if you flip a coin 100 times, it’s much harder to get 70 heads and 30 tails. You might get 57-43, you might get 55-45. If you flip a coin 1,000 times, it’s pretty much not impossible, but very near to impossible to get 700 heads and 300 tails on a fair coin. That just means that as you increase your sample size, your actual result – the percentage heads to percentage tails – is converging to your 50/50. And that’s exactly the principle that Greg is leveraging in this design of the models that they’re creating.
Greg: Absolutely. A regression towards the mean.
Kirill: Okay. So once you’ve establish that, you would have different models for people with higher credit scores and different models for people with lower credit scores? Is that right?
Greg: Well, it’s going to be one model, but it may be segmented differently. When we try to segment a population, I try to always look at not so much different behaviours, but what are different profiles of people. A couple of segments that we have are a) what we call a thin file or a ghost, which means they have no credit history whatsoever, or they maybe have a very, very thin credit history like a $500 credit card or something like that. That could be either because they’re 19 years old, they’ve never had credit. Or they’ve avoided credit their entire life. Whatever the reason may be, if you had a dataset that had default and non-default in a whole set of attributes and you looked at an algorithm to find the most predictive attributes, if you took that segment, they would have a very different set of attributes than somebody with an established credit history.
So what we try to do is we say, “Okay, let’s take our thin files or our ghosts and make them a separate profile.” And we will look at other things like the structure of the loan, which would be the down payment, the loan to value, their payment to income, their job time or the general stability. But if you look at a person with established credit, you’re really looking at that credit. You’re trying to see okay, they’ve had two auto loans in the past and they paid each one very well. They also had a couple of credit cards they paid well. So those are the attributes that are more important than their job history. Because we make the assumption that they may have had instability their entire life, but they still find a way to make that payment.
Kirill: So it’s about the habit of the person rather than where the money is coming from?
Greg: Correct. In the credit world, we call it “intent to pay” and “ability to pay”. And there’s a very big distinction in there.
Kirill: Yeah, you might have the ability to pay, but you can be like “Nah, I’m not paying.” Right?
Kirill: Okay. So that actually brings up an interesting notion. I was reading this book by Ramit Sethi. I think it’s called “I Will Teach You to Be Rich”. Yeah, because he has a blog “I Will Teach You to Be Rich” and the book is also called “I Will Teach You to Be Rich”. That’s one of the things he talks about. Like, I’ve always tried to avoid credit in my life. But what he talks about is that whether you need credit now or not is not important. What is important is that when you will need it, will you be, like you said, one of those ghosts in the eyes of a credit lender, or will you have some history? So what he says is even if you don’t need credit, it might be a good practice to have some credit cards and just put some minor expenses on them but still pay them off so that in the future—I know in your case it’s cars but this applies across the board, the mentality—so in the future, you can demonstrate this intent to pay. Is that the same way that it works with your business with vehicles?
Greg: Absolutely. So we look at it—if somebody has no credit history, we have no idea if that person has any intention of paying. And if you think about it, any person with an 850 credit score, which is the very, very upper range, all of them started out at one point with no credit score. So you’re wondering if that person who’s applying now with zero credit, are they a future 850? And if so, you want to give that person a loan. Are they a future 520? You might want to decline that loan. But you don’t have anything to base your decision on. So you just go off the things that you do know: their job, their income…
Kirill: Gotcha. And in terms of credit score, just because we don’t have it in Australia, I have a vague understanding of what it is. What does it range between and what goes into your credit score?
Greg: So the lower range is 400 range and the upper range is close to 900. In the States there’s three main credit bureaus. There’s Experian, there’s TransUnion and there’s Equifax, and they all have their own proprietary model, so it’s not a standard. However, generally speaking, they’re on the same range. They look at the same attributes. What they really look at is your payment history, the length of time that you’ve had credit, and they look at your inquiries. So are people pulling your credit?
That one is sort of a touchy subject for some consumer advocates. They may say “Well, who cares if people are checking? How does that affect someone’s ability to pay?” So the laws have changed on that. Now you’re able to do what’s called a “soft pull” to view someone’s credit without affecting their credit score. However, it is important to look at inquiries from a risk perspective because Kirill, if you come to me and apply for a loan and I see that you’ve applied with five other banks over the last 60 days, I may think “Well, if you’re still applying, that means they all declined you. And why do they all decline you?”
Kirill: Yeah, gotcha. That’s a bad situation to be in.
Greg: Yes. So if you want to build credit, my recommendation would be limit the inquiries that you have. Obviously, make your payments, don’t be delinquent on those. Establish as early as you can – even if you’re not using your credit – establish some sort of presence on the bureau and don’t max yourself out in debt. And I know it’s easier said than done, present company included. We were all students once. Again, like you said, there are no bureaus in Australia, so I don’t know if they were to come to Australia, what they would do there.
Kirill: There probably is, but it’s just that the credit score is measured differently than the United States.
Greg: Yeah, probably. Right. So strictly, this is the American experience.
Kirill: Yeah, gotcha. I’m sure that was helpful, some tips for our listeners, unexpected tips on how to manage your credit situation. We got a bit off-tracked from our data science. So moving back to your models, you’ve told us a bit about things you think about when you’re creating the models. What tools do you use to build these models?
Greg: We use a wide range. At previous companies we used SAS. When I say previous companies, this was probably 7-8 years ago. SAS is kind of going by the wayside, I think, in the FinTech world, and now it’s more R and Python. So at the company I’m at now, we use R. I personally prefer Python.
Kirill: Why is that?
Greg: Just familiarity with it. No real reason. I wish I could say oh, the regression formulas are more finely tuned. You know, at the end of the day, it’s for the most part 99.9% the same. It’s just that I’m more familiar with Python.
Kirill: And what techniques do you use in R for building these models? This is the fun stuff. We’re getting into the fun stuff!
Greg: Right. Okay, so primarily in the credit decisioning models, we use regression models. And the reason why—well, there’s quite a few. One is it’s very computationally easy. It’s easy to explain, it’s easy for people to understand but it’s also not a black box in the sense that a lot of models can be, and what we need to do is we need to provide a continuity to a dealership because they can adjust the parameters of the application and that will adjust the risk accordingly. So, for example, obviously they can’t adjust the customer’s credit score or their income or their job time. But what they can adjust is the type of vehicle they put them in, the down payment that they get, the price that they sell the car for, the term of the loan – are they going to pay the loan in 60 months or 48 months or whatever.
So all of those things can change and we need to provide sort of a continuity of our risk-backed dealership, meaning if a dealership submits an application for a $2,000 down payment and they get an approval and we say we’ll charge a $1,000 fee, they expect that if they get say $2,500 down, that that fee will drop proportional to the risk, because it is a less risky loan. And a regression model is very good for that because you’re getting incremental increases or decreases in your score depending on those attributes. If we were to go with a CART model or any other decision tree model, if the first break point or the first cut point in that model is down payment and they go from one side to the other, it can throw it down a completely separate set of decision logic and they can get very strange approvals. From a data science perspective and from an analytics perspective, that may be more accurate but it’s not sellable, it’s not marketable to the dealership.
Kirill: Wow! That is fantastic. That is mind-blowing. I mean, even just that one point is well worth listening to this podcast. I learned something cool just now. Like, you know in advance that maybe there are techniques out there like a decision tree or a Random Forest or a gradient boosting algorithm. Something that could probably provide a model of the situation better from a data science perspective but at the same time the people who are using these models – your partners, the dealerships – they except that as they change the parameters, there is some sort of continuity, as you mentioned. So they want to see the models spit out what they expect. So if they’re increasing the down payment, they want the risk to go down and consequently for the price to be lower and the interest rate to be better and things like that. But if you use other models, it won’t happen so that is why you stick to the regression types of model. That is a very cool concept of where data science meets explaining things to people and working in partnerships and stuff like that. So that’s a really cool way of doing it.
Greg: Absolutely. And I would advise anyone who is working in any sort of model where they need to give a response back to a customer, if any parameters can be changed by that customer, try to go with a straightforward model. Now, what we’re trying to do is we’re trying to separate our attributes from fixed attributes, things that will never change like the customer’s credit, and adjustable attributes, like a down payment or a sales price. We’re trying to right now build a more complicated model on the fixed stuff, because that will spit out a score that will never change, and then merge that with maybe a regression model with the adjustable attributes. So we can maybe get a little more lift out of our model and still keep the continuity of it.
Kirill: Yeah, that was my next question. It’s definitely a valid point. Stuff that is not going to change, you could use it separately. For instance, have you considered—let’s say you take the fixed attribute, like credit score, and based on that, you put people into categories and based on which category they’re in, you apply a different regression model. Is that how you’re going to approach it or is it going to be like—
Greg: Absolutely. You got it 100%. That’s exactly right.
Kirill: Gotcha. That’s a pretty smart idea. Wow! This is getting exciting. So for that fixed part, have you decided what technique you are going to use? Is it going to be just decision trees, or is it going to be an ensemble method or something like that?
Greg: It will be an ensemble method, probably a Random Forest. They tend to be the most robust.
Kirill: Yeah, gotcha. And just for the sake of our listeners, could you explain what an ensemble method is and why you think a Random Forest is more robust than a decision tree?
Greg: Sure. An ensemble method combines hundreds, thousands or more votes, if you will, into one score. One of my favourite phrases in the data science world is “the wisdom of the crowd”. You have all these little decision trees that are voting and at the end of the day you merge all of those scores together to get one final score. And the problem that you may run into if you use one decision tree, even if it’s a very good one, is if you get into nodes that can be thin or, like I said, things that can change, that can really affect the score. And if you go to an ensemble method, you minimize outliers and you minimize the effect of one strong variable.
If we continue with down payment, for example, if the dealership knows that they’re going to get a better approval with a stronger down, with a higher down payment, they are inclined to lie about that. And if you have a singular CART model, that can really underestimate your risk. And if you have an ensemble method, you can sort of average out all of the votes and you get a much smoother transition.
Oh, actually, now that I say that, that reminds me of another reason why we use regression. We can limit, we can put caps on variables like down payment. So we can stop giving credit once a certain variable gets too high so it doesn’t drown out the model.
Kirill: Yeah, that’s really cool. Thanks for the explanation. It was a great explanation and I’m sure a lot of people find it useful. And that’s exactly it. The ensemble methods have a better predictability because they’re leveraging the power of many rather than just one. Okay, so now we know the techniques you use. How do you go about building a model? I can just imagine. You mentioned that right now you’re trying to improve your model, trying to get some additional lift in your model by splitting out the fixed and adjustable attributes. At the same time, I can imagine this is a very lengthy process. You sit there, you’ve got a team, you try out this method and then you try this, you adjust this… What is this process all about? How does it happen? Can you walk us through from start to finish? What steps do you undertake to actually spit out a model in the end, like five or six months later?
Greg: Sure. By far, the biggest hurdle in any model building process, I’m assuming, but definitely for us, is the data, is getting a clean dataset. Now, when it comes to credit variables, those are clean for the most part. They’re coming from a trusted reliable source like Experian. You have a lot of integrity in that data. Now, when it comes to a lot of other factors that are—in a perfect world, if the data is right, that can be very important, like job time, like income, like down payment, you start to get into a grey area where a) you can be relying on a human to input these values and they can very, very easily fat-finger something; b) there is maybe not the true value of that field. So down payment, for example. A dealership could say the down payment was $2,000, and it really wasn’t. They could say it was $2,000 but what it really was is the customer traded their vehicle in and the dealership gave them $2,000 for it, which is a very different thing than giving $2,000 cash down. So when you have data like that, it’s very hard to separate what’s accurate and what’s not.
With job time, it’s open to so much interpretation. “How long have you been at your job?” “Two years.” So someone on our end might just put in 2 for 2 years. Well, what if someone else puts in 24 months? I have a value of 2 and I have a value of 24 and it’s supposed to mean the same thing. Same thing for income. Is that your weekly income, your monthly, your yearly? So there’s a lot of data scrubbing, data processing, that goes into that, data auditing.
It’s very common in any industry to switch systems from one CRM to the next or from one platform to the next, making sure that data was all migrated properly. Many times it’s not. Many times something was never collected. So if I’m looking at a set of loans for the past 3 years, we may have only collected the vehicle miles 8 months ago and on, so I don’t have that data going back 2 years or 3 years. So I think getting the data is definitely the biggest challenge.
Once we have the data and we’re very comfortable that it’s clean and accurate and precise, then it’s a data exploration process where I just test all sorts of theories that I have and I basically bend attributes into certain groups of 10 – deciles or quantiles or maybe just a binary field, yes or no. I just look through it manually, like “What is very predictive of loss?” Are all the customers that have, say, a repossession in their previous credit history—how do they perform against people that have no repossession? So I have a flag, “auto repo – yes/no”, and I look at the default rates for each of those. And I start to sort of just build a mental list. You know, I write it down and I say, “Okay, these are the factors that I really want to look at.” Then I’ll go into more detail and I’ll really look through them and I’ll make sure the data is all correct. I’ll see if there’s any weird things happening there.
We try to get sort of a linear relationship. So if you take down payment from $0 down to say $5,000 down, you expect a very straightforward relationship in the default rates. When you look at say 0 to 1,000, 1,000 to 2,000 and so on. If you see something like a U shape, that doesn’t make sense. So you want to dig into that and see if there’s a reason why. Once that is all done then it’s a fairly straightforward process. You know, you code whatever library you’d like, whatever model that you want – probably a few different ones – you check the accuracy, you measure it, you measure it against the hold-out data sample to make sure it still holds if you’re looking at out-of-bag sampling.
Then the hard part starts because you have to get management sign-off, and executive sign-off. It’s unfortunately non-data scientists who are executives. Probably one of the trickiest parts of my job is trying to explain—you know, they’ll look at one particular loan that charged off. That looked really good on paper, but it charged off. And they’ll say “How come it didn’t work on this loan?” And there’s always that one exception that you have to deal with. They see this loan they don’t like, they see that loan they don’t like. So getting the sign-off on that is probably one of the hardest steps.
And then once that’s done we put it into beta testing and we run it in the background for a couple of months just to make sure a) it’s working correctly, that it’s calculating the numbers correctly. And then b) also to make sure that it’s still being predictive. And then once we’re comfortable with that, then we launch it into production. We monitor it like hawks. We adjust them constantly. We may make a new model once a year. So by adjusting, that is something sort of like—say we have a score from 0 to 100. We may find out “Hey, the model is not being very predictive on scores of 30 and below on say high LTBs.” So we may say, “Okay, let’s just decline those.” So we’re making an adjustment on sort of the box that we allow through the door, or we may decide just to increase a fee on that.
So those things are all adjustments that are probably going on, I would say, once a month. To measure the accuracy, all we do is we have an expected cash flow that we’re expecting to come in. And it kind of looks like a logistic function if you cut off sort of the beginning tail. So, it starts off sort of slow, it increases rapidly, and then it starts to flatten out and approaches 1. And our cash flows should follow that line very closely. If they start to deviate from that line, then we dig in and say “What’s wrong? Something is not working correctly.”
Kirill: Gotcha. It makes sense. That’s a very involved process. There’s lots of elements to you creating a model while you’re looking up some models and convincing executives and so on and coming up with new models. I’m just curious—out of everything you’ve done, out of all of these iterations and out of models that you’ve created throughout your career, what would you say was your biggest win in terms of using data and data science?
Greg: Biggest win? So fraud is a huge issue for any lender, obviously, because we’re not meeting these customers face-to-face and we’re relying on a dealership to give us the accurate information. There can be fraud from the customer perspective – they go and they lie to the dealership. There can be fraud from the dealership perspective where they lie to us. Or there could be both the dealership and the customer are in on the fraud and they both lie to us. So trying to minimize that is a huge priority for us. And fraud is always a tricky thing to call fraud. Because you have to show intent and it’s very hard. So we just have a lazy definition of it, and we say if somebody just did not pay at all, like their first payment, that’s fraud. Because that person never intended making a payment anyway. So we use a third party data provider who aggregate data from about 300 or so different sources and they’re looking at everything from buying data from other companies to what they can find open-source on the Internet and they helped us create a fraud score. It was very accurate.
If the customer provides us with an e-mail address and their home phone number, which we require on the application, they can tell us a) how long that e-mail has been in service or been in use, and they have sort of like a fuzzy language-processing network that it goes through that says how close does this e-mail address match their name. So for example, my name is Greg Poppe. My Gmail address is gpoppe – you know, first initial and last name – it’s very obvious that that is me. But if it was losangelesguyinthesun – you know, that’s a very suspect name. And if that e-mail address has only been in use for 30 days, well that’s even worse. And if their cell phone they provided to us is just a prepaid cell phone that they got last week, then there’s red flags going up all over the place. You try to think in the mind of “What would I do if I wanted to commit fraud?” and it would be “Well, I’m going to create a fake phone number or I’m going to get a phone just for this. I’m going to create an e-mail account just for this and I’m going to try to mask my identity as best I can.”
Kirill: Okay. So thinking like the criminal?
Greg: Yes. Yes.
Kirill: All right. So you implemented this system. Did it show results?
Greg: Absolutely. Basically it cut our what we call “first payment defaults” in half.
Greg: So that’s a huge win for us.
Kirill: Fantastic! That’s a great result. And did it have a lot of false positives, or was that minimized?
Greg: That was minimized. So it came through as a yes/no. About 12% of applications came through flag marked as “yes”, and of those about half of those turned out not to make a first payment or two. And I say two because the dealership is on the hook for the first payment. So if the dealership is committing fraud in collusion or not in collusion with the customer, they will make that first payment to get themselves off the hook.
Kirill: Okay, gotcha. Interesting. So, low false positives and you cut that in half, that first down payment not going through. That’s a really great result and very interesting. How long ago was this implemented?
Greg: 6 to 8 months ago, I think.
Kirill: Yeah, powerful stuff. Okay, so that was your biggest win. And what would you say has been your biggest challenge? Apart from what you mentioned about cleaning the data, what has been your biggest data science challenge when working with credit risk models?
Greg: Biggest challenge besides clean data? I would say executive sign-off and understanding of the models that we build. It’s very hard. I would say if there’s any students out there who are studying data science, I would say one skill that I’m very thankful for is—when I was in college I was a math and statistics tutor and it was a great experience because it really helped me explain complex problems to people who were not invested in the subject, who did not have a background in the subject. So as their tutor, I had to be very precise with my words but very specific and explain these very difficult concepts to them so that they could understand it. And once they could understand it, then obviously they get better grades and everyone is happy. I think that experience, doing that as a student, really helped me. I basically take that same approach when it comes to explaining this to management. So getting their sign-off is incredibly important and challenging. And if you can’t communicate it well – what you’re doing and why you’re doing it – then it’s an uphill battle.
Kirill: Yeah, I totally agree. And that is some rock-solid advice about tutoring and about teaching. I completely side by that. That if you want to get that valuable experience of communicating data science and insights from your analytics—it’s a very helpful thing to do, to try to explain or try to teach data science or explain data science to your peers. That’s helpful to them because they get to learn data science, but it’s also helpful to you because you get that valuable experience of explaining complex things in a simple manner and that’s exactly what you need in the workplace. At the end of the day, there’s lots of people who can crunch numbers, who can write models and code in R and Python. What counts is the people that can convey those insights to the end stakeholders, so it’s a very, very important skill to have and I’m glad you highlighted that.
Speaking of education, I noticed you had an education that is very closely related to your career currently. You did a Bachelor of Science in Mathematics, you’ve got a certificate in data mining, you’ve done graduate coursework in applied statistics. What would your advice be to people who don’t have that background but who want to get into the space of modelling or into the space of credit risk scoring? How would they go about gaining these skills that they might be lacking that you were able to develop during your studies?
Greg: Sure. You know, during my studies, computer programming was not on the menu. You know, it was 10+ years ago.
Kirill: You’re so old, Greg.
Greg: I know, I know. The extent of my computer programming experience in college was a class in C++ and that was it. R was around, obviously, but it wasn’t really widely used in anything outside of academia. Python was the same thing. And those languages have become so popular now that if you don’t invest the time to learn those, you’re at a tremendous disadvantage. The good news now is if you have any programming experience it’s fairly easy to pick up these skills. There’s so many tutorials on the Internet for free or for even not a lot of money. Like I know on Udemy, you do the R courses. I took one of the R courses, actually, and it was very helpful.
Greg: So all you listeners, check out the Kirill catalogue on courses. They’re very good.
Kirill: Thanks, Greg.
Greg: No worries. But you can get a very good free or nearly free education in coding, and a lot of these will offer certificates. I don’t have any certificates in Python or R or any other computer programming language. I do in SAS, but no one uses it anymore. So I would suggest just take the initiative to learn. And it depends on what your ultimate goal is. If your goal is to be like a hardcore data scientist, you’ll have to spend a lot of time learning the mathematics behind the algorithms, the reason why they work and why they don’t work and how to adjust them and how to apply them and when and where to apply them.
If you want to maybe be in more of a managerial role and oversee data scientists, then I would say it’s less important to have the details but more important to have the communication ability and project management. I mean, most of my job right now is just project management.
Kirill: Gotcha. Yeah, I really like that point. Actually, we’ve got this machine learning course with Hadelin which we’re finishing up still. It’s huge. We’re putting a lot of content into it. I was sitting down and explaining on video, or preparing the slides to explain a Kernel support vector machine, that algorithm, and I was asking myself the same question, like “Into how much detail and depth should I go?” A lot of the videos out there, a lot of the content that you find online, it goes into a lot of depth. Like, all the way. It tells you everything about what support vector machines are, and the different types of Kernels that exist, and how these Kernels work. They go into the formulas. But when you think about it, I think there’s a level of acumen that people should have, especially going into data science role. And then if you’re a manager you might take a step back from that. You might not need that much detail.
If you’re doing the algorithms, that acumen might be enough. You don’t need to know the nitty-gritty mathematical academic formulas to everything about support vector machines or Kernels and stuff like that to apply it properly and get results. On the other hand, if you find that you do need that stuff you can go and spend some additional time learning. A lot of people fall into the trap. They try to learn everything in a lot of depth, whereas I think the space of data science is so broad you can’t just learn everything in huge depths. It’s better to learn everything to an acceptable level of acumen and then deepen your knowledge in the spaces that you need. Would you agree with that?
Greg: A hundred percent. I would have to agree. You know, if you really want to get down and dirty in the mathematics and the formulas behind all these algorithms, MIT and Stanford all offer free videos of their lectures. You can just go on to the MIT website. Actually, I did watch one on SVMs – support vector machines. Having a background in mathematics is a huge advantage. I think what turns people off is just the notation alone. And having a background in mathematics, I’m very comfortable with the notation, but I think that scares a lot of people. It looks like Chinese to some people. But at the end of the day, if you take the time to sit down and understand what all these Greek alphabet letters mean, it’s just like reading a sentence when you look at a formula.
Now, if you don’t want to get into that detail, I totally get it. You can be totally fine without it. I have never once in my career had somebody ask me what are the formulas behind the algorithm. And if they did I would say “Give me a second. Let me consult my friend Google.” You know, it’s not like an exam in school where you have to know it or you don’t. Now, you’d probably do it if you want to work for Facebook or Google – I think they ask you those things – but there’s a lot of jobs out there for people that don’t know them.
Kirill: Exactly. And you can totally be successful as a data scientist.
Greg: Absolutely. A hundred percent.
Kirill: All right, that’s been a great overview of the skills and how deep you need to go into the skills. Plus we’ve already kind of touched on the history of data science. You’ve mentioned that you didn’t have R and Python, and you had to learn them as you progressed through your career, and also the decline of SAS, which was interesting to hear about. Now I want to look forward, I want to look ahead. What would you say from where you are in your position right now, from what you’ve seen about data science, where do you think this industry is going and basically what should our listeners prepare for?
Greg: That’s a great question. And I do myself spend a considerable amount of time thinking about this myself, oddly enough. I think the future is the three A’s: automation, artificial intelligence and autonomy. That’s where I think the future is going. You know, automation is here. It’s been here for some time, and I’m not just talking about automation of tasks that you can program. A dishwasher and a copy machine – those are all humans trying to automate tasks. That’s here and it’s not going away anytime soon. And it’s always been very simple. I can make a machine or a robot to automate the task of taking part A and putting it on part B and then sending it to the department that makes part C. It’s simple. I don’t know if anyone’s heard, we’ve had an election here recently. It was a very mellow election. I’m sure nobody really paid attention to it.
Kirill: The biggest one in history!
Greg: I’ll try to keep my statements as apolitical as possible, but one of the themes was bringing American jobs back that were outsourced as a by-product of trade and globalization. And I think that yes, some jobs have been lost due to that, but the majority are lost to automation. So no matter what, it’s a bitter pill to swallow, but those jobs probably aren’t going to come back the way they were once before. It’s a cold hard truth, and I think we have to adapt and adjust our expectations rather than moving backwards. If you try to fight a rising tide, you’re going to lose all the time.
So with that framework in mind, the question is what’s next. And I think we’re just only at the beginning of automation, to be honest. Like I said, we automate simple tasks and what we haven’t been able to do is automate tasks that require decision making. And I think that’s where the artificial intelligence aspect comes into it. You can quote me on this – predictions are fairly accurate – but I think that the biggest innovation that we will see in our lifetimes outside of the Internet, which I think will revolutionize the world, I think the next innovation is going to be self-driving vehicles. That encompasses the three things. It encompasses automation, artificial intelligence and autonomy. Once self-driving cars are out there, I think it will put taxi drivers, Uber drivers, FedEx drivers, UPS drivers, the trucking and shipping industry – all those jobs are going to go. And what do we do next? I don’t know. I guess that’s a good question for our politicians and our lawmakers to discuss.
That being said, I would say anything where you can learn the skills of programming that can affect any of those three areas is going to be critical. I think the self-driving cars are sort of the first step into a more broad technology, which is the automation and the autonomy. If you think about it, if all these cars are self-driving, it’s sort of like a hive technology. Why can’t we use that same technology to fight a disease in somebody’s body? Maybe a more scary scenario is fighting a war. I think once that technology is – I don’t want to say perfected, but improved, it’s going to change our lives dramatically in ways that I don’t think we can even comprehend at the moment.
Kirill: Very interesting. I totally love that rule, AAA, automation, artificial intelligence and autonomy. And yeah, very profound examples of how technology behind self-driving cars can be used elsewhere. You’re right, it’s a huge disruptor that will not only disrupt the industries and reduce the amount of available jobs significantly in specifically in those industries, but it can be used throughout other places. And this is like a first step. We don’t even know what’s coming next.
Greg: Yes. And I think – from a cultural and a societal standpoint – it is the first step for trusting the machines over the humans. And it could have been anything, but it just happens that cars are going to be the first thing, I think, to breach this new frontier. And I think about it all the time because this will directly affect my industry.
Kirill: Yeah. You sell cars!
Greg: Yeah. So if they’re not buying cars, or they’re not driving cars, or it switches to something like a car rental program like, if with your cell phone you buy minutes and it’s like you buy minutes or miles for a car every month, then how does that change my industry? It’s going to change it significantly.
Kirill: Yeah, totally. Greg, I feel like we could just have a whole podcast dedicated to the future of data. It’s really interesting. Unfortunately we’re running out of time. But we might do that separately.
Greg: Oh, I’m in!
Kirill: Yeah, for sure! But it’s already a good point for people to start thinking about it. Think about these things that you see around you, and think of how they will change your industry because this disruption isn’t local. It’s global and it’s going to impact everything.
Greg: And it’s not going to go backwards.
Kirill: Yeah, it’s not. It’s definitely not. It’ll be very interesting to see what happens in the years to come – 2, 3, 5 years – it’s all happening very soon. Wrapping up today, how can our listeners contact you, follow you, find you, follow your career, ask you or bombard you with questions about the future?
Greg: Sure. They’re welcome to contact me on LinkedIn. You can search for my name – Greg Poppe. It’s P-O-P-P-E. The handle is just linkedin/greg_poppe. They’re welcome to contact me on Gmail as well – it’s [email protected] Feel free to add me, follow me or whatever. I will respond the best I can.
Kirill: Wonderful. We’ll definitely include all those links on the show notes page on SuperDataScience. Thank you very much for sharing and being open to that. And one final question: What is your one favourite book that you can recommend to our listeners for them to become better data scientists?
Greg: Sure. There’s a book I read in college, so it’s a little old. It’s called “Innumeracy”. It’s written by a guy named John Paulos. It’s very short, it’s I think 150 pages maybe. It’s definitely under 200 pages, so it’s not intensive reading. The book is basically about how the author feels – and I agree with – that society has embraced sort of how it’s cool to not be good at math. You hear people all the time say, “Oh, I’m so bad at math.” “I’m not a math person.” But you would never in a million years hear somebody say the same thing about English or history. And it’s kind of like a joke. So the author sort of outlines this problem facing society called innumeracy, which is basically like illiteracy but with numbers. And he goes through a lot of examples about how people just have a general misunderstanding of numbers. I distinctly remember one example he gave where if you were to put like a 12-inch ruler and you were to say “Okay, let the left side of the ruler be 0 and the right side be 1 billion. So halfway in is a half of a billion. Where would you put 1 million?” And people would put it at the 8-inch mark or at the 9-inch mark! It’s just a general misunderstanding of numbers and analytics and science. Yeah, it’s great. So I advise everyone to at least check it out. It’s a good one.
Kirill: Okay, gotcha. Thank you so much. So that is “Innumeracy” by John Paulos. Guys, check it out if you want to learn more about how not knowing mathematics has become a joke in our society. Thank you so much, Greg. I really appreciate you coming on the podcast and sharing all your experiences with us today.
Greg: Thank you, Kirill. I was happy to be here and happy to do it again any time you’d like.
Kirill: For sure. We’ll definitely catch up again sometime soon. All right, take care, have a good day.
Greg: Thank you. You too.
Kirill: So there you have it. I hope you enjoyed today’s show. As you can see, by the end of it we went into a very passionate discussion of where the space of data science is going and what to expect in terms of job opportunities, in terms of the future of different industries and various roles in society. So it’s a very, very interesting, and some very powerful insights there from Greg.
From this whole podcast, my main takeaway, the thing that struck me the most was when Greg mentioned that the way they create models is with a large focus on the end user. So even though he knows that there might be more powerful modelling techniques that he can explore, such as Random Forest or other ensemble methods, some of the time, he actually uses a simpler methodology such as a simple linear regression or other regressional methods to create his model in order to maintain continuity in the output of the model so that the person using the model can see that what he expects is happening. So if somebody is using the model and they get a certain percentage as the result, so a certain interest rate as a result of inputting their clients’ parameters into the model and then they suggest to the client to increase the down payment on the loan, then they expect that the interest rate will go down. So the model should comply with that. And it should be kind of intuitive to the people that are using it. And that was a very powerful insight and very interesting way how data science and the psychology of the end user actually blend in together to come up with this hybrid result.
So there we go, that was our episode with Greg Poppe. As always, you can get the show notes at www.superdatascience.com/14. We will include all of the resources mentioned in this episode along with the best ways to contact Greg if you want to bombard him with questions. And to finish off, if you enjoyed this podcast, then make sure to leave us a rating on iTunes to help spread the word about SuperDataScience and get more people interested and passionate about developing a career in this fantastic space. And I look forward to seeing you next time. Until then, happy analysing.