How Alternative Data is Transforming Lending

Jim Marous (00:07):

Welcome to another episode of Banking Transformed, the podcast that dives deep into the trends, innovations, and strategy shaping the future of banking. I'm your host, Jim Marous, owner, and CEO of the Digital Banking Report, and co-publisher of The Financial Brand.

Jim Marous (00:20):

As traditional credit scoring methods face increasing scrutiny and limitations, alternative credit data has emerged as a powerful tool for lenders looking to expand access to credit and drive profitability.

Jim Marous (00:33):

In this special miniseries on the Future of Lending sponsored by LoanPro, we're excited to welcome two industry leaders, Warren Hogarth, co-founder and CEO of Empower, and Colton Pond, chief marketing officer of LoanPro.

Jim Marous (00:48):

Our guests explored the examples of alternative credit data points, the evolution of underwriting processes, and the regulatory considerations surrounding the use of non-traditional data.

Jim Marous (00:59):

Warren will also discuss Empowers recent acquisition of Petal Card and the future of credit, while Colton offers insights into the shifting focus from debit to credit products in the FinTech space.

Jim Marous (01:12):

As the lending landscape continues to evolve and consumer expectations shift, embracing alternative credit data will be crucial for lenders looking to stay competitive and to expand their customer base and foster long-term growth.

Jim Marous (01:25):

However, the adoption of alternative credit data also comes with challenges including regulatory scrutiny, data privacy issues, and the need for robust analytics and underwriting models.

Jim Marous (01:36):

So, Warren, let's start with you. Can you share a bit about your journey as an innovator, investor, and visionary? And also, what are some of the most promising opportunities you see in the lending space?

Warren Hogarth (01:47):

Thanks, Jim. I really appreciate you having me on today. Excited to be here.

Warren Hogarth (01:51):

A little bit about my journey. Before founding Empower, I was a partner at Sequoia Capital for about eight years, where I invested in a range of early-stage startups, FinTech being one of the areas I focused on.

Warren Hogarth (02:07):

I was very fortunate to invest in a company called Sunrun, which is the leading solar finance company in the country that's now public, and also, one of the early robo-advisors called FutureAdvisor, which was acquired by BlackRock.

Warren Hogarth (02:19):

So, I had this unique window, and also, while I was at Sequoia, we had invested in Stripe, and Square, and Nubank, and a number of sort of the most iconic FinTechs of the last decade, actually. And so, I got a front row seat to see sort of how new technology enabled disruption in FinTech.

Warren Hogarth (02:38):

So, if you fast forward a little bit to Empower, the opportunity was both a personal one and sort of professional. Personal, when I was an immigrant to the US, when I came here, I did what I thought were the right things.

Warren Hogarth (02:50):

I paid all my bills out of my savings and cash flow, which I thought was the right thing to do. Then when I went to borrow money to get a car to drive to work, I was told I couldn't have a loan because I was actually supposed to borrow money to pay my bills. That was a better person to underwrite. And that never made sense to me.

Warren Hogarth (03:05):

And then many, many years later, all kinds of similar, what I thought were first principles decisions turned out to be the wrong decisions.

Warren Hogarth (03:12):

And at the same time, I was again, sort of sitting there as an investor and I was seeing the first wave of how AI was disrupting sales and marketing and all of these different areas, and I thought there was a unique opportunity to do something for the US consumer.

Warren Hogarth (03:30):

And put that into perspective, there's about 260 odd million adults in the US and 60% of the populations, about 160 odd million people have at best, the one in two chance of getting access to credit. And yet credit is really important for social mobility and financial security.

Warren Hogarth (03:50):

If we take out our ability to access credit, most of us may not have gone to the university we went to, or the college we went to, we might not be in the house we're in, we might not be driving the car we're driving. Our relationship might be different.

Warren Hogarth (04:03):

So much is impacted by credit, and yet 60% of the population has such a poor shot at getting access. And so, I saw an opportunity to use new forms of data to hopefully expand access to credit and improve the lives of people that way.

Jim Marous (04:20):

It's interesting, while it's more than a noble cause, but it's challenged at every point. Certainly in the US with regard to doing things outside the norms of what we've done for decades since I was in banking 50 years ago, 45 years ago.

Jim Marous (04:38):

So, when you founded Empower, what challenges did you find in that path to try to build a new product set and a new way of looking at the accessibility of credit? What were some of the roadblocks you saw?

Warren Hogarth (04:53):

I mean, I think the first one is just a general skepticism. Lending is a space where I think there are a lot of failed attempts at innovation.

Warren Hogarth (05:05):

And it's one of those things that it's hard to know early on if you're going to be successful because you might lend a dollar today, but you might not know for three months or six months if you're going to collect 90 cents, or 98 cents, or 50 cents.

Warren Hogarth (05:21):

And so, I think because there's been a lot of attempts at the past and a lot of things have failed, there is a healthy dose of skepticism.

Warren Hogarth (05:28):

And then just generally as an entrepreneur, you've got to layer on the fact that you've got to be a little bit on the spectrum, a little bit crazy to start a company in the first place.

Warren Hogarth (05:36):

And so, typically, you're used to getting a lot of nos, but maybe instead of 8 out of 10 people saying you're crazy, in lending, I think it's probably 9 out of 10. So, you had to be a little bit resilient.

Jim Marous (05:48):

Colton, it's interesting, I've been in banking a long time to say the least. And whenever I looked at credit scores, especially in the last 20 years or so, I realized there was so much left out and nobody understands them.

Jim Marous (06:02):

I mean, if you're trying to fix your credit, you don't understand what you have to do to get there. And you pay down a credit card and you all of a sudden see your credit scores go down instead of up, and all these other elements.

Jim Marous (06:12):

But overall, the FICO scores and credit scores overall looked at such traditional things and to get out of that mold has not been the easiest thing, as I mentioned.

Jim Marous (06:24):

What do you see happening in the marketplace around experimenting with new alternative credit data points without increasing credit losses? Where do you see traditional financial institutions going and what do you see as their barriers to actually embracing this change?

Colton Pond (06:43):

Yeah, it's a great point, Jim. We're at a fascinating time, and that's why I am excited that Warren's here to talk about Empower, and the journey of Empower is one kind of the, from my perspective, early innovators and adopters of how to look at credit and credit underwriting uniquely and differently in the space.

Colton Pond (06:59):

But we're at an interesting time because debit interchange is shrinking and we'll continue to shrink close to zero as pay by bank and other payment methods come. You see a lot of financial institutions then saying, “Okay, I need to shift my focus and refocus to credit to be able to increase profits and revenue.”

Colton Pond (07:16):

But simultaneously, we're at a time of economic uncertainty. So, we're seeing, especially in unsecured lending, default rates take up too.

Colton Pond (07:26):

So, it's like, “Hey, how do I shift my focus or balance my focus with credit now, as the pressure happens within interchange without increasing my risk and increasing my default rates? And increasing my credit losses?”

Colton Pond (07:41):

And generally, you bring up a great point. Banks and credit unions are relatively risk averse. There's a lot of factors influencing their ability to use alternative credit data.

Colton Pond (07:50):

I will say that the institutions that I know that have used it and be able to implement it to the highest degree, have set up a side innovation hub, whether it's through their digital brand, or a neobank, or some side innovation hub where they can test new aspects.

Colton Pond (08:09):

Sometimes it's new technology in this conversation though, it's new underwriting models, new data points to look at, new things to look at.

Colton Pond (08:16):

So, they're not influencing the broader lending of what they do in the mothership or their traditional bank, but they can test new things, understand the impact on credit losses, understand the impact on approval rates. And then based on success or failure, roll that out to the broader institution.

Colton Pond (08:32):

And that's the approach that I've seen be most helpful within financial institutions who are actively looking at how do I adopt alternative credit data? And what does that look like?

Jim Marous (08:43):

It's interesting, Colton, we've known each other for quite some time, and you many times get out of your realm of influence and what you're doing when you're trying to sell, let's say LoanPro services.

Jim Marous (08:55):

When you're working with traditional financial institutions, I know that when you see the back office of these organizations and you're trying to implement new ways of doing things with your partnership with financial institutions, you also, can't help but get your hands into other things that are going to improve a financial institution's ability to provide credit.

Jim Marous (09:15):

What is the biggest overriding challenge you see around the ability to embrace change, not just from a alternative credit data standpoint, but when you're trying to sell LoanPro services?

Jim Marous (09:27):

What do you see as far as organizations that you're saying, "Guys, we really have a better way of doing things. It may sound like it duplicates the way you do things today, but it's different." How do you get through to organizations that are saying, "We get it but I'm not too sure we want to do it."

Colton Pond (09:47):

So, the change management process within banks and credit unions is hard. There's two challenges that they face. One is legacy infrastructure where technology holds them back and a legacy vendor says it's 12, 18, 24 months to be able to make a change. That's a big problem and does hold folks back.

Colton Pond (10:05):

But the other aspect is change management, and what does that look like, and how to get the board on the right page, how to get the leadership team on the right page, how to get all my individual contributors on the right page.

Colton Pond (10:15):

A bank I'm close with, actually Mascoma Bank up in New Hampshire, they're a $3 billion community bank, but they've done a great job at influencing change. They're going through a digital transformation right now, migrating from Jack Henry to Thought Machine, adding Alkami on there, going to a truly modern stack.

Colton Pond (10:33):

And when I asked Raphael, the CTO and president of their sister company, how he's been able to do so, he said, "Hey, it starts at the top aligning internally as a leadership team, this is the direction we want to go and this is why, and taking strategic bets."

Colton Pond (10:49):

"Aligning with the board and helping the board gain a longer term perspective, not a shorter term perspective because anything within a bank on a change is a longer term perspective."

Colton Pond (10:58):

"And then getting the team members excited around the impact that they're going to be able to make and the reason why they're making those changes."

Colton Pond (11:05):

And the banks I've seen, credit unions I've seen actually make an impact of, have effectively done change management the right way while modernizing their debt stack.

Jim Marous (11:14):

So, Warren, to get a better understanding of the widened potential of alternative credit data, what are some of the data points that you've seen helpful in understanding the underwriting process, and how has your view of different alternative data points evolved over the last several years?

Warren Hogarth (11:35):

Yeah, great question. So, I think for everyone's context, just to level set, sort of one of the questions is why now? What's unique about this moment in history that we can do this, that we haven't been able to do it in the past?

Warren Hogarth (11:49):

And there's a couple of building blocks that scaffold things. The first one is very much access to new data in what I would say generally decently structured formats and in close to real time or real time.

Warren Hogarth (12:02):

And so, some of the sources, for example, would be open banking data, whether you get it through Plaid or an MX or somebody like that, it gives you this cashflow stream for the user.

Warren Hogarth (12:13):

And then another example might be some of the alternative data sets like Experian and some of the bureaus have as well, that might give you stuff that's not on the traditional report, but I think can add context, which is very, very valuable for the user.

Warren Hogarth (12:27):

And so, if you think about, again, this sort of the problem statement, we have credit scores today and they do a good job for certain segments of the population. I think if you're prime, it does a really, really good job.

Warren Hogarth (12:41):

But I think, again, for the 60% of folks who aren't prime, who have at best a one in two chance of approval, and for many it might be 15%, 20% chance of approval, if we can add more context to augment that data, we can make better decisions.

Warren Hogarth (12:58):

So, take one example. Let's say someone's a 650 credit score and we layer on some cashflow data or some alternative data.

Warren Hogarth (13:07):

Instead of seeing back in the past that 6 months ago or 12 months ago when that person might have been going through a divorce or some sort of family crisis or what have you, we can actually see that their income stream is improving. The volatility of their incomes going down, their spending has been adjusted.

Warren Hogarth (13:25):

And so, you can get this real time holistic view of the user and a much better sense of their trajectory, their capacity to repay, et cetera. So, maybe that person had a 40% chance of being approved before, and they're in that 40th to 50th percentile range.

Warren Hogarth (13:41):

Now, we can say, well, actually, if we add all this extra context, we can see things are going really nicely. And we can use that to sort of tip the decision in the user's favor and underwrite their potential and where they are today, not where they were 6, 12, 18 months ago.

Warren Hogarth (13:54):

And so, say that's the history. If you sort of peel that back a little bit, you look at cashflow data, for example, there's so much you can use. We actually have about 200 features that we create in our model. So, you take the raw data, you structure it, you featurize it, and then you train your models.

Warren Hogarth (14:12):

And those things again include duration of income, how many sources of income, volatility of income. So, lots of cashflow variables there.

Warren Hogarth (14:26):

Then it can be how quickly do they spend their money? How burdened are they with other debt? What's the trajectory? There's so many ways you can look at the features and what's going on that are very powerful.

Warren Hogarth (14:38):

And similarly, you can look at utility data or phone data. And so, you can bring all this in and create a new tapestry. And again, with the other advances we've had in technology, so we can get that in real time.

Warren Hogarth (14:50):

And then within a hundred milliseconds, we can have a ML model go through that data much more clearly than a human can and tell us, actually this person's on a great trajectory and therefore we can give them an instant decision.

Jim Marous (15:07):

It's interesting, I went to Shenzhen China in, geez, it was January of 2020, so it's the beginning of the COVID crisis and visited WeBank, the biggest digital bank in the world.

Jim Marous (15:17):

And I had this eye-opening experience where they were evaluating credit capability, not from a standpoint of how much should we give them as much as, will they go bad, just point blank, will they go bad? And what they used as a primary source of credit data was phone usage.

Jim Marous (15:36):

They build a model that said, we can tell from phone usage whether or not this person has a high likelihood of going bad. And then they included everybody else, but it was for different levels of credit.

Jim Marous (15:48):

So, they did not look at risk of avoidance. They looked at risk management, which is a completely different view of the way that traditional banking, certainly in the US has ever viewed credit and risk and all these other things.

Jim Marous (16:03):

And what they said is, "We realize some of these people may go bad, but if we find those that we know are not good players, we can give them at least certain levels of credit that will not throw off all of our balances. And yes, on an individual base, we may have losses, but they're going to be small losses."

Jim Marous (16:22):

Warren, from your perspective, how much of what's going on is just legacy thinking around risk where you gave a great example of a divorce situation? Maybe aren't going to be able to give them a traditional amount of credit.

Jim Marous (16:37):

Maybe it's going to be just a bridge to make it so that they can pay for food on those months that things don't balance out versus we got to look at this person as a traditional $1,500 personal line of credit person. If they do or they don't, it's either a go or no go as opposed to an adjustment.

Jim Marous (16:57):

I mean, just from my perspective at least, I get frustrated. Yeah.

Warren Hogarth (17:01):

It's an interesting time is what I would say. I think there's lots of reasons that maybe a more traditional institution might not be as comfortable embracing the alternate data.

Warren Hogarth (17:13):

And I think you got to remember many of these institutions have been against open banking. So, there's already this thought process that's changing. Many of them can't join the data in their own systems which is a challenge as well. See, the data's very messy. Just because I get a raw data stream from an open banking provider.

Warren Hogarth (17:37):

Classic example early on I remember is at AT&T Park, (I used to live across) what now, it's changed, Oracle Park. But the baseball park, since we're talking baseball earlier in San Francisco, I used to live across the corner from the Willie Mays statue.

Warren Hogarth (17:51):

And because it was called AT&T Park, every time you swipe your card at a vendor, it came out on the statement as AT&T something or other, but that wasn't the same as paying your phone bill.

Warren Hogarth (18:00):

And so, you've got to right pass that data, but you got to have some intelligence on that data, but it's hard to do from a raw transaction string.

Warren Hogarth (18:06):

So, I'd say part of it is just there's this institutional challenge where traditionally banks had to do that because the credit firms have done that for them and cleaned the data and given it to them in a very clean format, or even given them variables themselves.

Warren Hogarth (18:22):

I think some of it is, look, just the context that the banks are going through an interesting period right now, due to the post COVID reaction and risk and people are pulling back. And so, it's not necessarily the right time to extend themselves.

Warren Hogarth (18:34):

And so, there's a lot of reasons but I think as more of this becomes clear I think you will start to see people weighed in. Now, do they do it themselves? Do they do it through acquisitions?

Colton Pond (18:46):

So, Warren, one other thing that I have that I want to touch into that you mentioned, but it's really important given the audience and banks and credit unions and the landscape. You mentioned using a machine learning model to be able to analyze, make a decision.

Colton Pond (19:01):

What percentage of credit decisions that Empower made automatically without a human intervention, and like how is that important? How has that made an impact? Why is that important for Empower's business model?

Warren Hogarth (19:16):

Yeah, 100% are made automatically-

Colton Pond (19:19):

That's what I thought it may be so. I lost in hoping, but that's what I thought.

Warren Hogarth (19:23):

And it's important because you've got to then flip this to the consumer value proposition. From the consumer's point of view, they don't necessarily want to know that they're going to be approved or have access in 10 days or 14 days time.

Warren Hogarth (19:39):

They want it ideally in the moment, especially if, again, typically people who aren't in the prime category, often I would say they're often time poor again. And so, when you flip the shoe on the other foot and put yourself in the consumer situation, waiting is valuable. Like time is money in that sense for them.

Warren Hogarth (20:03):

And so, if they can with confidence, know that they've got coverage or if it's a medical emergency, or if they want to get a something for school, for their kid, they probably want it in either the now or today, not well into the future.

Warren Hogarth (20:16):

And so, that certainty and then the knowledge or the security that comes from knowing that you've got a decision is very, very valuable.

Colton Pond (20:25):

Warren, the other thing that I'll pen in and tie into that is yes, a quick automatic approval, but then also how quickly can you get them access to the money and funds, and some of the evolutions we've seen on the payment side to be able to get funds immediately and instantly.

Colton Pond (20:39):

Because you're right, maybe I'm at the doctor and I don't have the money and I don't have a credit card, and I need something immediately to get something that helps me and my family and our lives.

Jim Marous (20:50):

It's interesting that with PayPal — I have a business, I was getting money for subscriptions, and PayPal regularly would offer me bridge loans instantly pre-approved because they know what my flow of funds was. And they were making the decision based on more flow funds than anything else.

Jim Marous (21:08):

And yet my traditional bank that has the account for my business, it would take 14 days minimum to not only go through the credit adjudication process, which I'd have to do in the branch, but also, then to get approved and then get just distribution of money.

Jim Marous (21:24):

And as you said, Warren, the reality is that whole decision process, if I need it, I need it today. And I don't want the risk of having to go to a second institution if my primary doesn't say they'll do it.

Jim Marous (21:39):

So, Colton, I'm going to start with you on this question because it all feeds into this. How does generative AI and the use of the future of AI really change the game here from a perspective of kind of predicting what the impact's going to be by adding new layers of alternative credit data or peeling back the risk assessment that we've done in the past.

Jim Marous (22:03):

Colton, what's your perspective on how AI and generative AI and all this is going to really change the game with regard to the accessibility to credit from people that have thin files?

Colton Pond (22:17):

Yeah, great question. So, I think there's two immediate impacts from my perspective, but there's also a risk and the risk that I want to talk through that we haven't talked through yet.

Colton Pond (22:25):

One is, Warren talked through, you get data, open banking laws and Dodd-Frank 1033, and the stuff we're seeing in open finance is awesome and providing a greater availability to access data.

Colton Pond (22:35):

But Warren's spot on, sometimes the data's not clean, it's not structured. A lot of these AI models can affect more effectively than a human cleanse and interpret and categorize the data in the right way to be able to make decisions.

Colton Pond (22:49):

And then the second is how to use a model to be able to actually effectively make a decision. And as Warren said, make a decision in real time for the consumer that decreases zero credit losses and gets the consumer what they need as far as access to funds as quickly as possible.

Colton Pond (23:04):

The risk though, and especially with banks and credit unions, and I'll tell a quick story. LoanPro's based in Utah, our co-founder, CEO, sits on the Utah Governor's Fintech Board and in the economic office development.

Colton Pond (23:19):

And they tasked us, and some of the leading banks in Utah to, "Hey, the governor wants to increase lending to very small businesses. Can you all get together in a think group and understand how can we increase access or to funds for very small businesses? Folks that are like one, two, three, five employees."

Colton Pond (23:39):

Companies like Stripe have done a great job underwriting these folks based on cashflow data and getting them access to funds immediately.

Colton Pond (23:46):

So, we met, and one of the ideas is, “Man, if you had access to some of this data outside of banking data in a central repository that we can make decisions on, that'd be great.”

Colton Pond (23:55):

But one of the credit unions and banks was like, "Hey, one concern that I have is the increased pressure from the CFPB and other regulatory entities on fair lending. And if you can't perfectly describe it, you can't use it."

Colton Pond (24:13):

And that creates a real challenge for banks and credit unions that are potentially exploring how do I use alternative credit data outside of a world that the CFPB is not really fully looked at other entities that regulate these financial institutions.

Colton Pond (24:29):

It's creating a challenge and it's created some uncertainty where a lot of financial institutions are not approaching the space as a result, which I think negatively impacts society and access to capital and all the great things that Warren and the team at Empower are doing.

Jim Marous (24:47):

That's interesting, Colton, because what we see is financial institutions use regulations and compliance as an excuse. And not that it's not important, but you've got to fight that battle.

Jim Marous (24:58):

So, I'm going to throw that back to you, Warren, because with Empower, that's key to your business model. You can't just lay down and just do what's maybe clearly indicated by compliance and regulators as to what they're going to accept.

Jim Marous (25:15):

How do you look at the regulatory environment today, and how are you working to change some of the perspectives of ... I say the one definition of regulators is that they're the longest, most entrenched bankers, which makes it a challenge at times. How do you work with the regulatory environment right now?

Warren Hogarth (25:37):

So, look, the regulations are there for a really good reason, and I think if you go to the first principles, it's really around inclusion and discrimination. And so, the way we think about is, let's take a step back and look at the data.

Warren Hogarth (25:50):

Over 50% of white Americans are good credit, so they have a very, very good chance of getting credit. When you move to the different underrepresented populations, let's say you look at African Americans, it drops to 20%.

Warren Hogarth (26:03):

So, how are we going to increase access? You're not going to do it by focusing on people who are 720 and above. You're going to focus by giving more access to people who are shut out of the system today.

Warren Hogarth (26:16):

And so, I think this is where the big opportunity is. It's how do you, rather than just approving 20%, improve those same 20% of people, but can we get another 10% of people into the system?

Warren Hogarth (26:30):

And if we're doing that in that population, by definition, we're going to be increasing access for underrepresented populations. And I think at the core of it, that's what the regulations are trying to ensure happens.

Warren Hogarth (26:45):

And so, I think when you explain it from that perspective and you just look at the data, I think it becomes much more clear to people that it is what needs to be done, not just what is possible, but it's like that's how we're going to make a difference. And so, that's part A.

Warren Hogarth (27:08):

Then part B is, okay, well, there are things like explainability that you need to be able to do. And so, I don't know yet how to use a neural net to be explainable. That's one kind of AI model. So, we don't use those models. We use models where there's very clear guidelines and there's very clear explainability.

Warren Hogarth (27:27):

And so, we can comply with the rules, and we can also, further the purpose of the regulations that's there by enhancing access to underrepresented populations. So, that's how we approach it.

Jim Marous (27:43):

It's interesting, Warren, because we've seen this before and I talked to a very large bank about a year and a half ago, and I said, “When you're doing innovation, how do you deal with regulations that may not be clear in that area?” We were talking about open banking at that time.

Jim Marous (27:59):

And the reality is, they say, "We know what the regulations are meant to be doing. We know what their intention is. So, we sometimes go outside of the realm of what's clearly defined to say we're going to fight that battle and show why we are fighting that battle. And usually, we're informing the regulators on what's possible."

Jim Marous (28:20):

I go back in the US records and talk about signature cards. We all thought we had to have signature cards when the reality was, until somebody woke up and said, “All we're having to do is know your customer. We have to have a way of knowing the customer.”

Jim Marous (28:34):

And signature cards were just the element, but it was a very antiquated element that no longer was needed. And we never get out of that until somebody said, “We think we can define this in a different way.”

Jim Marous (28:44):

So, I want to shift topics slightly, I guess, Warren. And number one, congratulations on your recent acquisition of Petal Card, which really expands the capabilities that you have. Can you explain a little bit about what Petal Card is and what gap it fills in the marketplace?

Warren Hogarth (29:03):

Yeah, thank you. We're very excited. So, the short version is that Petal Card was the leading consumer credit card that used cashflow based underwriting to enhance access to credit.

Warren Hogarth (29:18):

So, again, if you think about the non-prime, the 60% of the population that earn 720 or better, they were the leading independent company that used cashflow based data with their underwriting to increase access and provide a simple product for users and a way for people to also, then start building credit further and proving that they are capable to everyone else in the system.

Warren Hogarth (29:42):

And so, yeah, we acquired them because our mission is to enhance access to fair credit because we believe deeply it improves social mobility and financial security. And so, they were the leaders in this space. And we want to be best in the world at serving marginally served users.

Warren Hogarth (30:00):

And so, we brought this capability in-house that enhanced our products set substantially. And there's a few other capabilities that we have. So, other data sources, and also, we also think a lot about once you've approved someone, how you can make them more successful at credit.

Warren Hogarth (30:19):

And that's by understanding the cash flow. Then you can think about how to customize repayment schedules that are matched to a user's cash flow, for example. So, that's a capability that we thought would also be very complimentary.

Warren Hogarth (30:29):

So, it's a great opportunity to put the leading consumer credit company with where one of the leading cashing vents and revolving credit companies for marginally served users.

Jim Marous (30:41):

Well, it is got to be interesting because you're building a real strong data set in this marketplace of underserved consumers.

Jim Marous (30:50):

And I would imagine that makes it so that innovation going forward, ways to fill other gaps in the marketplace, gets more and more powerful because of this data set that you have that is certainly better than almost any traditional financial institution that hasn't put their feet into this.

Jim Marous (31:08):

I mean, you know in many cases using traditional alternative data points and alternative ways of assessing credit worthiness where you can go next. I mean, doesn't that happen where as you're building this dataset, the models get better?

Warren Hogarth (31:26):

The short answer is yes. And I think you'd asked me a question earlier just sort of what have I learned over history? And I think that's one of the most powerful things in compounding in this space.

Warren Hogarth (31:34):

If I look at the last three years, every time we've improved our models and we do it about every six months, we roll out a new model with both more data points and features, but more training data that underlies that model and more nuance.

Warren Hogarth (31:53):

We've taken our loss rates down over three years by about 65% while expanding access and while expanding average credit line by more than double.

Warren Hogarth (32:07):

And so, those are usually intention and pulling against each other. But because we've got tens of millions of loans or advances now, across millions and millions of consumers over, and the repeat usage, it compounds in very, very powerful ways.

Jim Marous (32:26):

So, Colton, from your perspective, using the same foundation of that question around the building of more and more use cases, more and more data points to be able to prove the shift in thinking and banking, what do you see in the marketplace with LoanPro and with other things that you see in the financial services industry around payments and around lending where the acquisition of new data points, new models have really changed the innovation process at financial institutions?

Jim Marous (33:00):

I mean, your product loans, LoanPro, was really built out of a need that many financial institutions may not have realized they had, but it builds on the efficiency ratio. What do you see out there today?

Colton Pond (33:13):

So, Warren brought up a great point and something that I think Empower and others leading folks in the space that are decreasing credit losses do. A lot of folks look at alternative credit data in a silo of underwriting and origination and my first interaction with you.

Colton Pond (33:29):

But, Warren, I love when you brought up, hey, cashflow data determines when you get paid, and can influence payment cycles, and auto pays, and how you get paid, and what does the servicing experience look like?

Colton Pond (33:39):

Other examples that we've seen several of our customers look at is, with a line of credit, how do you look at repayment data and data that you have and grow on the customer to either decrease or increase available credit on that line?

Jim Marous (33:56):

And I think from my perspective, the leading institutions and FinTechs that we've seen truly adopt alternative credit data in the right way have viewed it more holistically through the entire lifecycle of how does it not only influence my origination on underwriting, but how does it influence my servicing collections and those activities that I have as well.

Jim Marous (34:21):

It's interesting when we look at this and this is what's been great about this series on the future of lending that's been sponsored by LoanPro, is that we've looked at different areas. We looked at personalization, we looked at alternative innovation models and things of this nature.

Jim Marous (34:38):

And now, we're looking at alternative credit, which really fits into both categories in a way, whereby you have a much more personalized credit experience for those that have never had the credit availability. And you're looking at innovation.

Jim Marous (34:52):

So, in both of your experiences, (I'm going to start with you, Colton) what are the biggest obstacles preventing widespread adoption of not only just alternative credit data, but new innovative formats?

Jim Marous (35:06):

Because I'm sorry, in the banking world, lending has been lending, has been lending but there's more going on now than ever before, but it's not moving fast enough.

Jim Marous (35:17):

Colton, what do you see as the biggest obstacles preventing what I'll call innovation in credit, innovation in lending, innovation in payments? What's been the biggest challenge?

Colton Pond (35:29):

Yeah. So, if you would've asked me two years ago or three years ago, I would've for sure said open banking, open finance laws. The US was far behind the rest of the world from my perspective in open finance.

Colton Pond (35:41):

Luckily, and I think good for financial institutions, good for FinTechs, and good for consumers, we have seen increased momentum there and it looks like we're on the right track and on the right pace, which is awesome.

Colton Pond (35:52):

When I look at today, a lot of especially banks and credit unions struggle in using more modern technology, whether it's alternative credit data or whether it's a way to streamline your collections, whatever that is due to their modern or their legacy infrastructure.

Colton Pond (36:09):

And one of the biggest aspects is how do I look at my core provider, not as the everything be all, but how do I look at it as the ledger and the source of truth and I innovate and I do things above that level?

Colton Pond (36:25):

Modernizing that tech stack and infrastructure truly enables my perspective the next gen of what banks and credit unions and others are able to do.

Jim Marous (36:35):

Warren, stepping back maybe a little bit from Empower and Petal, but looking at your investment background or investor background, I should say, what do you see are the biggest obstacles preventing the open of the eyes innovation needed in lending and payments in the traditional financial institution space?

Warren Hogarth (36:56):

Yeah, I think Colton's has a good one, which is around having more modern infrastructure that can actually handle both the data and the speed, for example, that we need.

Warren Hogarth (37:11):

So, for example, if one of your value propositions is going to be instant, which to me means in let's say under 100 or 200 milliseconds for an answer, you've got to be able to retrieve the full transaction history in your ledger in, call it 10 milliseconds or something, so that you can then process it with the other data and get to an answer. That would be one example.

Warren Hogarth (37:34):

And so, having, as we've looked at different partners out there, partners who can support that kind of processing power, because we didn't design data. This is getting down into the weeds, but the data databases in the past didn't need the ability to extract that much information that fast.

Warren Hogarth (37:53):

Well, if we want to do the machine learning and look at two years of history as an alternate data sort of source on a user, well now, you do have to have a database that can extract data that quickly and analyze it and make decisions, and it can do it on lots of your users concurrently very, very fast.

Warren Hogarth (38:11):

And so, I think the beauty of software as a service and modern infrastructure is what LoanPro and others are doing is that they're providing simple ways for people to get access to that technology.

Warren Hogarth (38:23):

I think the other one is though that the data is still very messy and it's garbage in, garbage out. And unless you have a lot of nuance and understanding to structure that data and to think about how to create the features and build your models out of it, it is challenging.

Warren Hogarth (38:40):

And if you're a credit risk person, you need to stair step your way into the risk if you've got a portfolio today. And so, building the courage and the confidence and the capabilities to do that is hard.

Warren Hogarth (38:52):

And that's why, I mean, again, if you pop up globally, if you look at all of the global neobanks in the world, I don't think I can name a single one that's gone from deposits into credit and done it very successfully.

Warren Hogarth (39:03):

I can think of a number like Nubank and others who have gone from lending into deposits, but lending was the core competency. And so, there is a really important skill set there. The margin for error is low.

Warren Hogarth (39:18):

And that's why if you don't have the staff and the internal capability that does make it challenging from an adoption point of view.

Jim Marous (39:24):

Boy, that's an interest … both the architecture, the legacy architecture, but even the credit first mentality, it's an important differentiation in the marketplace. And when you're looking at … we talked about legacy financial institutions.

Jim Marous (39:41):

As a final question for both you, what would you recommend traditional financial institutions do to move forward in the credit space overall? What's the one thing they got to start with? Where do they start? I'll start with you, Warren.

Warren Hogarth (40:01):

Oh, that's a good question. I would say being open-minded is a good start. And I think there's quite a few FinTechs out there I think that you could talk to that would start and would educate folks around the opportunities or help set up some kind of like pilot or learning.

Warren Hogarth (40:22):

But I think it sort of goes back up to this question of like what is your mandate? Like what do you want to do over the future if you want to expand access, if you want to go back sort of the principles of things?

Warren Hogarth (40:36):

I would again argue that to me, it's the most powerful way that I know to expand access to credit in a fair way, in a way where it'll work with a business model. And so, then you needed to be open-minded and go collect the data.

Jim Marous (40:49):

Yeah, that's good point. Colton, what's your perspective on that?

Colton Pond (40:52):

I love it. Warren, you've had such great insights. I'm just sitting back and like, man, this has been awesome to hear your perspective in both your experience as an investor at Sequoia and over the past seven, eight years at Empower, right? Yeah, it's been awesome.

Colton Pond (41:06):

I would say on my side, I'm going to call out something relatively unique, and we've kind of touched on it, but most of the banks and credit union executives that I'm very close with are in the industry not necessarily to get paid or have the highest title or things like that, but to truly make an impact.

Colton Pond (41:28):

And folks like Warren and the team over at Empower and several other folks that I know, the mission is not to make a lot of money. The mission is how do I increase access to credit and lending.

Colton Pond (41:40):

And I think maintaining that perspective helps make the challenges that a bank or credit union has to go through to be able to get to the point to where we increase availability to credit without increasing credit losses through sources like alternative credit data, modernize our infrastructure, makes it worth it.

Colton Pond (42:00):

And I think, Jim, back to my time, my first job at FinTech was at MX and I built a really close relationship with the late co-founder, CTO, Brandon Dewitt.

Colton Pond (42:11):

For those that don't know Brandon, Brandon had stage four cancer, and I built a close relationship with Brandon, traveled the US with Brandon, visiting banks and credit unions.

Colton Pond (42:19):

And one time I asked him, I was like, "Hey, Brandon, like you have stage four cancer, it's likely terminal." And about two years after I joined MX, he did pass away from cancer. I was like, "Why do you do it? Why don't you like go on a beach somewhere, go hang out and relax, like go spend time with your family in Illinois. Like why do you do it?"

Colton Pond (42:43):

And he told me, he is like, “Hey making an impact is the biggest and most important thing I can do in my life.”

Colton Pond (42:50):

Financial stress is the biggest determinant of some of the hardest things that people go through. The biggest factor of suicide is financial stress. The biggest factor of divorce, financial stress. It takes so many good things out of this life, out of individual people's lives.

Colton Pond (43:08):

And I believe that institutions that embrace that and embrace the true impact that they can have in their huge scope of influence are poised to make a difference and actually change the financial world we have today. It's similar to the way that Warren and the team at Empower are doing today.

Jim Marous (43:28):

Boy, that's a heavy ending to this podcast. As you know, I knew Brandon Dewitt and respect to everything he was at.

Jim Marous (43:37):

But you're right, at the end of the day the difference of financial institutions is going to make, not going to be the interest rate they paid on deposits, but on what are you going to help me in the future if I need your help for my financial future? And nothing's more key and credit is the way to go.

Jim Marous (43:55):

I want to thank both of you for being on the show today. Thank you, Colton, for you and LoanPro sponsoring this series on the Future of Lending. And I think as we go forward, it's going to be very important for financial institutions to look outside their realm of comfort.

Jim Marous (44:12):

Warren, you mentioned in your last answer referencing the partnership with FinTech firms. That's a good starting point because basically you can get off stuck.

Jim Marous (44:21):

There are organizations out there that can make it so you can innovate very aggressively and very proactively in the credit space, but you may not be able to do it in your four walls. And organizations of all sides are finding that FinTech providers are really providing great partnerships and collaboration to build these products of the future.

Jim Marous (44:40):

Again, thank you both for being on the show today. I really appreciate it.

Warren Hogarth (44:44):

It's good fun.

Colton Pond (44:45):

Thank you, Jim.

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Jim Marous (44:45):

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Jim Marous (44:56):

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Jim Marous (45:04):

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