Lenders within both consumer and SMB rely on data from a multitude of third-party sources to make informed credit decisions. Understanding a borrower’s income with a high degree of certainty is critical in terms of the borrower’s ability to repay. Today, we see income verification solutions employed across a variety of different applications, whether leveraged within marketing efforts, typically in the form of targeted prescreen offers, as well as within income solutions, usually through targeted credit line increase offers.
In this episode, Don Chapman, Head of Strategic Partnerships at Powerlytics, whose core focus is partnering with platforms, data providers, and others to leverage data and solutions that deliver value to banks and lenders. Rich and Don analyze a variety of unique credit solutions and take a deeper dive into topics like:
- The increase in reliance of income solutions by both banks and non-traditional lenders
- Recent trends in the market, including which cities are best prepared to take on a recession
- Three critical items lenders look for in data providers
- The value of proactive credit line increases and navigating the CARD Act
- How a nine-digit ZIP Code can accurately estimate a household’s income or a confidence score against the household’s stated income
About Don Chapman
Don Chapman joined Powerlytics in 2020 as Head of Strategic Partnerships. Don is focused on partnering with platforms, data providers, and others to leverage Powerlytics’ data and solutions to deliver value to banks and lenders. Don brings over 20+ years of senior marketing, business development, and sales experience in the financial services, technology, and consumer goods industries. Prior to joining Powerlytics, Don served as Head of Marketing for internet services company Verisign where he was responsible for the company’s global marketing efforts.
Before that, Don spent 18 years with Capital One and American Express where he served in a range of marketing and partner-facing roles, including building and managing co-branded card partnerships with JetBlue, Sony, and Fidelity. Don earned a BS in Marketing from The University of Illinois and an MBA from the Tuck School of Business at Dartmouth College.
Be sure to follow Don and our host Rich on LinkedIn, and for the latest GDS Link updates and news, follow us on Twitter and LinkedIn. You can subscribe to the Lending Link on Apple Podcasts, Spotify, Google Play, or wherever you prefer to listen to your podcasts!
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Rich Alterman 00:22
Welcome to the show everyone. I'm your host Rich Ultraman. And on this episode of the Lending Link, we're sitting down with Don Chapman, head of strategic partnerships at Powerlytics, which provides comprehensive, granular and consistent Consumer and Business Financial Data underpinned by multiple data sources. On this episode, Don and I are going to spend some time talking about the growth in the use of income solutions by banks and lenders, various use cases, trends in the market, and so much more. But first, please head over to GDS links LinkedIn and Twitter pages at GDS link and hit those like and follow buttons. And please be sure to subscribe to the Lending Link on Apple podcasts, Spotify, or wherever you prefer to listen to your podcast. All right, now let's get linked with GDS link. Good afternoon, Don. And welcome.
Don Chapman 01:12
Rich. It's great to be here. Good to see you.
Rich Alterman 01:14
Good to see you too. Where are you joining us from today.
Don Chapman 01:17
So I am in rainy Potomac, Maryland, which is just outside of Washington DC actually, we're a little bit outside the Beltway, but barely,
Rich Alterman 01:24
You're having cold weather up there as well as we are here in Atlanta.
Don Chapman 01:27
We are, we are unfortunately.
Rich Alterman 01:29
It's been, it's been interesting cycles of weather last couple of days. So, alright Don, you've been with Powerlytics since July 2020. Can you share a little bit about your background prior to joining Powerlytics?
Don Chapman 01:40
Yeah, Rich, I spent most of my career in the credit card industry. I spent a total of about 20 years primarily with American Express, but then with Capital One, I've also spent time in marketing leadership roles with companies like VeriSign, at their a large technology company. But you know, most, most of my background, again, involves financial services, credit cards, data has played a key role in my, in my career, which is, which is what threw me to Powerlytics.
Rich Alterman 02:05
Well, I know you and I are both Amex Alumni. There's a lot of those out there.
Don Chapman 02:05
We did not crossover but we are.
Rich Alterman 02:11
Yes, yes. Before I get started on the business side, I always like to start out with a little more personal information. And, you know, I had a chance to chat and understand that you enjoy playing the guitar in line with that, you know, what are some of your favorite bands, I'm actually headed off to see the Eagles this Monday night. I'm excited about that. What are some of your favorite bands and maybe be interesting with one of the hardest songs that you've ever mastered.
Don Chapman 02:36
Thanks for asking that I love the Eagles. But my really the core of what I love is sort of hard edge bluesy rock and roll. So think about bands like Led Zeppelin, which was more of kind of a something I love when I was younger, but then, you know, some of the Southern bands like the Allman Brothers and scattered. And then there's a kind of a late 60s, early 70s band called Free that some people might remember. And then of course, my favorite of all time are the Rolling Stones now, in terms of mastering, I don't think I've really mastered anything, but I love the stuff Keith Richards did between 1968 and 1974. That's kind of the classic era of the Rolling Stones. And so, I tried to play a lot of that stuff, right. And so I've tried to play like Honky Tonk Women. And for years I play and I'd say to myself, the chords are right, but it just sounds wrong. And then about 15 years ago, I was somewhere on the internet, I realized that he was playing that song and almost everything he played in that era in an alternate tuning called Open G, which is what the old blues masters used to do. But once I realized that, learn how to tune it and learn how to play that way. It sounded close enough to the songs and it actually sounded good. So that's what I'm actually most proud of, Rich, I wouldn't say I've mastered anything, but that's probably the high point of my fairly lame guitar career.
Rich Alterman 03:50
Good. Well, you know, when you think about the clients that we serve, tuning would be a key word, right tuning the way they use data and analytics. Let's get down to business. So entities in the consumer and business lending space, as we both know, rely on data from a multitude of third party sources to make informed credit decisions, understanding consumers income for business, their revenue with a high degree of certainty is really important as we think about that consumer or business's ability to repay their loans, right. And we see income solutions employed across a variety of different use cases on the marketing side, targeting a pre screen prospects, who you want to do business with stated income prior to approving an applicant for a loan. And of course, credit line increases when we look at income solutions. I'm certainly at GDS, you know, where we connect to almost 100/3 party data bureaus in the US and about 200 globally. One of the areas of growth we've really seen has been in income verification solutions. The first one out there really been Equifax is The Work Number recently TransUnion and Experian have both rolled out their solutions. And then you have permissioned based solutions that have permissioning obviously being a key thing going on today, we think about Argyle and Pinwheel. And then you have modeled solutions like TransUnion's credit vision income estimator. So when we think about Powerlytics, can you kind of share how it fits into the echo system of income? And revenue verification? And, you know, how do you differ from the other providers? And maybe you can share where you might source some of your information?
Don Chapman 05:30
I'll start with your last question first, which is that our data is all sourced from essentially government data sources, primarily IRS. And as we go through it, we'll talk about the business data, I think a little bit as well. And I'll talk about some of the different sources there. But the income data is all from IRS tax filings. Now, what I'll do is I'll give a quick overview on Powerlytics and what we do and then I'll drill into the Income Solution. So, Powerlytics has built a database about the IRS tax returns of all 100 and 50 million US households and about 33 million for profit businesses. Think of us as a very comprehensive, accurate and granular view into any household or any US business, like GDS Link, we do a lot of work across financial services, but we do most of our works with banks, lenders, credit unions, and they have found the data to be very predictive in driving both marketing and risk outcomes. Now, from an income perspective, what's unique about our solution? I mean, essentially, it is, it is based on the nine digit ZIP code, right? So really all we need. And we can provide an accurate estimate of a households income, or a confidence score against the household stated income, simply based on that nine digit ZIP code. And I think what differentiates us, I'll touch on four things. Number one, our solution is 100%, zero friction. So there's no permission required, everything happens in the background. And that is critical from a lot of the lenders, I've spoken to bricks, it adds cost to the process. And it actually I think, can push away some of the better credits who don't want to take the time or go through the hassle to give permission or provide any kind of provide what they would need to get that permission, we have a, I would say, very close to 100%, coverage, Rich, I mean, really any household with a nine digit ZIP code that's based on an address, not a post office box, we can provide an income estimate or a competence score. Some of the income solutions are based just on payroll data, and W-2, we have both the W-2 as well as things like wealth income, retirement income, schedule C income. And you mentioned model, we are not a model solution. We are based on actual tax data. And in our discussions with the regulators, they seem to like that better.
Rich Alterman 07:41
So when you say you're not modeled, we mentioned TransUnion before, right? Their vision, credit vision income estimator, it's an estimator, but you're really using a zip, to look at where I live and really saying, well, if my neighbors make X, and I live near these neighbors, and I probably may make that same amount or near that same amount. So from that standpoint, would you not say it is somewhat modeled?
Don Chapman 08:07
It depends on your definition of model, but essentially a statistical distributions on that nine digit ZIP code, right? So there's no modeling, there's no taking, you know, someone's demographics, or what kind of car they buy, what kind of house they live in, and then kind of estimating or modeling an income based on that. So it's different than what I think we typically call a model solution. It really is based on that actual raw tax data.
Rich Alterman 08:31
Now, you say your solution is based on a nine digit zip. And it's funny, I was thinking, the last time I keyed in my nine digit ZIP, I don't think I ever have. I have it right here. I have it right here on a bill. Certainly from a marketing perspective, it's something you've been around for a long time. What is the difference between, you know, really help people understand the difference between a nine digit zip, and the regular zip code that most of us we all know, our five digit zip and I suspect most people would have to struggle to remember what their, their nine digit zip is?
Don Chapman 09:00
Yeah, I didn't know mine until I went to work for Powerlytics, but the real difference is in granularity, right? So a five digit ZIP Code, there's about 40,000 of those. So if you do the math, that's about 3700 households, not very granular a nine digit ZIP code. There are 40 million in the US, that's about three to four households on average. So that is a very granular, and very small geographic area. It's really you and a few neighbors.
Rich Alterman 09:25
So if a lender were to use your service, in addition to sending you the nine digit ZIP, what would be some of the other variables that you would ask for them to send and what would you do with those?
Don Chapman 09:36
Yeah, the only thing we need definitively is that nine digit ZIP code, but if they have an own or red flag, they can send us that actually helps improve the accuracy as well. Right. And so, you know, one of the things we found early on, as we did some verified income tests in areas like Miami Dade County in New York City, really densely populated areas, where people are living in large apartment buildings or condo complexes, is that owners and renters living next to each other tend to have a pretty different economic profile, the owner will have higher income. And so we essentially developed what I would call, I'll use that word tuning again, a tuned estimate based on home ownership or, or the fact that someone is a renter, right. And so if you don't know that, we give you a blended or combined estimate, but if you can flag to us that you know, the person's owner or renter, we will give you a tuned or segmented estimate based on that specific status. And that, again, takes what's already accurate and makes it even more accurate.
Rich Alterman 10:35
Does your system deal, or how does it deal with two income households where there's a, let's say, a husband and a wife? And the wife is applying and she makes a lot more than the husband? How would that potentially be picked up? Or you know, how do you deal with that?
Don Chapman 10:50
Yeah, great, great question. One of the other income estimates we can provide as what we call as an adult average and adult means, so think about a household that's married filing jointly versus single, we essentially can look at a specific household, or house like is it for and look and say, well, there's four households, but they're actually six adults. And then we can actually divide that by number of adults so that sometimes, we have some clients that actually like that adult average, rich for the very reason that you you just brought up.
Rich Alterman 11:22
So you know, I kind of understand with a home. But let's talk about apartment complexes. I mean, yeah, just the number of apartment complexes popping up around where I live is incredible. And less and less people they say, will be able to afford a home. So how does it play out in an apartment complex as far as accuracy compared to homeownership?
Don Chapman 11:43
I think one of the things that helps is the own rent designation, if we know that we can really kind of nuance an apartment building based on the owners and renters. You know, the other thing is, we always get asked this by clients or you know, folks who are considering our services, when they think about an apartment building, they think that's one zip for typically, there are multiple nine digit ZIP codes or zip fours within that apartment complex. So to the point where typically a floor will be its own nine digit ZIP code in many cases. And so that helps kind of get that granularity even deeper.
Rich Alterman 12:13
So if a client I assume some of your clients will send you income, stated income, yeah, as part of the data points, can you share any statistics or insights on how the stated income kind of compares to the zip plus four income that you're pulling from tax returns? And whether you use any type of analytics machine learning to incorporate into your offering in that regard?
Don Chapman 12:40
Well, I mean, the stated income is what it is, right? I mean, sometimes people are telling the truth, sometimes they're they're fudging it or they're under. So we provide a competence score, as well as kind of the income that says, you know, between one and 10, you know, it's really versus maybe verified income, where we've done a bunch of blind tests that look at our income estimates versus verified income. And you know, the key thing most of our, our customers care about most of the lenders we talked to is overstatement. And what we find is that in 80% of the cases, we are no more than 20% overstated and that that level of risk tolerance has gotten most of our clients and many lenders very comfortable with what we offer.
Rich Alterman 13:20
You mentioned that you have information on 33 million for profit businesses. So once again, getting back into this zip plus four, you know, I can understand how there may be a comparison to me and my neighbors. If I think about driving down the road where I, you know, where I live, we have a couple of Mexican restaurants we have a dry cleaner, we have Petco, we have Kroger, how does zip plus four kind of help from a business standpoint it's, it's not intuitive to me,
Don Chapman 13:50
Actually, that's a great lead into our business data, which is a little bit different and not based on zip four. So from a business data perspective, it's underpinned by DAC tax data. We also have some Department of Labor data and Census Bureau data that we combined to create the business data, essentially the way we productize the data the way most of our clients use it as we turn it into a complete set of financial statements. So any business that's a corporation or partnership, we have an income statement and balance sheet about 35 financial ratios sole proprietors, we only have the income statement, they don't have to file a balance sheet with the IRS in a smaller set of ratios. Now, your question about zip for our match keys are a bit different on the business side, it starts with a five digit ZIP Code nine digit ZIP code is just too granular for businesses. I mean, there's not going to be businesses in most nine digit ZIP codes right? Then we use the next classification system. from an industry perspective. We can also take NASIC and crosswalk that to NAICS. We use size as a third sort of match key either revenue or number of employees. And then the fourth would be legal form is it a corporation partnership or sole proprietor? So if you wanted to take that Mexican restaurant and get an estimate of their revenue, you know, typically in you were a lender, what you would send us a zip code, you would send us, you know, they're quick service restaurants and you'd send us that NAICS code. And then if you knew how many employees they had, you'd send that to us as well, based on that we could give you an accurate estimate of their revenue.
Rich Alterman 15:16
Now, from an application standpoint, on the consumer side, we were kind of laughing a little earlier about knowing your nine digit ZIP, I suspect, well, I know from from our business that a large percent of consumers do not enter their nine digit ZIP, right, because once again, it's not top of mind. Maybe a plug for some companies, but are there any companies that you guys work with, that have the ability to, you know, append that nine digit zip as part of the overall process flow.
Don Chapman 15:46
There are two that we've worked with in our place of work with one is a company called Service Objects. The other is a company called Smarty. They used to be called Smarty Streets. Essentially, they're both very low cost. They both are very good services, you call their API, you send them an address, and they pretty instantly send you back that nine digit ZIP code. So it's a really easy process.
Rich Alterman 16:05
Now, do you guys support the ability to do retros studies?
Don Chapman 16:08
We do? Absolutely. We do this all the time.
Rich Alterman 16:11
And how far back can you go?
Don Chapman 16:12
Well, we actually evaded going all the way back to 2004. I mean, in not too many of those retros, that the folks asked to go back that far. Right.
Rich Alterman 16:19
It might be, you know, they might have records to like 2016 they have us look at but yeah, we're constantly doing retro analysis. So we're talking about going back, what about going forward? How current is the data?
Don Chapman 16:31
So tax data is filed annually, right. And then I guess, thankfully, it's filed annually, or else we've none of us would be very happy. We update the data quarterly, right as tax returns flow in. And so you know, the most recent year we have right now is 2021. We'll have 2022 data fairly early in 2023.
Rich Alterman 16:51
Okay, so 2021 data, we're sitting in a time of large inflationary pressures, layoffs.
Don Chapman 16:59
Rich Alterman 17:00
So would it be fair to have any concerns about the predictability at this point? Or any insights on that?
Don Chapman 17:08
I guess what I would say is no data solution is perfect, right. But, you know, we got these questions all the time. I mean, I joined in the middle of a pandemic, right, and that there's never probably been a greater economic whipsaw, from great economy, to terrible economy to really good economy can in the course of I don't know, what was it like nine months and no one stock market going crazy people losing their jobs and getting money from the government. We talked to a lot of our clients during that time, and they really didn't see any impact on the accuracy of our data, or the efficacy of, you know, the Income Solution during that time. So, yeah I think a lot of our clients gotten more comfortable with the data during that time, which was after a number of fairly smooth years, or real kind of rocking period.
Rich Alterman 17:53
So we mentioned at the beginning, that different use cases, application processing, credit limit increase programs, so let's kind of you know, break those down, let's kind of start with credit limit increases. And like me, I'm sure that a lot of people in the audience today are aware that when they visited their, their bank, online banking, or their credit card, a thing pops up and says, you know, what's, what's your current income, are you willing to give us the updated income, let's talk about where that came from, and how that may be being used and how your platform kind of plays into that same space.
Don Chapman 18:27
Yeah. And so having spent a number of years in the current industry, being able to get a competitive line, increase those credit lines is a great way to increase profitable balances, the average American has three to five cards in their wallet. So you've got to have a competitive line or your cards gonna fall out of the wallet. So proactive credit line increases have always been part of what card issuers do. The CARD Act actually changed the game a little bit there, I think that was in 2009. Now, you've got to be able to understand someone's ability to pay before you can do a credit line increase, and part of understanding that is getting their income. Now, when they apply for a credit card, they give income, right. So for the first 12 months, you're good. But after that, the regulator's have said, we want to refresh income on an individual after 12 months. That's why you get that pop up Rich, or you'll call customer service, they'll ask the same question. That kind of works. But I can tell you, at least from my experience that has a pretty low response rate. People just are kind of unnerved. They don't respond to things like that they're too busy. And so our data can be used as a replacement for stated income and making that ability to pay calculation. We have a very large credit card is suer who's been using it for that purpose very successfully for a couple of years.
Rich Alterman 19:39
Have you met with any of the regulators to kind of validate that your data is acceptable?
Don Chapman 19:46
Yeah. Some of our leadership did meet with with the OCC. They went through the data solution in detail and walked away with clear usage guidance on how the data could be used. And so really the key use cases are the credit line increases you talk through, top of funnel targeting pre screen type use cases. And then in loan decisioning, one thing I didn't say is we are not FCRA, right? So we can use at the Yes, with less friction, we cannot be used to decline someone for a loan, we cannot be, you know, be used for an adverse action. But those are the three use cases. And generally speaking, unsecured loans, credit cards, auto loans, those types of things are acceptable loan types where our data, you know, where the OCC has gotten comfortable with our data.
Rich Alterman 20:33
One of those opportunities I've seen with our customers using data like yours, as you mentioned, is kind of like building a comparison between the stated income and your true income product. And if it's greater than "x" percent spread, then you want to start introducing some other variables or tools, like The Work Number, and maybe combine that with a Vantagescore, and say, hey, if it's a really high Vantagescore, and the income spread is "x", I'm going to do one thing, but if it's a low FICO score, and the income is spread the same. So there was a report recently put up by Lending Club that talks about the number of people that are living paycheck to paycheck, and not only low income earners, but even high income earners are feeling the strain. They said that I think of those earning more than six figures 45% reported living paycheck to paycheck, which is incredible to think about with all your income data power Linux has, are there any interesting trends that you'd like to share with the audience today?
Don Chapman 21:37
Yeah, Rich. You know, one area that I was curious about was how different geographic areas fared economically through the pandemic. So what I do is I dug into our data, you know, I looked at income growth between 2019 and 2021. And I specifically looked at which MSA is had the highest and lowest income growth during those years, which kind of the pandemic fell right in the middle of, and the top five markets in order in terms of highest income growth were Nashville, San Jose, Pittsburgh, Raleigh and Cleveland. You know, then I did a little research trying to understand why some of those markets might have seen that income growth and you know, with Nashville, which had over the two years, not per year, but over the two years, growth of over 5.9%. I found a couple of things. Number one, this is a market that has just been growing very rapidly and seeing a really vibrant economy between 2010 and 2020. Nashville far outstrip the US in terms of population growth, GDP growth and job and job growth. And I've got to believe there's a strong correlation between that kind of dynamic growth and just general income growth. There's probably a pretty general strong income growth trend there. And then in addition to that Nashville obviously has a huge tourism market. And they certainly got whacked when the pandemic hit with, you know, from a tourism perspective. But that came back very quickly. It certainly came back I think, almost fully in 2021. So that was a little bit of what I found about Nashville, when I looked at San Jose and Raleigh, that was actually no surprise to me. Those are huge tech hubs. You know, Tech has boomed over the last couple of years. And that's created, you know, job growth, a very tight labor market, which of course, has driven up wages. Pittsburgh and Cleveland, you know, that one might be a surprise to some people that people think of those as sort of older, slower growth cities. But actually, you know, Pittsburgh has got a pretty big tech base, and they had a significant growth in tech workers. And then Cleveland actually saw a pretty big growth in tourism. So those are potentially some of the reasons why income may have grown in those markets. At the bottom, and there were four markets where income declined, Miami, which saw the biggest decline of about 2.3% over those two years. And then also Houston, Las Vegas, and Atlanta declined a little bit in New York was the fifth from the bottom that grew slightly, without diving into all of those, you know, Miami and Las Vegas certainly rely heavily on international tourism. And even when tourism came back with in the US domestic tourism, that international tourism given different restrictions and lockdowns that certainly has not come back fully through 2021 So that's probably part of the reason why those markets fell toward the bottom so you know, hopefully, we don't go into a recession but if we do this may give some insights into which of these markets will be best positioned to weather that storm.
Rich Alterman 23:31
Any product roadmap that you'd like to share with us?
Don Chapman 23:35
One of the key innovations over the last couple of years, we have come out with a database of investable assets of all households, right? So we use tax data to underpin that we can provide a very accurate estimate of really three things. Number one, what is the households interest bearing assets look like? Right? That would be deposit CDs, money market funds, bonds, bond funds. So for a bank looking to capture the deposits, that's a really important metric, we then have the equity related assets that are non retirement, you roll that all together into sort of a liquid household investable assets. And then on top of that, we have the retirement assets. And then we can combine all of that essentially into a net worth for a household of everything, but their home value. So that's one key innovation. I think I can't really reveal it right now. But there's some interesting things we're going to be doing in the real estate space with our data around home foreclosures next year that you'll see in the marketplace as well.
Rich Alterman 24:33
Have you developed any trending reports yet, where I can get visibility, kind of like we think about having credit bureaus have come out with trended data where I can get some trended type visibility?
Don Chapman 24:44
In our core data, we offer trended data on a number of our variables. So we have trended income data. How was it change versus a year prior three years prior five years prior? If that's what you're asking, right?
Rich Alterman 24:55
Don Chapman 24:56
That's, that's something that we've offered for a long time.
Rich Alterman 24:59
We catch up from a tax return perspective and see what the economy is doing, and 2022 and then 2023, and we start looking back, that's where we're going to probably start seeing the effects of layoffs and things like that, which once again, during the pandemic was kind of shadowed, or covered up by all the injection of money from the government.
Don Chapman 25:16
It was, it was it was, yeah, well, I think the power of the trended data is people feel and behave based on change, right. So if I make "x", if that's lower than it was a year, prior three years, prior five years prior, I might behave differently than if it's flat. Right? If it's the set, right. So I mean, I think that's why we that we tend to find that to be a very powerful metric, even when we, when we work with lenders to build a predictive model on who might respond to an offer, who might default on a loan who might lapsed on an insurance policy, that metric we often find to be pops in those models as being very predictive.
Rich Alterman 25:53
One more question before we wrap up. And it's kind of an uncommon when I ask our, our podcast guests, and that's, if you were talking to rising college senior, and giving some career advice, based on your background and your experience? Where would you try to take them?
Don Chapman 26:10
You know, I would say, maintain your relationships and maintain your network, right? I mean, there's just, I mean, I think I've done a pretty good job of it. But there are people from like, I won't even say how many years ago, but a long time ago, that I feel like I should have just maintained a better relationship with and I think there's so much value in that, that I mean, I've got, personally, I have a son as a senior in college and other music, who's a sophomore. And I have a daughter who's a senior in high school, I tell them all the time, relationships are so critical, maintain those relationships, don't let them slip. It just gives you so much leverage in whatever you're trying to do. Hey, it's fun to have friends as well as connect with people.
Rich Alterman 26:50
Yeah, at the end of the day, sometimes it's who you know, as much as what you know. And not that you're going to help them get a job, but sometimes you can help them get that foot in the door. We work we live in a very competitive landscape.
This is Rich Alterman and we've been syncing up with Don Chapman, head of strategic partnerships with Powerlytics. We hope you've enjoyed this podcast and will stay connected with GDS Link, the Lending Link to listen the future podcast and catch up on ones you may have missed? Thank you make it a great day.
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