Intro  0:04 

You're syncing up and tuning in to The Lending Link podcast, powered by GDS link. Where the modern day lender can dive deeper into the future of data decisioning and Credit Risk Solutions

Matt Tepper  0:20 

Hello, and welcome everyone to the very first episode of the lending link. I am Matt Tepper Global Marketing Director here at GDS Link and we're thrilled to be introducing our fresh new industry podcast designed for the modern day lender. On this episode, I'm joined by a special guests, the man of the hour and the host of the Lending Link Podcast rich ultimate. Rich has a deep rooted history in credit risk management within consumer lending, where he boasts over 40 years of helping the modern day lender stay competitive in our ever changing environment. We'll certainly be diving more into Rich's career and expertise within this episode, but first, head over to GDS Link's LinkedIn and Twitter pages at GDS Link and hit those like and follow buttons. Be sure to subscribe to the Lending Link on Apple podcasts, Spotify or wherever you prefer to listen to your podcasts. All right, now let's get synced with GDS Link. Welcome, Rich. How have you been? Where are you? Joining us from today?

Rich Alterman  1:22 

Hey, Matt, happy to be here. Yeah, I'm joining you from my home office in Alpharetta, Georgia. 40 miles do north of the Atlanta airport? Excited to be officially launching the Lending Link Podcast?

Matt Tepper  1:34 

Yeah, absolutely. I'm located here in Dallas, as well as where the GDS Link headquarters are. This podcast has been in the works for a little while now. And it's finally nice to get this effort off the ground. I gave a little bit of a brief introduction over your 40 years of experience. And we're definitely looking to touch on that throughout this episode. But figured I'd just kind of open the floor up to you kind of lets you start delving into how you got started within finance and more specifically within credit risk.

Rich Alterman  2:01 

Thanks, Matt. Yeah, it's been a it's been a long journey. Actually, this July 19, I celebrated my 40 years in the industry started July 19 1982 at Citicorp retail services provider of private label credit cards had graduated from Boston University, and was fortunate to land a job was a different cold time in 1982, with a pretty tough job market. So I was thrilled to land a job was such a prestigious organization, started my career in a management trainee program working for the risk department. And that was really my first exposure to how analytics would be used in helping make decision, be it credit or collections account monitoring. After three months there, I actually stepped into a role as a collection agency auditor 22 year old flying around the whole United States knocking on the doors of large collection agencies and saying surprise I'm here. From there, I would spend time evaluating how those agencies were doing work for our portfolio, and kind of came out of my shell then being forced to sit down with the managers of the different offices and tell them that what they were doing right, what they were doing wrong, and forming relationships with these gentlemen and women across the country. But it was definitely a neat experience for someone my age to sometimes be in three cities. In a day. We did work with several women apparel shops, charming shops, learners, jewelry company, Tiffany that many of you might be familiar with. And my role there was to sit down with the managers on a monthly basis and review how they had done against the budget and forecast. And that really helped me dig a lot deeper into how the different processes within credit organization work. Before I moved on from Citicorp, I spent some time in a department that was responsible for actually bidding on companies portfolios to offer the Citicorp service. And within that I did a lot of work where I would do bottoms up analyses on what it would cost to make a collection call what would it cost to process a payment? What would it cost to process a credit application. So a lot of time really sitting down and digging into the details of the various processes and understanding the cost economics. From there, I was happy to get a call one day from a headhunter. They'll remember his name Bob Mirabilia. It's amazing how things are picking your brain and offered me the opportunity to go for an interview with the Bank of Boston. I graduated from Boston University, as I might have mentioned in 1982 and love the city. So it was really exciting to get an opportunity to go up to Boston and interview for a job. And when I interviewed for the job, I was interviewing for a job to run the collection department, security plastics, and in house recovery. I remember saying to my boss at the time, John, Hey, John, you know, I've never run these departments. I never ran collections. I've never run security, fraud plastics in my prior long-term tenure three years and he's said no problem rich. He said you're coming from Citicorp. And when you come from Citicorp, that's like having your masters. And I quickly learned that that was the case, I was amazed on how much I had absorbed while working at the bank. But now I had the opportunity to apply those different learnings that I brought with me and was successful and turning around was some pretty difficult portfolios that we had booked, and really turned the collection operation around brought in the first predictive dialer, into Bank of Boston set up incentive programs. That was in house recovery department. And when I look back on my time at Bank of Boston, it certainly some of my fond memories of the things that my team and I were able to accomplish in the three years that I was there, from there got a call one day to interview for a job with American Express, Centurion Bank. So I kind of stepped out of the collections operation and spent some time managing the budgeting process and forecasting, quality assurance, I ran a business requirements group even responsible for the mailroom and HVAC, though got exposed to a lot of areas outside of the actual credit and collections operation, which really allowed me to start rounding out my background and skills. So that was a fortunate opportunity. And from there move back into collections before moving on from American Express in 1992.

Matt Tepper  6:24 

It seems like, even at the earliest start of your career, you were certainly kind of accumulated a wealth of experience in a lot of different environments and positions. I can only imagine a Boston Terrier now jet setting around the world helping Tiffany's and sure you probably have some stories for folks, obviously, this, there's an audio medium, which is a pretty tall guy. So I'm just kind of curious, what's your what's your best collection story had a rough anybody up or anything like that you certainly have the frame to hold your own.

Rich Alterman  6:51 

I spent very little time doing collections myself. And it was certainly part of our training. But what was funny is the way these collection agencies work, they would try to plan out where they thought you were going to go next, because the collection agencies had offices all over the country. So if I had flown into Chicago to go meet with a particular company, that company manager had a responsibility to call the corporate office and say, Hey, Alterman is in town, he may be going to this place next, and they would try to predict where you would go next. I think they probably even kept track of it. So in the future, they can say, hey, after Rich flies into Chicago, he's going to fly into Ohio. So I planned in Ohio, knock on that door at nine o'clock in the morning. And first thing they bring me that collection tray. And guess what every single account in that collection tray had been reviewed yesterday by a supervisor. Now how did that happen? I actually say guys, like, you know, at least you could change up the date. Like don't do it yesterday, when you know that I was in the office the day before.

Matt Tepper  7:52 

It's  funny to hear that the term Alterman is in town still command of the same sort of respect years ago as it does today. I know whenever that same sort of memo comes down the GDS office, everyone is making sure to fly right a little bit more. So obviously, we've kind of talked a little bit about your experience here stateside, I know you also have a wealth of experience internationally. Do you want to kind of touch on that a little bit?

Rich Alterman  8:13 

So my boss was talking me about this bank in South Africa, well, First National Bank of South Africa. And whether or not I'd be interested in rejoining the company, Computer Science Corp, which had acquired the Hogan Banking software platform, and basically hop on a plane and fly down to South Africa to pick off this project and try to actually win the business. So sat down with my wife did a little soul searching, do I start my own thing? Or do I go back into the corporate world? And the opportunity to spend time in a foreign country was interesting. It wasn't anything I had done in the past. So decided, in fact, my first trip to South Africa was January 1998. I can remember it like it was yesterday. And we had a lot of expats that live down there, we had a relationship with several banks. So really started off a project, I was told I was only going to go to South Africa a couple of times, I would assemble a team back in Dallas that would actually build out the project for this event driven risk management platform. And it's interesting that that project, which started in 1998, Incorporated platforms from some of the vendors that we work with today, including Experian, and TransUnion, and FICO. Were all involved with this project back in 1998. And so anyway, spent time going down there, and once again, I thought this would be something temporary. But at the end of the day, I ended up spending two and a half years, traveling back and forth to South Africa, 38 trips spent 370 nights in the same hotel, which was a fantastic experience being treated somewhat like a king. So started out with that simple project, but then led into taking on account management response civilities for three of the largest banks in South Africa. And you know, it was a great way to learn about some new culture, met some wonderful people that I'm still friendly with today, and have been back there a few times, hoping to get back there again. GDS has several clients now in South Africa. So looking for that to become a opportunity for me to hop back on a plane and fly down there.

Matt Tepper  10:24 

And part of the reason that we were looking to really get this podcast off the ground is one thing that we've heard from not only our clients, but even from some of our competitors as well, is that just the wealth of experience that GDS Link is bringing to the table. We've been for over 16 years, we've been helping hundreds of lending institutions and organizations across the globe, achieving higher growth offering market leading risk management workflow automation solutions that encompass the entire credit lifecycle. And I think Rich is just a little bit of the tip of the iceberg when it comes to some of the expertise that GDS yields and wields in the in the marketplace. Can you maybe provide a little bit more color and context for folks that might not be as familiar with GDS link as to some of the type of solutions that we provide? And then maybe kind of give a little bit of a story as to how you came about joining GDS? Sure. Thanks,

Rich Alterman  11:14 

Matt. Good question. When you start a technology company, which GDS started back in 2006, by Paul Greenwood and Yves Duhoux, having just come over from experience, I think you make a decision. Are you simply looking to provide the technology? Or are you looking to really sell a solution? And at GDS Link, we like to focus that we're solving problems, we're bringing a solution to the table. So we really have, you know, brought in several people, including myself, that have a lot of experience in the industry, and really kind of act as the local, if you will subject matter experts, that the sales team, the account management team, the delivery team can pull from our experience when they need to. So you know, we really try to identify what are the problems that our clients or prospects, more importantly, at the start, are really trying to solve and really try to take more of a solution selling approach versus just the technology approach. So GDS today, and we certainly have gone through a product evolution. When we started, we called our Product Data View 360, we now have dubbed it Modellica. And when we think about how to best describe what GDS does, and certainly I interfaced with a lot of people not in the industry. And you know, you run into the person at a cocktail party, and they and certainly, personally many asked each other is, you know, what do you do for work? And if I'm trying to describe it at the highest level, what we do, I'll say to someone, have you ever filled out a online credit card application or mortgage application, or auto loan application? And of course, the answer nowadays is typically Yes, I filled out an online application, I said, Well, you know, when you hit that submit button, we're the technology behind the submit. So what our software brings to the table is the ability for small business lenders or consumer lenders be it secured lending or unsecured lending, to really try to maximize the use of the multitude of data sources that are available out in the market today, to leverage that information to bring it into a digestible, easy to understand format, and then to apply intelligent decisions to each of those data points, if you will. And then if that application is going to be approved, based on that lenders credit criteria, making the best decision about what that loan amount should be what that term should be what that rate should be. Because just because you you know, approve a consumer doesn't necessarily mean that the other consumer you're going to approve necessarily qualifies for exactly the same loan. So that ability to introduce risk based pricing, and to really make sure you're trying to optimize the profitability that any one applicant will bring to your franchise is critical. When we first started our company, we were really focused on data access, aggregation, and automated decisioning and really didn't get too involved with the manual underwriting process. But back in around 2014, I think it was we were starting to get involved with what has been dubbed marketplace lending, when you think about companies like best AIG prosper Lending Club, and there was a need from the market to also support manual underwriting. So we developed the product we dubbed it case center, which is really a workflow system that allows a manual underwriter to work accounts that require typically some type of manual verification steps or stipulations, so maybe it's verifying their income, maybe it's verifying their place of employment. So if you think about Rules Engines, obviously the goal there is to automate as much as possible in the credit card world There's typically objective that you want to automate, maybe as much as 90%, if not 100% of your applications, but certainly some number are going to require maybe some human touch. But in the personal loan space, when you're doing a loan of $50,000, unsecured, you probably want to have some human touch there, if you will, to make sure you're comfortable with this person, that's going to be getting a ACH for 50 grand, that's unsecured. So very often that you might deny an application fully system, the automated, but from an approval standpoint, you might want to run them through some manual steps. So that's what our case center does. And it's not a one and done. So there could be situations where potentially, a consumer has stated that on their application that they, you know, make $100,000 a year, but you're only able to verify, let's say 85,000, so that consumer has been approved. But because of that income of $85,000, you want to rerun that application back through your system. And maybe you're going to give them a lower loan amount or a different term or a different rate because of that income variance. So our system supports that resubmit, if you will, where in those cases, you're not going to repol, the credit reports, but you just want to apply some decisioning around what type of loan amount. Now, one of the real areas of expansion has been in data bureaus that are available to the market. And how do you really know that the person that filling out that application is who they say they are. So there's been an explosion of explosive growth around the different data bureaus that support ID type solutions, device ID solutions, behavioral solutions. So not just using credit data, but also looking at other data sources that can help mitigate fraud, whether it be first party third party synthetic, which has certainly been a growing areas, certainly post COVID. So one of the value propositions that GDS brings to the table is that we have these pre built connectors. In the US, it's probably about 100 connectors today, probably about 200, globally. And we're currently working on our new initiative, which we have dubbed the data exchange, where the ultimate goal is that our clients will be able to self serve. So they want to take advantage of a certain data bureau, that's part of our exchange, and they've been credentialed, they'll actually be able to go into that exchange, or that marketplace, if you will, and pull those connectors themselves into the solution, and then add them into their overall credit flow. One of the new things that we introduced a couple of years ago, was to start getting into the analytics world, we hired some senior people, Carl Spilker, who has years and years of experience has been around for a long time, it's worked for many lenders. And he joined us to head up our analytics group and has a great team. And, you know, we're really excited about having the opportunity now to work with multiple lenders, where we're building out custom scorecard models that you know, leverage data that we get from the bureaus, maybe lenders have it in file, or we have to do what's called a retrospective analysis, but really looking at that lenders performance data, what type of data they accumulated during the actual application process, and evaluating that data and seeing if we can bring improvements to their bad rates, and their approval rate. And of course, today, you have the advent of machine learning. We have a team with Florian and others that work for Carl, where we're doing things like gradient boost random forest. And those become really critical, given the sheer amount of data that's available today. So with all these different data sources, and you want to accumulate these pieces of information, you need a pretty powerful engine that can quickly identify patterns and value that's coming from this data. So Florian's team has to use these different techniques to try to build the best model and optimize what data sources should be used in these models. Our system is really more about the enabling technology, if you will, for implementing those models. So whether that model is built in Python, built in our traditional linear logistic regression models, or even needing to support PMML, our platform is able to quickly allow our end user client to go in and bring that that either that code in if it's Python, or our, or go into a simple wizard, where they can enter information around a traditional scorecard where I have maybe 15 attributes with various points and values.

Matt Tepper  19:37 

So you'd say that things have kind of changed a little bit since you first got your start over 40 years ago?

Rich Alterman  19:38 

It seems like this fundamentally evolving landscape for your core aspects of underwriting have really not changed. When you think about what are you trying to decide. I'm taking an application via the business or consumer and I'm trying to decide whether or not they're going to pay me back. And whether or not they're going to be profitable. So that really hasn't changed over a long, long time. What is different is really that amount of information that's available. The channels in which that consumer is coming to me, once again, more online than ever, I think they say some of the young younger generations may never step foot into a bank branch. And everything will be done online over a mobile device, maybe one day, we'll just think about it, and it will happen. So the fundamentals are still the same. It's just the different ways that we interact with the consumer, which has added a level of risk from a fraud perspective, companies like TransUnion, IOvation will let you know if this iPhone that I'm holding in my hand, potentially has been involved with fraud, or the laptop that I'm working on. So information not only about me, the consumer, but the device that I'm interacting with. Company we work with called NeuroID, we'll place a JavaScript in the actual application payload, and can detect behavioral signals, that could indicate that I might be a fraudster filling out that application. They rely on one's long term memory, when I'm entering my Social Security, my address, my name, my date of birth, those things should flow off my fingers quite easily. If I'm a fraud ring, and I'm looking at a Excel sheet with 1000 rows of consumers, and I'm having to keep looking back and forth while I'm filling out that application, I'm not relying on my long term memory. So neural ID can detect those signals or patterns and help identify potential fraud. And most recently, and maybe not yesterday, but over the last year or two, we've really seen an explosive growth in the area of using bank data. The industry refers to it as open banking. And there's many, many companies now supporting acting as aggregators, and offering up that ability for a consumer to permission themselves, to let that lender pull in their bank information, be it from Wells Fargo, or Chase or Bank of America. And that's something that, you know, we wouldn't have thought of years ago. And on top of that, we can, we can also help build models. So once again, working with a lender or getting their performance data, and then using that bank information, to see what type of lift we can give them in, in model building. And now, combining that with data from the traditional bureaus. COVID had a big impact on data reporting with the forbearance and deferment. And with the stimulus coming in from the government, a lot of the behavior of consumers from a credit perspective was changed up a little bit from a credit reporting perspective. So the joining of open banking data, along with credit bureau data really gives a powerful lift to lenders. And also one of the key things that it allows him to do is help around the area of financial inclusion. CFPB is very much focused on you know, how do we bring these unbanked underbanked consumers into the the banking environment, and you know, these consumers may have a bank account, but maybe they have a thin file or maybe no file at the credit bureau. And certainly, that bank account data has been helpful in expanding the reach down into the population that may not be as easily identified or assessed the traditional credit report.

Matt Tepper  23:23 

It's interesting to hear you talk about how necessarily the solutions haven't necessarily changed over time. But more than just the landscape, especially with this new wealth of information, this new customer information that's now at our disposal, and is just increasing exponentially day by day has just kind of started to create these new challenges for lenders. So yeah, while the lending landscape continues to evolve at a rapid pace, and the introduction of these new customer touch points, as well as new offerings, has kind of helped create new hurdles. GDS Link is committed to helping our clients solve these problems ultimately, make quicker, better decisions, and be that subject matter expert to help unlock new opportunities, and ultimately exponential growth. I'd always speak for Rich when I say that we're hoping this podcast provides a little bit of a peek behind the curtain as to really what some of the intricacies of these new hurdles and problems are in this ever evolving landscape and how GDS specifically helps address and solve these problems. We're hoping to provide these insights through conversations like this really kind of tapping into the wealth of information that Rich has at his disposal, but also speak to some of our partners, as well as some of the clients that we've helped address some of these issues, and they can kind of speak firsthand as to the lift that GDS has provided, as well as industry leaders that are out in the marketplace that we routinely lean on just to provide a little bit of nuance and new additional insights. On that note, if you would like to be a part of the lending League, you can reach us at thelendinglink@GDS link.com That's the lending link all one word, thelendinglink@GDS link.com If you'd like to be a part of any of these conversations moving forward, I guess is we kind of put a little bit of a bow on this, the introductory episode rich, maybe just a little bit of a quick hit, obviously, I'm aware that you were in Chicago at Lend 360 as part of a speaking panel, really trying to kind of speak to the idea of how flexible and open decisioning platforms can really help drive new value for banks and fintech, maybe just off the top of your head. What do you think is probably one of the more pressing issues and challenges that lenders might be facing today? Obviously, I know that we've just had an inflation report come out, that seems to be pretty top of mind, would that be maybe the first thing that kind of comes to mind or what whereas I think that the, you

Rich Alterman  25:43 

know, one of the key things we touched on in the session was really about collections and recovery. During great economic times, it's a lot easier to book accounts, make origination decisions, when you're more competent, that that consumer is going to pay, but all of a sudden, now you're looking down a road where we know inflation is up, we know there are layoffs coming and certainly have happened. So you know, a lot of lenders will find that they're going to have to start putting more focus and attention potentially, into their collection processes. And what we talked about at the one thing we talked about at this at the seminar was how analytics can play, and decisioning platforms can play a role in collections. So when you think about collections software, you have accounts that are a certain level of delinquency, a certain a certain cycle during the month, and you want to potentially take an action on an account today, there's a lot more options on what you do with an account from a touch point perspective, do you send a letter? Do you send a text do you make a phone call, you have junior and senior collectors, senior collectors tend to be more experienced. Typically, in a flow, you'll have a 30 day account that will go with a junior collector, and it's more of a customer service call. We also have staffing shortages. Now it's harder to find people. So you know, you have a certain number of accounts that you want to be worked by any one collector. And if you're having trouble staffing, you might find that you really have to do a better job of deciding which accounts to work and when to work them. So analytics can take a role in developing behavioral scores, collectability models, propensity pay models, bringing in your own internal data, looking at historic patterns, bringing in economic data maybe, and also pulling in trigger files, or points from a data bureau like a collection score or recovery score. And combining all that information, recognizing the fact that if you have five delinquent accounts and 60 days delinquent, that owe you $1,000, they're not all the same. And if you can only make one call today, and I asked you which one to call, well, in absence of any other information, it would be a flip of the coin, which of those five accounts you should call. But I can give you some intelligence around what those five accounts actually look like, based on not just how they stand with you today. But incorporating data from other sources so you can get a better profile on what that consumer looks like. And I also think there's a great opportunity for the use of this open banking data, when we start thinking about settlement programs. And a lot of lenders will offer settlement programs. What I could envision is that as part of that settlement program, the consumer could be asked by that lender, hey, do you want to participate in our settlement program, I need you to permission yourself into your bank account. So I can have a better view of your financial situation, from a cash flow perspective. And using that data to optimize the amount of settlement that you're willing to take. Right? You might end up leaving money on the table. If you think a person situation is more dire than it really is so that open banking data can be used along with bureau data, your internal data, to really try to optimize and make a better decision on whether you're going to take 20 cents on the dollar or 80 cents on the dollar.

Matt Tepper  29:16 

Appreciate the insights, they're Rich. From all accounts, it seems that the panel up in Chicago was very valuable and offered a lot of insight. I know that we got a lot of rave reviews from those folks that attended Lend 360 up in Chicago, we've certainly kind of jet set around the world from from Boston to South Africa to Dallas and ultimately, Alpharetta covered a little bit of riches four decades of industry experience covered some of the insights as to the value that GDS has delivered for the past 16 years as well as some of the more pertinent items lenders need to address. I for one I'm looking forward to the future discussions Rich will be holding on this podcast. Be sure to subscribe to The Lending Link on Apple podcasts, Spotify or wherever you may listen to Do your podcasts and make sure you press that subscribe button. You want to be sure that you are you're made aware that you know Alterman is in town and he has the newest insights about what is happening out in the landscape. Again, thank you for your time today. Thank you, man. We look forward to speaking with you all soon.