Macro-Economic Trends and Opportunities for The Credit Department

[00:02] ANCHOR:

We’re ready to kick off an amazing panel today. The topic is macroeconomic trends and opportunities for the credit department. We have a fantastic group of panelists that are ready to come up here and share all of their wisdom and expertise on the topic. So with that, I’d like to invite our panelists to come up. Our first panelist is Christopher Rios, Vice President for Finance Solutions with Dun & Bradstreet. Next on the panel is Dan Meder, V.P. of Solutions Consulting with Experian.

[00:37]ANCHOR:

Next up, we have Mike Flum, CEO and Senior Vice President for CreditRiskMonitor. Dustin Luther, Head of Marketing with CreditSafe. And last but certainly, certainly not least, Eric Kider, Senior Vice President and General Manager of InfoGroup. And with that, I’d like to welcome up our moderator today, Bill Weiss, who is our Vice President of Business Development for Credit and Collections at HighRadius. Welcome Bill, and thank you and take it away.

[01:08]BILL WEISS:

Thank you very much! So we’ve really beefed up our panel this year and this is really an elite group. So I’m very excited to be moderating this panel. And we’ve got some great topics today. Before I jump in, one of the first things, that a couple of these questions have recession talk in them, and I wanted to mention, just to remember that they say that economists had predicted that the last, nine out of the last five recessions. So this is a very difficult topic to address.

[01:38]BILL WEISS:

These are not economists. But these are some great you know, I’m going to give you some really good insights on these questions. So with that, the first question, with ongoing trade wars, the Coronavirus outbreak and talks of a possible recession, what are the key macroeconomic signals that credit managers have to be wary? And we’ll start with Dan from Experian.

DAN MEDER:

Thanks, Bill. I guess the most telling indicator, if you read the press, obviously, is has already occurred as far as whether a recession is coming or not.

[02:08]DAN MEDER:

And that’s when the yield curve inverted, I guess, was back last May, and it stayed inverted for about four and a half months. And essentially what that means is that short term bond rates had risen higher than long term bond rates, and that has preceded, I think, the last seven recessions. So, it seems to be a very telling indicator. So, and it usually occurs within like eight months to 18 months after the first indication of the inversion, which would say that through November 2020, it is about when the window would be open if you go back to that initial inversion. Now having said that, there are a number of times where it’s happened and no recession has followed. So, you know, nothing is perfect. You know, that’s not to say that that’s the indicator that says could happen, but it seems to be a pretty predominant one. But I think another area to take a look at is around consumer spending.

Consumer spending is about 70 percent of the economy. And it’s still growing, but it’s growing at a slower rate in December for the quarter ended December at the end of December, it was up about 1.8 percent, which was lower than the 3.2 percent that had been up the quarter before. So, I think that’s one to watch. Certainly, consumer confidence would follow on that as well as unemployment if people start losing their jobs or not going have money to spend. I know from the Experian side, we track default rates on all kinds of consumer obligations like mortgages, credit cards, auto loans. And in December, we saw the default rates tick up about 2 basis points and they were up a little bit in November as well, so there’s a little bit of a negative trend there. So, these are the things, I think, to keep an eye on. None of them really points to a great sign for the future where they point to a recession or not, I guess. Still to be seen.

BILL WEISS:

Sure. Thanks, Dan. Mike?

MIKE FLUM:

I would just say, you know, building on what Dan was saying on the consumer spending side, you know, there is a recent Deloitte CFO survey where I think 97 percent or so said that they thought we were either in a recession already or would be by the end of 2020.

And the major point that they brought up in that was consumer spending. So, I mean, when you start talking about it being 70 percent of the GDP, that’s a pretty big one. So you start to see that declining or not growing at the same rate. That’s a really big sign. From the CreditRiskMonitor side, you know, we spend a lot of time looking at public companies. That’s really our thing. So we look at net finance and non-financial corporate debt as a percent of GDP.

And that’s something that the Altman, folks are all about at the Solomon, Solomon Center at NYU. And right now, I think the non-financial corporate debt to GDP is about 48 percent. Typically, you know, it’s a lot lower than that in every other period, at least in the last three times. And it’s got above forty-five. We’ve seen a spike of defaults approximately 6 to 18 months afterward, and that typically goes from about a 2 percent default, 1 percent default all the way up to 10 to 15 percent.

So when you look at the fact that GDP is also climbing as a percent overall, when you look at the losses on that, just bond wise, that’s like close to one point one trillion dollars or so of losses for trade creditors standpoint, I mean, you’re below on the capital structure. So now if you’re getting recovery rates of 10 to 15 cents on the dollar, you’re doing pretty well there. The other thing I would point to, too, is you look at the investment-grade bond rating.

So, you know, right now I think it’s like 53 percent of investment-grade bonds are currently in the triple B category. So when you start thinking about that and potentially a recession, you know, you have a lot of potential fallen angels right there. And while that’s kind of concerning on its own, I think the people that really need to be worried are those that are already in the junk status because now you have crowd-out effects, I don’t see why anybody would want to be buying a triple C rated yield if you have a triple B that just dropped out. I think the only other thing I would point to is the IMF report on global financial stability. I think they just recently said that you know, debt at risk, corporate debt at risk. If we had a recession that was half as bad as the global financial crisis, you’d have about 19 trillion dollars or so, about 40 percent of GDP essentially just gone.

So that’s pretty scary. I mean, those are essentially companies that can’t cover their interest payments based on their earnings. So that starts to happen on the scale. That’s really dangerous.

BILL WEISS:

Agreed. Chris?

CHRISTOPHER RIOS:

Yeah. So we certainly take into consideration all of these macroeconomic indicators. I think I’ll answer the question slightly differently in that I’m not an economist, I’m a practitioner. So all of this data is valuable, but understanding how to apply it to managing your portfolio I think is critical and what a lot of our risk managers want to know.

So what we do is we provide an economic health tracker monthly. We have predictive analytics that factor in all of these macro indicators. But I think what I tell businesses I speak with is it’s taking this information and overlaying it on the portfolio. And since where the theme has been AI and ML, it’s really utilizing AI & ML to help identify behavioral patterns within your portfolio. And those patterns should help predict outcomes. And those outcomes are what help inform business decisions.

So I think, certainly not discounting all of the indicators that were just referenced, but I think it’s what’s the pragmatic or practical application of this information? When you’re a credit professional and applying it to managing your portfolio. So I think yesterday I heard their concerns about, you know, the credit function in jeopardy of being important in the future state. I think where I see the function going is really becoming data stewards and informing the business on activities and decisions, you know, whether it be cash forecasting or we’ve got new Cecil requirements with some of the new FASB regulation changes.

So I think it’s really understanding how to take this data, marry it up to your portfolio and then really start to provide the proactive business decisions that I think most of the C level suite expect to, you know, gain from there, you know, from the Finance Operations Organization.

BILL WEISS:

Excellent points. Thanks, Chris. Thanks, everyone. All right. Onto the second question. Our current environment with, of course, the possible economic downturn. What are the key account management and credit strategy you should employ?

We’ll start with Mike from CreditRiskMonitor.

MIKE FLUM:

I would say I think in general, you really start to think about dollars at risk, more so than just absolute accounts. So if you’re really moving into a potential down and down cycle, you really need to start focusing on those where you really have large dollars at risk. You know, a lot of our analytics here are always going to give you probabilities of default or probabilities of bankruptcy. You know, the real key is that those doing the expected value on it, you need to have what your credit line is, how much money you have out there.

I mean, that applies both on the credit side and the procurement side. You should be looking at your spending, too. So from that standpoint, I think you definitely want to look at proactive monitoring as well. You need to find your customers and make sure that you’re really doing, you know, more frequent checking. So certainly any analyst you can find that are going to provide things that are maybe more granular than a quarterly. You know, you’re going to want to tap things like the stock market, I would think there’s a lot of smart people that are running a lot of money and they’re looking at default, whereas, you know, you’re really looking at bankruptcy in a credit function. So from that standpoint, I think you need to look at those signals. What? That’s something we obviously appreciate, CreditRiskMonitors, why we incorporated it into the first score.

BILL WEISS:

Sure. A lot of people don’t look at dollars at risk. So that’s a great point. Dan?

[10:10] DAN MEDER:

Yeah, I mean, I think kind of following on what Chris and Mike both said, a portfolio health check is probably a really good idea right about now. You want to look for vulnerabilities in your portfolio. Certain industries that might be vulnerable to a recession. We all know construction can tend to struggle during recessionary times. Try to find out what sort of risk buckets you have. And kind of to Mike’s point, what kind of dollars do you have in those risk buckets?

And then do some scenario planning against that. Giving you an example in the 2008 recession, if you take a look at the default rates on CNI loans in 2007, then you compare them to 2009. They jumped almost fourfold. So, so it can be pretty dramatic in terms of the defaults and even in your existing portfolio. So I think it’s a good idea to do it. The health checks and see where you are. I’d say the other thing is because we’re not there yet, you have a, you don’t probably have a lot of time, but a little bit of time anyway. Go through your processes to make sure that you’re up to date on, in terms of your account management processes, are you getting the kind of signals that you need to indicate that trouble is ahead? Things like, you know, alert services and notifications that there is trouble with different accounts that you might have. And also to make sure your collection processes are buttoned up and that you’re able to act early to get your money out while you can if you so if you smell trouble.

[11:37] BILL WEISS:

Excellent. Eric?

ERIC KIDER:

Yeah. No. So building, you know, kind of leveraging what both Mike and Dan just shared, you know, with InfoGroup, what we’ve been explaining to our clients is looking at and reviewing the alerts and the triggers that you’re leveraging as you’re doing your risk management or credit decisions, you know, as the economic conditions change whether be here in the U.S. or in other countries, that you might be working and actually having clients, we’re not telling people to panic, but leverage that of the alerts and look at ways to include those more into your advanced processes, not just in your credit department, but we’re also helping people bridge them to their sales and marketing efforts because if you actually take advantage of those alerts and those type of risk triggers, you actually can help improve identifying better prospects. So that way you don’t actually have that debt or any kind of bad clients coming back downstream that turn into a credit or risk situation for your organization.

So we’re doing that both because in some cases the credit function really should not be seen as a back office. It should be a front office enabler with the marketing and sales teams. So that’s what we’re doing now with our organization.

BILL WEISS:

And then credit and sales working together as a big theme of what we’re talking about. So that’s absolutely fitting. Thanks. All right. Question number three, what are the bureau’s best practices for thin-file customers? And when I say thin-file customers, I mean customers that don’t have a lot of credit data available.

And we’ll start with Dustin. We didn’t forget about you, Dustin, for this question.

DUSTIN LUTHER:

Okay. So, yes. So one of the things that stick out for me when we talk about that is just like when the company CreditSafe came into the US market there, they largely came from European markets where the date is much better on smaller companies. So we knew we had to go out and just get data in smaller, you know, like what is it?

What does it look like? So when I’m, the advice I would throw out, then when you’re looking at this, you know, there are lots of other data sources besides just trade payment. Right. So we can do, you know, some of the things we’ve looked at exploring and our options and I know other people on the panel have, you know, their companies have similar things, but it’s like the financial trade data that we can add to that, the file.

We’ve made it just as easy as we possibly can to have people add their data into our database using, you know, connecting up their accounting software and as well as just, you know, making it easy to go do like soft pools on personal credit reports, that kind of thing for the owners of smaller businesses. So the thin-files in my experience, start largely around more smaller entities. And for that, it’s really just looking at alternative sources and enhancing the data that you can get beyond what might be just this standard in a credit report.

[14:10]BILL WEISS:

Thanks, Justin. Eric?

ERIC KIDER:

Yes. You know, the thin, dimension of thin records. It’s a relative term. It depends on what you’re looking to do with that organization. And ultimately, what you’re trying to determine is what is the information you really do need to make that credit decision. In some cases, you’ll need to look at other sources. We actually promote multi-sourcing because there is no single source that will give you all the answers. Some data providers will give you information that is unique to a certain segment or a certain industry grouping.

Others will give you different financial details. You know, when I talk multi-sourcing, a lot of companies tell me, well, my gosh, that’s that sounds like a pretty expensive in the end, you know, double digits of a budget for data or any kind of credit function. And in fact, it actually doesn’t have the end budget or cost if you know how to best source which information you’re looking for. So thin records when you’re looking to manage your credit decisions don’t really need to be thin if you actually find the right balance between what you’re looking for, and more importantly, what else are you trying to leverage with that information?

BILL WEISS:

Thanks, Eric. Dan, take us home?

DAN MEDER:

Yeah. So follow in a little bit on what Dustin said. I think he made a great point about being able to use personal credit reports on business owners. You know, that’s something that, at Experian, we’ve done for a long time and the scores that we create by blending the commercial data with the consumer data, even if it’s thin file commercial data tend to outperform both the commercial score and they actually outperform the consumer score because the blended scores are are are geared towards business performance, not individual performance.

So that’s a really great way to overcome some of the issues on thin-files on small businesses. Another area that’s starting to become more prominent is the use of user permission data. There are aggregators out there that if you’re hooked up to one of them, you can go in and get the credentials from the owner, the business owner. And they have to give it to you. It’s, they have to permit it. And then you can see things like their DDA accounts, you could even see their 4O1Ks if they want to make it available to you. Like if you want to do a verification of assets or something like that to see what they have. This is also, by the way, part of a trend that you may be hearing about, which is especially on the consumer side, where they’re trying to allow consumers to take more control of their data. And this whole idea of user permissions stuff is going to become more and more, I think, prominent.

So something maybe you want to keep an eye on. Nothing I did as social media data. We’d see it again when we combine social media data with our thin-file, commercial data. We’ve seen improvements in the performance of credit scores. Now you’ve got some compliance questions that people would raise about, you know, OK, you’ve got the number of likes as a predictive attribute. It’s really the comparison of the number of likes overtime to some benchmark, so it really is a valid number, even attribute, even though it doesn’t maybe seem so at first, but it certainly has helped raise the power of the scores and helps offload some of that thin file problem that you have.

BILL WEISS:

Great perspective. Thanks. All right. Question number four, how attractive does cross-border trade look in the current scenario? And we’re gonna start with Eric from InfoGroup on this one.

ERIC KIDER:

So, you know, cross-talk, you know, of course-trade, you know, part of the trade is actually something that will, is always here and actually is something that each company, if you do business outside of the US, is still attractive.

However, there are multiple different factors you need to take into consideration. Tariffs, you have to look at data availability, what is the economic climate of the country or the actual location which you’re looking to engage with? To me, it’s more of looking at the balanced approach to understanding not just the company doing the business within that country or that location, but also their affiliation with other companies or their partners and affiliates in other countries that also expose risk.

We’ve been working with clients actually and identifying what is the right sourcing of data to feed into their credit decision process so that instead of saying, no, I don’t want to work with a country in Argentina or a company in Argentina or somewhere in Mexico, we’re actually guiding them to say, look, here’s the correlation of what things you should be figuring out and looking at in order to be on the right terms to mitigate your risk overall. But it’s still very attractive.

I mean, the markets themselves, there are some very interesting companies and opportunities to grow without exposing yourself to greater risk.

BILL WEISS:

Thank you. And Dustin, CreditSafe knows a thing about international business?

DUSTIN LUTHER:

For sure. Now, it’s interesting ’cause when we talked about this question, it came up and a lot of it was like, well, of course, it makes sense. Like, it’s not you know, we want you should be looking at international opportunities.

That would be, you know, dumb for us to say otherwise. Right. One of the things that stuck out for me as I’ve been, you know, working with more of our prospects and customers and hearing their stories and stuff is just how sometimes when you’re going out internationally, you’re looking at, you know, there are different data sources like different data points will mean different things in different countries. And sometimes you might even not have great data.

And this gave a short story, of just why I was out at a prospect meeting with one of our sales guys. And, you know, they were talking it was a big company. You guys would definitely all know it. I’ll leave their name off for now. But, you know, they actually give money internationally to different companies. So they can produce different movies and shows and things like that. So they went you know, we went through and said, well, what did they’re asking us, like, what data do you have on this company in Argentina? What data do you have on this company in Romania? What data do you have? And our sales guy was piping through. And then they came through and they said, what? How about this company in Mexico? And he goes, you know, Mexico data is just not that great. Does it matter who you’re working with? That data isn’t going to be as good.

Well, let’s look at it anyway. So we pull up the file. It really was kind of pretty thin. But there was a big fat warning on there that these guys owed half a million dollars, that half a million dollar leaned to the government. And so. Ah, and so the guy who is in the audience there who was asking questions had I seen that we went to give them a half a million dollars. Right. And so sometimes even when it’s really thin like data, one or two data points can be enough to help you make a much smarter decision about where you’re going internationally.

[20:17] BILL WEISS:

Yeah, absolutely right. Thank you. All right. The fifth question, with recessions, there is also a likelihood of fraud. How do you mitigate these risks? And we’ll start with Chris from DnB.

CHRISTOPHER RIOS:

Yeah, I think from a DnB perspective, we focus on our live business identity, which is essentially identity resolution. So as your onboarding customers, it’s making sure you know who the actors are, who the beneficial ownership belongs to. But then. Going a step further, obviously, we would encourage utilization of the Dunns number to ensure that you identify corporate linkage and relationships within your portfolio.

We do address rationalization. I think what’s important is the customer record is significantly important in maintaining it in a clean and accurate way. And it’s not a one time exercise. You really need to go implement a maintenance program around your customer records once you’ve onboarded the new clients because again, it’s all about ensuring you understand who the actors are that are trained that you’re transacting with. The other thing that I’ve seen questions come up now. We’re seeing a lot of what vendor onboarding would use to validate and verify vendors.

We’re getting a lot of questions on the customer onboarding side. So things like we’re incorporating o-fact checks, restricted parties listings, FCPA violations. I mean, these are all also indicators to ensure that you know who you’re transacting with. And so I encourage anyone, you know, whether you’re using anyone on the panel’s data. It’s really about ensuring identity resolution, corporate linkage, and your customer’s hierarchy and the validation, you know, of the postal code versus physical address codes.

And then look at this other data that you may find on the vendor management side because it is applicable even in the customer onboarding phase of your process.

BILL WEISS:

Thanks. And knowing you for so many years, I know you have a lot to say about this too.

DAN MEDER:

Yeah. So. So one of the approaches that we’re looking at is more along the lines of device intelligence and the actual devices that are committing the for fraudulent transactions, the fraudsters today can do things like mimic credentials on somebody’s machine.

They can change IP addresses and that sort of thing. So, these are events that occur. So even after you’ve, you know, to Christmas point, even if you’ve verified the business, the person who is actually coming through maybe only mimicking the business. They may not actually be part of the business. And that shows up in the device. Now, the good news is that fraudsters tend to have abnormal activity. As an example, they start to pump through a ton of transactions, which would be unusual for a single machine to be doing that.

So there are some triggers that say, yeah, there is a high likelihood that this behavior is a fraudster and not the person who they say they are. And then from there, what you can do is you can start to track through different, different methods, say where the transmission originated from. And one example that we saw play out not too long ago with a client of ours, is that a transaction that looked legitimate, actually came from somewhere in Eastern Europe and the client was able to shut it down before they got themselves in any kind of trouble.

So everything seemed to check out credential wise. But when they inspected the device, they found out that it was not the person who they said they were and we were able to take effort. So that’s some of the kind of things that you can look at, the behavior of stuff that could be very, very important.

BILL WEISS:

Excellent. Thanks, Dan.. And we have time for one more question. This is one that will be no surprise to anyone that I’m asking.

So finally, what role do you see automation and technology playing? And we’ll start with Dustin.

DUSTIN LUTHER:

So in terms of automation and technology, the part that I come back at it, I must say I do come from a marketing perspective. I can’t help it, that’s what that’s where my head is most of the time, like with the technology, when I’m seeing the problems and having talked to others, where they’re getting problems is, when you’re going after technologies where the integrations aren’t working well.

So one of the things that have come to me, constants, is we’re looking at tools are trying to expand. We’re working on it’s like, you know, there are all these features you get. But if you don’t have good, in my case, like a good CRM. Right. And this or you know, where you end and you’re really focused on how well is all this stuff going to talk to each other these days just becomes so critical to all these integrations.

So without a doubt, where I would focus and I would think any of these is get yourself the knight that, you know, your hub platform, whatever you’re using is core and then focus less on features and more on the process when you’re looking at new technologies.

BILL WEISS:

Thanks, Dustin. Mike?

MIKE FLUM:

I would say, you know, obviously, as we start getting into more and more complex analytics, you know, you’re going to see accuracy as a lot of these scores go up.

That’s just part of the main game. I would say, though, you know, one of the concerns that I have is obviously as you move into more deep neural nets, it’s sometimes very hard to explain any of this stuff, up the chain. You know you get into a steering committee, you try to explain why a score dinged this company. Well, I don’t really know. So I think it’s important to really keep looking at developing, you know, deep neural net models, but also maybe running similar linear out, you know, linear regressions or logistical regressions next to him, just so you have some idea what the mechanisms of the turn are.

The other thing I would say is, you know, we talked a lot about fraud and thin-files here and as blockchain and smart contracts become more and more prevalent. A lot of those concerns kind of go away. You don’t necessarily need to do as much on the thin file review because you can set up a smart contract where it’s outside of a fraud condition and outside of, you know, I’m not giving you this money. I mean, Dustin’s point, right?

Not giving you five, you know, half a million dollars for you to just pass on a lean. I’m giving you half a million dollars in a place you can’t touch it until I get delivery of my goods. That’s a very different mechanism. So I think that’s a big one that probably moves. Certainly. You know, I think I think all of this stuff is good for the credit manager. You’re gonna be able to focus much more so on your real risks.

So anything that can move you into those classifications where you’re really now adding value, I mean, to Christopher’s point as that data stored actually looking at the insight and passing that along, that’s your new role instead of doing the rote math. You’re not going to be calculating spreads. You know, it’s just it’s easier to throw that off to the algorithms.

BILL WEISS:

It’s great to see how the role is evolving. Great point, Eric?

ERIC KIDER:

So, yes, I think, you know, when you think about AI and automation, the advancements that are being made, including that of what we saw, you know, built over the last few days here at Radiance 2020. The thing that’s interesting is I look at data and of course, being with InfoGroup, you look at data as the fuel and to do it, the data itself is something that a lot of organizations when you think about the data hygiene, the consistency, not only through that of the record, as we discussed earlier, but to be a thick record or thin record.

Most companies are still not looking at the data as a way to really create the hygiene process to ensure that whatever is in place within their CRM or ERP platforms that either fuel the sales, marketing or credit or risk functions, that data has to be the best, consistent and most accurate. Or else every AI or anything that is related to automating the process becomes only as good as that of the fuel source. You know, if you put diesel fuel in a Ferrari, it’s not going to actually go as fast if you unless you put super gas in.

And so the same concept applies without AI in automation. You know, we are really focused on helping organizations looking at ways to improve their data accuracy, consistency, so that as they take advantage of new AI and automation, including enhancement of their models, that ultimately becomes that much more powerful and more predictive.

BILL WEISS:

Thank you all very much for attending this panel. I hope you get a lot out of it and enjoy the rest of the conference.

UNKNOWN SPEAKER:

Thanks, Bill.