Credit Management

When you’re launching a new product, business line, or starting up a business, you’ve got to move fast and break things.  This means taking a minimum viable product (MVP) approach, where you’ve got to sacrifice scalability by implementing manual processes to support the early-stage business.  Commonly, a manual process will be in place for credit applications and approvals – pulling the credit report, reviewing the data against a scorecard or policy, and then making the decision. Since this likely takes a day — or often longer — the process decreases your customer’s experience, and can hurt your ability to scale and grow revenue the longer you wait to automate. To grow the business and take it to the next level, you need to migrate away from the paper-pushing approach. The next step is to move toward an automated solution that integrates credit decisions with the back office, such as an ERP, CRM, or another custom system, employing APIs. Using an Application Programming Interface (API) to Connect to Your Decision Engine An API, or Application Programming Interface, is many things. It’s a set of instructions and technical documentation for developers. It’s a collection of services that allow you to interact with a product or service. And it’s a way for businesses to open up and allow for new kinds of innovation – allowing for new business models and application development that wouldn’t be possible without APIs. In the last decade, APIs have become system agnostic, meaning they plug-and-play into nearly any system because they are standardized and popular amongst the development community. Because of this popularity, APIs make it easier for the business to get buy-in from the IT department, which is essential to automating the credit decisioning process. Without an API, the IT department must devote significant resources to the project because more infrastructure to host large database will be required. APIs allow you to pull data in real-time only when you need it, reducing system complexity and decreasing application development costs.  Reduced complexity also means less risk because you are more assured that your IT department will be successful with the integration. Often, when IT departments are presented with information about the API, their response is “No problem, this is standard. We have integrated with a very similar API before. We can do this.” How does your decision engine interact with APIs? You can use APIs to get the raw data elements your credit policy or model needs to render a decision, no matter if the data is internal to your business or provided by third parties. Taking Decisions to the Next Level with Machine Learning According to a recent Harvard Business Review project, the key to successfully utilizing machine learning isn’t to get caught up in new and exotic algorithms but to make the deployment of machine learning easier.  There are many use cases where machine learning can be employed, but use cases where data-driven decisions are being made, as in the credit approval process, are archetypical. During the early stages of the machine learning process, you train the model by feeding it data from past applications. Then, as you use the engine for real-time processing, the engine learns from past decisions. If the engine was originally approving applications with a borderline credit score but found that these applications often ended up being poor risks, the model would then begin turning down these applications. The key ingredient in making machine learning start to work for your credit department is to have domain experts, credit managers, help the IT department focus on the key variables that can help the machine learning model to predict key outcomes – credit losses, bankruptcies, and business failures, and to put the models through many rounds of testing and validation before putting them into real-life practice. Now is the time to move your manual processes online using an API and machine learning. According to Mary Meeker’s Annual Internet Trend Report, 60 percent of customers pay digitally compared to 40 percent in the store.  And it’s likely that the gap will continue to grow. The longer you wait, the further ahead your competitors will be in digitizing the customer experience — and the harder it will be to regain your footing and catch up.

For credit and risk managers, how effectively you manage your book of business can sometimes be the difference between tirelessly chasing after accounts for collections or proactively growing your portfolio. Though there may be many factors that affect your specific credit risk management process, the underlying goal to reduce and manage your exposure to risk does not change. To help you successfully manage your portfolio, we address 4 common mistakes you need to avoid: 1. Not automating your processes By not having an automated, standardized method of assessing your current accounts, overall portfolio exposure to risk increases substantially. The manual review process relies too much on shrinking human capital, requires more time to complete, and can cause inconsistencies across the board. Automating processes where you can will help you focus your resources to the applications and accounts that need attention or manual review. 2. Not setting up triggers that alert you of key events When you know problems are coming, you can take steps to protect yourself and your business. The sooner you know about something, the faster you can act on it. Setting up triggers that notify you of key changes within your customers’ accounts like a rise in late payments, increased number of collection filings, or bankruptcy filings, allows you to keep a close eye on your customers and take immediate action, if necessary. Especially when your portfolio outgrows your resources to manage it, setting up automated triggers can give credit and risk managers the foresight to manage proactively, rather than reactively. 3. Not monitoring for risk (or growth) Managing a large portfolio can be extremely labor-intensive if you don’t apply risk scoring. A traditional risk score, in this case, usually considers the credit, public record and demographic attributes of the account, and applies a value to the results as a means of quantifying risk. This helps you prioritize your time and efforts on the minority of customers with scores that signify increased credit risk, rather than all your customers at the same time. On the flip side, you can target accounts with positive scores for growth opportunities.   4. Not segmenting your portfolio Another common mistake that many portfolio managers make is not segmenting their portfolios to identify insights at a macro level. For instance, leveraging data to segment your customers and accounts by industry, business type, business size, etc., can help you uncover hidden trends not obvious otherwise. This then allows you to apply appropriate treatment strategies to mitigate risk within the accounts. Additionally, you can identify market opportunities for growth using SIC/NAICS codes and other marketing data sources to grow your footprint. Want to talk to an Experian expert regarding your portfolio management strategies? Contact us today.

When a new customer wants to establish credit terms with you, the first thing they’re asked to do is fill out your credit application. When you hand over a paper application, did you know you could be negatively impacting your revenue or creating a poor customer experience? Some companies don’t. More than likely, your customer has filled out at least one digital application in the past. The initial perception your application says about your company is that you’re out of step with technology — which may lead them to wonder where else you may be lagging behind. Digital applications provide a simplicity factor, and by not offering one, your credit approval process is perceived to be more difficult, leaving the customer with more work to do —spending extra time writing their information by hand and returning the application — either by email, fax, or in person. Because many companies have already moved to a digital application, your pen-and-paper process sticks out to the customer — and not in a good way. Not to mention, manually processing a paper application takes longer — often much longer — than a digital application. This means customers leave without a credit approval, giving them time to change their mind about their purchase or find a better deal — meaning you just lost a new sale. And even if they still choose to work with you, their relationship with your company starts out with a less-than-amazing customer experience. After the paper application is completed, the workflow process is often time-consuming, error-prone, and cumbersome. The time involved also means that your company waits longer to receive revenue from the sale. By using a manual process, your team spends hours on processing and decisions that could be better spent directly servicing customers or working on other initiatives to grow business. DecisionIQ from Experian automates consistent real-time decisions, streamlining your entire process from applications to onboarding.

All business customers are not created equal. Even companies that look solid at first glance can hide festering problems that eventually can impact your bottom line. Successful credit management requires you to carefully evaluate the financial health of every business that asks for credit terms. Here are 5 questions you should be able to answer before extending business credit: 1. Is the business what it claims to be? Sometimes, companies needing credit will provide inaccurate information to win approval. Before opening an account, you need to confirm the applicant‘s bona fides, including its location, size, number of employees, annual revenue, years of operation and similar financial indicators. 2. What is its payment history? Although past performance does not guarantee future results, a company’s payment history is often a strong indicator of how it is likely to behave in the future. Pulling a business' credit report can easily provide you a snapshot of a company's payment history as well as other risk measures.  3. Are there hidden factors that could affect its ability to pay? Are there pending judgments, lawsuits, bankruptcies, regulatory citations or other “red flags” that could make it difficult for the applicant to meet its obligations in the future? This is another area where a business' credit report will be a key factor in helping you uncover a potentially risky business.  4. How much credit should you extend? All credit contains an element of risk, but you can mitigate that risk by limiting the amount of credit you extend based on factors such as the customer’s sales volume, debt to-asset ratio and similar aspects. 5. Under what terms should you extend credit to this customer? You can mitigate risk further by carefully calibrating the combination of interest rates, minimum payments and other contract terms based on each customer’s individual financial metrics.  

Here at Experian, we work with many clients whose customers and suppliers operate all over the world, and one of the biggest challenges for many is being able to detect financial duress by monitoring companies whose headquarters are outside of the United States. Early identification of negative activity helps your company prevent lost revenue and service interruptions, it also helps minimize reputational damage caused by doing business with a company in violation of U.S. laws. These early notifications can also help mitigate the effects of changing economic conditions while growing new business opportunities with lower risk. Today we are thrilled to announce that Experian’s commercial alerts now enable you to monitor more businesses in more countries with greater precision. Experian now offers 25 alerts on 8 countries in Western Europe, with 8 more countries coming soon! These international alerts offer the ability to stay up to date on changes such as: change in ownership, business name and address, as well as changes in credit limit, balance sheet information, and company status and much more. Proactive notifications empower you to act quickly and mitigate risk, collect on overdue amounts and retain your best customers. Want to know more? Contact us today so we can start helping you reduce the risk in your growing business.   International Reports & Resources

This week for Business Chat | Live we interviewed Peter Bolin about business owner wealth, and how lenders are finding new ways to evaluate entrepreneurs in the underwriting process. Gary: Today we're going to have a discussion on business owner wealth and evaluating business owner wealth in risk models. Joining me today is Mr. Peter Bolin. He's the Director of Analytics and Consulting for Experian. Good morning Peter. Peter: Good morning Gary. Good morning everyone. Gary: Let's just kick off this discussion. Business owner wealth and small business owners and evaluating risk. What's it all about? Peter: Yeah, thanks Gary, thanks everyone. As I travel around talking to a hundred clients a year, one theme that always comes back is, "Hey Pete, can you and Experian help us get to yes?" We know that there is a pool of small businesses, a pool of small business owners out there that are small, that are emerging, that are cutting edge, maybe not solid yet, but they don't have a credit file, what we typically call maybe credit invisibles or maybe thin files, but we know that they have a good idea, we know that they've good product and services, but we can't approve them. Is there anything you can do to help us get to yes? At Experian we got to thinking about that. We have a tremendous amount of data assets as everyone know. We looked around and we said, "Hey, there is this new product that we have called the Wealth Opportunity Score," and that estimates, based on our proprietary database, the wealth of an individual. We got to thinking, does wealth of a business owner affect the opportunity to be approved? That's what we're here to talk about today, Gary. Can we use the Wealth Opportunity Score on a business owner based on a sample of our data, we have some great results, and we definitely feel that we can help lenders, wholesalers, target marketers, get to yes. Gary: Okay. So you mentioned getting to yes. What does that mean in terms of say, a new business or a business that has say, a very thin credit file? How does that work? Peter: The first thing that we do when we're evaluating any new data source, and while wealth insights have been at Experian for a year or two, it's new, we're introducing it for the first time to the commercial space for the business owner, the first thing we do is say, "Okay, can we get a predictive lift by using this data?" The answer is yes. In particular, can we use this in the thin file, in the very, very small, emerging businesses that maybe we could refer to as the credit invisibles. It's kind of an overused term. Invisible to who? So, we're using this in this example as credit invisible are a very thin file or maybe have no file on the commercial credit report. We took a sample of business owners in that population and we added this Wealth Opportunity Score as an attribute within our demographic only segment, and we said, "Yes, indeed, this does help the predictive power of that segment." Going from a KS of a 16 to a 23. A KS for those of you who might not know, that's the Kolmogorov-Smirnov statistic, that's the industry standard for measuring the predictiveness of the model, the higher the KS the better the separation between the goods and the bad. We're seeing that yes, by just including this data, on the credit invisible/thin file, that segment was able to improve the predictive power by 43%. Gary: Wow, that's incredible. So, we discussed the better prediction of risk using the owner wealth. What does rank ordering have to do with that, and why is that such a big deal? Peter: As quant jocks sometimes we get overly, overemphasize the KS, the rock, the Genie, area under curve and those are all important statistics, but we can't forget about another critical part, which is the stability of the model. That's where we look at how well it rank orders. We'd like to see a nice, smooth monotonic progression in the rank ordering of the bads throughout the decile. We would like to see a large number of bads pushed down to the riskiest scoring, and fewer number of bads pushed up to the least risky. As you can see, when we added the Wealth Opportunity Score to our demographic only file, we got a nice, more smooth monotonic progression, which says not only do we get lift like we talked about in the first slide, but we're also improving the stability of the model, which is very, very important. As you can see, it's still a little bit choppy. Some of you might say, the skeptics out there might say, "Gee, Pete, this is all pretty choppy." However, keep in mind this is a demographic thin file, not much to go on other than some key demographic items, but by adding the Wealth Opportunity Score we're able to increase the predictiveness and the stability of the model. Gary: Wow. Okay, so there's been a lot of talk about women-owned businesses, minority-owned businesses lately. How does wealth play in the role in accessing credit for minorities, and in particular women-owned businesses? Peter: Absolutely. Gary, that's a huge topic right now, access to credit. Do minority-owned businesses and especially women-owned businesses have access to credit? We're also looking at data to help evaluate that. The next thing we did was, we were curious. Okay, is there a difference between a male-owned business and a female-owned business when it comes to wealth? Really, if you look at the bottom two lines on the curve, you see that there's really not. They're pretty similar. They are fairly similar. There is some blips on the lower side, so if you're looking at the left side of the graph, you do see where that pink line is the female owner, they do tend to have slightly lower wealth, and that's measured on the first Y axis. What we're saying is that there's really no difference. If you're looking at wealth, there isn't much difference between a woman-owned business and a male-owned business, so that should not be a prohibitor in access to credit. The other thing that we looked at on the other, secondary X axis, is the whole concept around annual spend. This is annual spend on the business owner personally, not the business, so I just want to make that clear. We looked at that, and we saw similar trends, that there really wasn't much difference in spending except in the very, very high quadrant up there, there was a little bit of difference in the extremely wealthy. Actually, it says that male owners, male business owners, had much more annual spend. So, not only were we introducing the Wealth Opportunity Score, which is a new concept in commercial lending, we're also looking at the total plastic spend on the business owner personally, which we found is a very powerful indicator, especially when you're trying to target market. Gary: Excellent. Okay, changing gears a little now, to target marketing and how does wealth help with those that are maybe in the market for credit? Peter:  Well, it's very interesting Gary, because what we find is that there's an inverse relationship between wealth and in the market, so very, very wealthy owners are not as in the market based on our in the market score. We have a business credit seeker model, which predicts the likelihood that someone's going to open up a new trade, so, that they're in the market. They're really serious about it. We threw these attributes and this scores into that model, and what we found is that intuitively I think that it makes a lot of sense as well. What we see is that lower wealthy business owners are more in the market, right? They don't have any personal wealth. They need capital. They need access to capital. They're anxious to get capital. There's a higher percentage of the lower wealth spectrum that are looking for credit. However, however, that's not to say that the high end, so if you look at the very high end, three million or more in wealth, there's also a percentage, 9.5% of the population that are also looking for credit. They could be a small business, they could have two employees or less, they could be around for two years or less, but they have high net worth. They might be invisible on the commercial side and this Wealth Opportunity Score will definitely help them, help lenders and wholesalers get to yes. Gary: Are there other particular industries that you recommend targeting with the new business credit seeker model? Peter: Sure, that was the other kind of thing that was surprising to me, I'll be very frank. This surprised me, because when we looked at the industries and then we plotted the risk scores, and then we plotted the bad rates, and then we looked at the wealth of the individual, or sorry, not the wealth at this particular point. What we're looking at here is the actual in the market. What we found is that the industries that have the highest in the market percentage, which is measured on the blue bar, and then we look at their average IPV2 score, that's Experian's commercial risk score predict a likelihood of a trade going 91-plus, we see a convergence that the agricultural, wholesale trade and mining industry, which surprised the heck out of me, were the three industries that have the highest percentage and likelihood to be in the market, and also had the highest IP score, which means they have the lowest risk. The mining industry in particular, Gary, is shocking, because over the last eight years, without me getting too politically sensitive, that industry has taken a battering in the last nine years. Big cuts, big cutbacks, in traditional coal mining, and what we're seeing now, with some of the new administration's outlook on mining, the regulations are coming off and we're predicting that this could be a very big growth opportunity for our clients in terms of marketing, in terms of wholesale credit, traditional lines of credit, and traditional term-type credit. The mining industry in the market and a very high IPV2 score, and later on we'll see also some interesting wealth information about that as well. Gary: Excellent. You talked about credit scores. What about bad debt rates and industry targeting? Can you talk a little bit about that? Peter: Yep, very similar results. We also wanted to look and see what the bad rates were for each of these industries to give the viewers an opportunity to see this trend. This is also the exact same thing as you saw with the score, relatively low risk. Agricultural, wholesale trade, mining again, when you look at their bad rates as measured on that secondary axis, but high in the market. Very, very surprising to me, once again, I did not expect to see especially the mining, given the fact that they've been hammered the last nine months, so again, if you're looking about low-risk industries to target, those are the three that I would recommend. Gary: Does owner wealth matter in these industries in particular? Peter: It does, it does, especially again in the mining industry. You see some interesting wealth statistics. If you look at the distribution of the Wealth Opportunity Score, which once again, predicts the wealth of the business owner, or the individual but in this case we scored it with business owners, you see some blips. Those yellow bars, some blips, and as you can see, there's also a tremendous opportunity really up there in that big, look at up there, there's a significant percentage in all three of these industries, but particular mining industry that have extremely high net worth. We already know from previous slides that they have low risk, low bad rates, so again, the mining industry, high wealth, high scores, low bad rates, again, another indication that using this data can help you get to yes. Gary: What about micropreneurs, you know, these businesses that are just emerging, just getting started, they're credit invisible, right? Does the business owner wealth factor come into lending and risk models? Peter: Certainly, and the whole concept of a micropreneur, that's kind of a new term, I'm not even sure it is a word, but maybe we invented it, the micropreneur, a great concept, very, very small businesses. It's not uncommon, Gary, that I get the following statement made to me when I'm talking to clients: "Pete, we don't approve anyone that's been in business for two years or less. We don't approve anyone that has two employees or less. We don't approve anyone who's a sole proprietor. We want to avoid those type of businesses," and I would urge all of the listeners and all of the viewers to think a second time about that, because even if you look at the far right-hand side bar, there are 28% of the population with three million or more in estimated wealth, 28% of that population have been in business or have one or two employees or less. That wealth could be used in your personal guarantee situation, that could be used as collateral. The other nice thing about the Wealth Opportunity Score before I forget is actually evaluating the net worth of an individual. It gives you the opportunity to verify that. It's very common in these situations that if a small business, one or two employees or less, goes in for an application, applies and they have to have a personal guarantee, and they say, "I'm worth $3 million," well, how do you know? Well, with this system you can come to Experian and at least get an estimate that that wealth is right, that you've verified that wealth and that you can set your credit limits, you can set your approval accordingly. So again, what does it mean for the micropreneur? It means that if they have wealth, it's another data point that can help you. Gary: It's helping them get their businesses started. It's helping to drive the economy, which is, at the same time this is good for everybody. It's a win-win situation of, it sounds like to me anyway. Peter:  Absolutely. You know Gary, the whole concept of what we're talking about today comes from my personal passion, and I know Experian Business Information Services as an entity's passion, to be an advocate for the small business owners. What we talk about frequently. Through this passion, we're looking at all of our data assets. Can we use our data assets for good? And the good is, as small businesses goes, so goes the United States, and I'm really passionate, and I know Experian Business Information Services is passionate about turning over every leaf, every piece to data, that will fuel small business growth and fuel our economy. Gary: That's awesome. Okay, well this has been excellent Peter. I think if folks are interested in this, we just invite them to drop a comment on the video here if you find this on YouTube, or come to our website, experian.com/b2b. I'm sure we can connect you to Pete and his experts in business information if you want to talk about business owner wealth models. Pete I want to thank you so much for taking time out this morning to come on and talk to us about this topic. I really enjoyed our chat, and we'd love to have you back again in the future. Peter: Thank you Gary. I had a lot of fun. My first live TV spot. Gary: All right, thank you Pete. Have a good day, and- Peter: Thank you. Gary: And have a good day everybody. Thank you so much for coming to our live video. As I said, we're just wading into this. We'd love to have more of these live shows. If you've got ideas for live shows, if there's things about business information, things that would help you be more successful in business or evaluating risk, just send us a note on our YouTube channel. We'd be happy to consider that, and maybe put a show together. Maybe even invite you on as a guest. That's it for today, so thank you everyone and have a good day.    

Over the past 10 years, some of America's most venerable industries have found themselves threatened -- if not put on virtual life support -- by aggressive Web-based "disrupters." Traditional hotels and motels now face stiff competition from AirBnB. Old school taxi companies are losing riders to fast and nimble ride-sharing services like Uber and Lyft. Blockbuster Video? Killed by Netflix, Hulu and Amazon Prime. The Yellow Pages? All but killed by Google. And anyone who's been following the brick-and-mortar retail industry knows that Amazon and its digital brethren are in the process of making retail department stores -- along with the shopping malls they anchor -- as quaint as hydrogen-filled balloons. So what's the next industry likely to find itself up-ended by digital disruptors? It may very well be debt collection. Granted, the words “Debt Collection” will sometimes send a shiver through your spine. But past-due accounts are a serious problem for the affected businesses. Not to mention being a drag on the economy as a whole. In an April 27, 2017 article in Forbes, contributing writer Robert J. Sczcerba offers the following statistics: At any given time, there is an estimated $12 trillion in outstanding consumer debt, with $672 billion of that debt being at some stage of delinquency. At least 7,000,000 small businesses in the U.S. have trouble collecting payments from customers. Of these 7,000,000 small businesses, 49% of them had to write off bad debt in the past year and 43% have customers who are more than 90 days late on payments. So, yes, the collection of outstanding debt is a big deal. Which is why the debt collection industry has to innovate. While many hotel chains, retail stores and transportation services have at least tried to keep pace with technological advances -- and customer expectations -- the debt collection industry has remained relatively unchanged for decades. As quoted in the same Forbes article, Christoph Bene, Managing Director at Brock Capital Group, describes it this way: "Fifty years ago, debt collections agencies relied on annoying phone calls and form letters sent through the mail to encourage people to pay their past due accounts. Today, with the ubiquitous use of smart phones, texting, e-mail and social media, the debt collection industry -- yep, you guessed it -- still mainly relies on annoying phone calls and form letters." Which is where Artificial Intelligence (AI) comes into play. AI has the potential to revolutionize debt collection by reaching out to debtors via the media they use, speaking to them in a language they understand, and developing customized solutions based on each person's individual circumstances. For example: The Medium is the Message. There is a growing cultural divide between generations. A business that tries to communicate with recent college graduates the same way they do with retired Baby Boomers isn't going to be in business for long. The choice of communications media -- be it email, text message, live phone call, printed letter -- depends largely on the character of the recipient. And when an agency is dealing with hundreds -- perhaps thousands -- of delinquent customers, it takes a very "smart" application to choose the channel best suited for each message. Context is King. You don't speak to an employer the same way you speak to a sibling -- or a police officer. Similarly, a collection agency can't use the same language when reaching out to a top-earning corporate executive as they do when trying to collect from a single mother of five who's holding down two jobs. With AI, companies will be able to communicate with the tone, vocabulary and incentives most likely to engage the individual recipient -- automatically. Staying Within the Lines. There are myriad state and federal regulations designed to prevent abuses by over-zealous debt collectors. Unfortunately, the industry remains notorious for its use of off-hour phone calls, intimidating language, and other aggressive techniques to try and pry money out of resistant hands. Using sophisticated rules engines, collections agencies can now make sure that they stay on the right side of the law while still disseminating strong, effective communications. Personalized Solutions. If there is one industry where "one size fits all" makes no sense, it's debt collection. Every situation is different. Every debtor has his/her unique set of challenges. Counselors, working one-on-one with customers, may be able to work out win-win payment solutions, but this takes time, talent, training, sensitivity, creativity and money. For many companies, it's just not worth the investment. AI can make this ideal a practical reality. A truly "smart" tool can analyze and weigh literally thousands of payment scenarios in a second, offering up a solution perfectly tailored to the customer's cash flow and competing obligations. Learn as You Earn. Perhaps the biggest selling point about Artificial Intelligence is that it is, well, intelligent. AI does not more than blindly follow a set of instructions. It learns. It adapts. It grows. It improves. Each failure is a lesson. Each success is an insight. As AI becomes increasingly common in the debt collection industry, expect its efficacy to improve by the month. That not only means more cash in the hands of the companies to which it is owed, but also happier, more satisfied customers. Companies such as Intellaegis with their masterQueue product, have been working to streamline the skip tracing space for the past six years taking a smarter BigData approach to vehicle recovery and debt collection. And Experian, always a leader in financial risk and credit management, recently released two advanced tools designed to help businesses improve their collections results. eResolve is the first self-service platform to help consumers negotiate and resolve past due obligations. PowerCurve Collections brings together data, decisions, and the collections workflow in a single, unified system. These are just the first steps in what promises to be a genuine revolution for business collections as artificial intelligence, Big Data analysis, and predictive analytics combine to create automated workflows that work to the benefit of the creditor and the debtor. Businesses will receive more of the money they're owed. Customers will have less debt to manage. And best of all, no more of those annoying dinner-time phone calls.  

On May 9th we hosted an episode of Business Chat | Live on our YouTube channel and enjoyed an enlightening discussion with Gavin Harding, Senior Business Consultant with Experian Business Information Services. In our chat, Gavin shared highlights from his marketplace lending panel "Bridging The Gap: Reconnecting Investors with Marketplace Lenders in a Volatile World."   Gary: We'll get started here. Welcome everybody. My name is Gary Stockton and I'm with Experian Business Information Services and we're gonna do a Business Chat Live today focusing on marketplace lending. I'm happy to be joined by Gavin Harding, and he's a senior business consultant with our business information services team on the global consulting side of the business. And Gavin is out at the Experian Vision Conference, so good morning, Gavin, or good afternoon, I should say. Gavin: Well, it's a little of both. It's morning for you and afternoon form me. Hi, Gary. Gary: So you had hosted a panel discussion yesterday called Bridging the Gap: Reconnecting Marketplace Investors with Marketplace Lenders in a Volatile World. Who was on the panel with you? Gavin: Well we had a really good industry cross section. We had Nat Hoopes who is the executive director of the Marketplace Lending Association. We had Frank Rotman who is the founding partner of QED Investors. And we had Peter Renton, who is the co-founder of LendIt, probably the biggest online marketplace lending conference worldwide. Gary: Peter Renton, he has worked on the LendIt Conference, but also Lend Academy, right? That's a resource for marketplace lending. I listen to his podcast. Gavin: That's right. Gary: We've spoken to Pete a number of times, so he's quite the expert in that field. There's been, in terms of marketplace lending and the news, there has been some negative news around the industry in recent past. Is that something that came up? Gavin: Indeed it did. Over the last 12 to 18 months, there has been a spate of negative publicity. The industry in general, the media has in a way turned on the industry on the basis of a couple of events related to specific companies in the space. The good news is that while that negative publicity had a negative impact last year, it seems that the industry has rebounded. It seems that it was a watershed moment where the industry recommitted to transparency, where they enhanced their whole approach to risk, improved their approach to operations. So if we characterize last year as perhaps a low point, the general theme of the panel was that the industry's really poised for growth, has grown up a lot over the last year. And you know, we talked a lot about credibility and trust and so on, and Nat, from the Marketplace Lending Association, you know, obviously that group started about a year ago and it has now grown to 19 members, so pretty rapid growth. When we think about the 19 members, we estimated that that covers about 90-95% of the total volume of loans and credit facilities in the space. So Nat and his team worked hard on transparency, disclosure, harmonizing standards and so forth, so it was really good to have him on the panel. Gary: And Experian, are we a member of the MLA? Gavin: We are a proud associate member, yes we are. Gary: Excellent. So let's talk a little bit about bank partnerships and what are the kinds of things you were talking about related to bank partnerships? I'm sure that was a big part of the discussion. Gavin: It was. About 24, 36 months ago is when this topic became pretty hot. Lots of conversation between banks and players in the industry. Those conversations in some very high profile ways result in partnerships. We think about Chase, we think about OnDeck. As the year has progressed, what's started to happen is, the mood within the industry has changed. Banks now expect partners in this space to speak their language in terms of risk, to be fully compliant, to understand all the rules and regulations. So the short statement is that in the last year, within the online lending space, compliance has become a competitive advantage. Compliance and operational discipline has become a selling point. So again, that's part of the ongoing theme of the industry and the sector growing up and maturing, so really positive. The one comment that I believe Frank had was as we think about partnerships with the banks, be prepared to hear no a lot before you get to yes. Be prepared to translate between the two very distinct audiences. So in terms of working with banks, use their language, understand the regulations, understand what pressures and demands are on them, and the outcome of that will be a much higher success rate and much more positive, productive conversations. Gary: Excellent. How about the sector performance overall? Is it a growing sector? The banks, I would imagine they've expressed a lot of interest in that. Are we seeing growth in that sector? Gavin: Interesting question. We talked about some of the negative publicity last year. Some of that related to some practices in parts of the industry over the last two to three years, so what's happening now is, because of a refocus and redirection towards credit risk management putting out more and better loans for appropriate returns and so forth, we're seeing the whole industry performance has really been elevated. A lot of the perhaps substandard loans or facilities have now run off, run off meaning they've matured and have been paid off. And the new business that's been put on is more sustainable. It's a more disciplined approach. So yes, overall the sector has improved significantly in terms of performance over the last year. Gary: Excellent. And so, obviously you're meeting with plenty of Experian clients there at the conference. What is this, your third or fourth Vision? Gavin: This is my third and we are here with, I think it's a little over 500 of Experian's clients globally. Many of clients from Europe, Asia, and so on so it's a really great experience. Gary: Yeah, and I saw you had Steve Wozniak, co-founder of Apple Computer as one of your keynote speakers. Gavin: That's right, that's right. On Monday morning for our breakfast presentation, we had the Woz and the big news on that, Gary, is, I know this will probably startle any listeners, that apparently Steve Jobs was not always a very nice person. So that's a newsflash there. Gary: Brilliant guy, though. You can tell I'm a customer. Gavin: Fantastic. The innovation, the dynamism was just radiating from him. He talked about some of his rules of life and he said he was never interested in money, he was interested in thinking and creating things and making things work. Somebody said, "Steve, what motivated you when you were an employee at Hewlett Packard and how does that maybe translate into what we should be doing with employees?" And Steve Wozniak said the major attraction for him at Hewlett Packard was that they let him go into their stores, their inventory, and take whatever electronic parts and components he wanted to create his own products at night. So he would talk about going home, having dinner, and going back, going into the stores, grabbing the components, and then making the products. And some of the original pre-Apple I computers were made from Hewlett Packard parts in Steve Wozniak's - he said didn't actually have a garage. It was more of a basement, but in his house. So really an interesting presentation. A really dynamic guy. We were lucky to have him. Gary: And you also had, an economic presentation by Diane Swonk I think I saw. Gavin: Diane Swonk this morning, really interesting presentation. A little bit of a different perspective than what we often see in terms of the high-level economic factors like just raw unemployment versus full employment and so on. She dug a little bit deeper but beyond that she had a couple of key messages. One of the messages is that we are almost at, depending on definition, full employment. Wages have increased over the historical averages over the last couple of years. So while the broad improvement in the economy was visible, it's only now hitting our pocketbooks. It's only now coming through in consumer spending. So that was pretty positive. She has worked a lot with both the current and past administration in terms of economic advisors and committees and so on, done a lot of work in Washington, DC. She is very much taking a wait-and-see cautious approach in terms of what the administration is saying. She confirmed that the intent or the goal investing heavily in infrastructure should have a dramatic effect on the economy overall, so she was supportive of that. The one question she had, and actually what she said was that her son on the way to school in the morning on the back of a napkin should be able to work out what the plan is to spend and at the same time reduce taxes without the other side of the equation is, to be charitable, going to require further definition. Gary: Wow, sounds like quite a conference. I'm quite envious that I'm not there to enjoy it with you this time, but maybe next time. Gavin, I really appreciate you taking time out. I know that there's a lot of people that you should be meeting with there, so I'm gonna go ahead and maybe end it right there for now. Maybe we can schedule another business chat soon. I know something's coming up with Moody's Analytics and yourself in June and the next release of the Main Street Report for Q1, so I'm excited to maybe talk about that in further detail with you very soon. Gavin: I look forward to it. Thank you very much, Gary. Gary: All right. Thank you very much.   If you would like to be informed of new episodes of Business Chat | Live be sure to subscribe to our YouTube channel, and follow us on Twitter.    

Businesses are faced with the need to collect on delinquent accounts. When pursuing these past-due accounts, the most successful way to approach them is with a combination of perseverance, politeness, and professionalism. This serves the dual purpose of increasing the likelihood of receiving a prompt payment and also staying within the guidelines set forth by the Fair Debt Collections Practices Act. Perseverance While constantly calling a customer for payment can be a drag, perseverance will pay off—literally. Keep notes when calling the customer, detailing when you called, the time you called and if the customer promised payment. If payment was promised, make a note of when. Most software will have note-taking capabilities, so use that to keep track of whether the customer is following through with payments or not. Aim to call once a week to keep your company in the forefront of the customer's payable person. Politeness Being polite can be trying when the customer is being evasive about payment status. Remember the old adage of catching more flies with honey than vinegar. Being polite gives the customer less reason to avoid payment. Share a story or joke with them. Get familiar with the person doing the payments for your business. Avoid negative outbursts containing vulgar language or calling multiple times per day, which are both violations of FDCPA code. Professional Above all, remain professional. Do not allow emotion or personal feelings about the customer cloud your attitude. This is strictly business, and the customer who may be slow or evading payment would do the same to anyone else in your position. Talk to them about payment plans if they are having a hard time paying. If they are hesitant to pay, ask for the reason why. Is there an issue with the product or service your company offers? If there is a problem with the product, talk to product/servicesupport staff to see if they are aware of this issue. If they are, ask them to contact the client with the solution. Sometimes it is necessary to involve sales representatives while collecting. The sales rep can go in and play "Good Cop," letting the customer know that they would love to sell them more product or further service, but that there's a problem with the account that needs to be resolved with the customer's accounts payable department. This normally results in the procurement associate contacting the accounts payable department and asking why payment has not been made on the prior purchase. Thisfacilitate payment, and in turn, increase company cash flow. Using these techniques will reflect respect and courtesy, which in turn elicits goodwill with the customer. Business Chat | LIVE - Credit & Collections with Katie Keitch We had a great interview about best practices in B2B Collection with Katie Keich. Katie is the V.P. of Commercial Services at InsideARM. She shares how to drive a successful collections strategy in your credit department or through 3rd party collections. Learn more about InsideARM