Alternative Data

For decades, lenders and others have relied on core credit data focused on financial borrowing and repayment behavior. Many factors go into the decision-making process, including the length of credit history, the number of open accounts, and on-time bill payments. What happens when a consumer or small business owner relies on cash for financial transactions or has never held a mortgage or a business loan? A significant portion of the U.S. adult population faces this problem. According to a 2020 report by the Federal Reserve, as many as 21 percent of U.S. consumers survive without a credit card. As a result, the traditional credit-scoring model doesn't tell the full story of their financial health, and they could be labeled “credit invisible" or “unscored" due to limited access to credit. It's both a personal and business problem. For consumers, that might mean not being able to secure a mortgage, insurance, or even be considered for a job. Start-ups and small businesses, meanwhile, may not be able to access credit to fuel their future growth and success. The recent explosion of new business filings brings the challenge of credit access to a head. Because small and emerging businesses can lack sufficient credit histories to qualify for credit on their own, they may rely on the owner's personal credit profile for lending decisions. Yet, some small businesses continue to struggle to get financing. It's especially pronounced in communities of color. For example, Black-owned businesses get turned down for bank financing at twice the rate that white-owned businesses do, according to the Federal Reserve. Technological innovations such as Experian's DataShare ™, Experian Boost and Social Media Insight are shaking up the conventional scoring system by bringing in alternative credit factors to fill out the credit picture. For lenders, the emergence of alternative data, otherwise known as non-traditional data, helps them make informed business credit decisions among a wider number of customers and prospects. What is non-traditional data? Traditionally, lending and other credit decisions have been based on factors such as credit history and on-time payments. That can allow people and businesses that do not have a lot of cash but can demonstrate good repayment behavior to borrow. However, it's quite another story when the situation is flipped. Some consumers and businesses don't utilize financial products, though they might have healthy cash flow. As a result, they lack the necessary data to generate a credit score, making them appear to be unattractive credit risks. Non-traditional data is an important way to give consumers access to better rates and open up borrowing to more consumers and businesses. This data can include things like rent and cell phone payments, giving lenders a broader range of information to consider. According to FinRegLab, 96 percent of U.S. households have a bank account or a prepaid card and 91 percent of U.S. adults have at least one utility account in their names. Overall, including other credit data, in addition to core credit data, would bring more people into the credit system. Including non-traditional forms of data can create financial inclusion for consumers and businesses. Where does non-traditional data come from? Non-traditional data is generated by aggregators that scour utility accounts, public records and property information to understand the financial activities of consumers. It considers a wider range of financial behaviors than what just appears on a credit report. These can include: Rent payments Utility payments Employment verification Bank account information, including recurring payroll deposits, average account balances and withdrawal activity. Property records Non-traditional credit data goes one step further and supplements this data with information on consumers' use of alternative lending arrangements such as payday loans, small-dollar credit lenders, auto financiers, rent-to-own, retail financing, and others. Commercial lenders can also take advantage of technological innovations to gather non-traditional data for lending decisions, which can drive more approvals and greater profits. For businesses, non-traditional credit data can include: Social media: How many user check-ins and reviews is a business getting on social media? That can say a lot about its business. Experian Social Media Insight™ provides a social media view to help lenders better score business borrowers with thin credit profiles. Online financial activity: An uptick in PayPal or Venmo transactions can suggest healthy cash flow. Bank details: Borrowers can permit lenders to view their business banking account, which shows how much cash on hand they have. Accounting software: With direct access to QuickBooks or FreshBooks, lenders can make determinations about a potential customer's financial health in real-time. Shipping information: For businesses moving products, analyzing shipping data allows lenders to make assumptions on cash flow. How is non-traditional data currently used? Financial institutions have become receptive to other credit data sources to provide additional insights. That can improve the accuracy of credit scoring and allow lenders to find more creditworthy consumers. According to Experian's 2020 State of Alternative Credit Data report, 96 percent of lenders believe that during times of economic stress, non-traditional credit data allows them to more closely evaluate consumers' creditworthiness and reduce their credit risk exposure. By deeming more consumers creditworthy, financial institutions can increase financial inclusion, while uncovering new lending opportunities for themselves. Modern tools make that possible. For example, Experian's Clarity Services provides insights on more than 62 million U.S. consumers, helping lenders better assess and manage risk. Lenders can see consumers' utilization of alternative finance and payment behaviors for a more holistic view of their creditworthiness. Likewise, Experian's DataShare™ is a non-traditional digital solution, which can be used to make commercial lending decisions. It provides owner-permissioned data to expedite new client onboarding and increase retention by bringing digital transformation to document gathering. With real-time financial data, lenders can understand the trends in their customers' financial positions to better assess risk. How can non-traditional data be used to calculate credit risk? Non-traditional credit data can help lenders gain deeper insights into their borrowers to better assess risk. For starters, it allows them to spot creditworthiness trends in real-time, rather than a snapshot in time that traditional credit data typically provides. A deteriorating financial position among prime customers and signs of improvement among marginal customers can be spotted faster with a combination of traditional and non-traditional data. Also, some consumers may appear to be “risky" through the lens of core credit data but may prove less so when non-traditional data points are included. For example, according to FinRegLab research, cash flow data can be predictive of credit risk, not just credit utilization and history. Owner-permissioned data lets consumers decide what lenders can see when making their credit determinations. For instance, lenders who use only traditional data might see an account that has been turned over to collections. With owner-permissioned data, on the other hand, a lender can also see a record of paying rent and cell phone bills on time. As a result, lenders can evaluate both types of behaviors in their credit decision, providing them with a fuller picture. Looking at how consumers leverage alternative financial products to manage debt can also reveal responsible credit-management behaviors. Consumers who appear to be low-risk in the eyes of traditional credit data may actually be riskier if they do not manage their alternative finance products well – an activity that doesn't appear on most credit reports. The challenges of non-traditional credit data Non-traditional credit data has the potential to open the world of credit to underserved communities. For lenders, it can unlock opportunities by bringing in a wider range of potential customers. But it's important to recognize that there are challenges too. For starters, lenders are still figuring out how to incorporate it into their lending decisions. While non-traditional credit data has always been available, big data collection now makes it easier to access. As lenders and regulators become more comfortable with its use, they will begin to incorporate it into credit decisions, while also being aware of its limitations. Consumers, meanwhile, may have data security and privacy concerns about how their information will be used and who may have access to it. The Consumer Financial Protection Bureau is working on guidelines that ensure that lenders are using data appropriately and fairly. Taking the next step Ready to see how non-traditional credit data can drive more business while also reaching your diversity, equity and inclusion goals? Modern tools simplify the process and bring a more holistic view to inform the lending decision. Learn more about how you can expand your customer base and improve profitability with non-traditional credit data. Learn more about Experian DataShare Related Posts

Subscribe To Our YouTube Channel [Gary]: I'm joined by Yvon Desieux, a Senior Director in our product team, and Yvon is responsible for product strategy and driving innovation in lending through the creation of products and applications for commercial lenders.  Sitting next to Yvon is Mike Myers, our Vice President of Product Management here in Business Information, and he leads the product team and AgileWorks Innovation Lab. Welcome to Business Chat, gentlemen. The COVID-19 pandemic spurred a wave of innovation in e-commerce, in restaurant delivery services, in FinTech, digital has raised the stakes. There's an expectation with consumers and businesses that client experiences should be on an even playing field across industries. But when it comes to applying for loans, particularly on the commercial side, those processes can be quite manual. One of the things that speed things along is real-time, financial statement data. What is real-time financial statement data? [Yvon]: Well, simply, financial statement data in real-time is just when lenders are able to access accounting software via the Internet, to power their lending decisions, and our new product offering Experian Datashare, provides this capability with the power of owner permission data. With this permissioning, small business owners are empowered with the ability to leverage their own financial data, to apply for the loans and other services that they need. So, unlike a credit report, permissioning puts the owner in control of what transactional data they share, to qualify for the loans and lines of credit that they need to keep their businesses running. So this consented data provides personalization balanced with privacy for businesses and growth, balanced with accountability for the lenders. What problem is real-time financial statement data solving for lenders? [Yvon]: The feedback that we've received falls into four-tier categories. It's revenue growth creating operational efficiencies, decreasing risk, and creating a better client experience through a modern digital journey that's easy to understand and delivers faster decisions and quicker access to the financing that the small businesses need. Mike, when you talk to clients about digital transformation, what are the things that are keeping them up at night? What are they most concerned with? [Mike]: I think all of our clients, to some degree, are going through a technical transformation, or we often call a path to modernization, and Experian is in the same boat. You know, it's hard to stay on top of technology and really leverage the cloud and be able to get new products, new services, new capabilities to market quickly. Some of the biggest challenges our clients expressed to us are how do we operate in what's become a very different environment over the last year, year, and a half with COVID. Things have moved at a much more rapid pace, as far as digitization. So the interactions with their clients have changed. It's become a bit more impersonal. It's become a bit more quick and with a sense of urgency, and many clients are struggling to do everything online and do it at a breakneck speed. This is often where API's and with different technologies, we can keep pace and help our clients integrate data, access data, and ultimately render decisions to their end-users in a much quicker and more time-efficient way. Can you talk a little bit about Experian's API Hub and getting access to our data? [Mike]: The API economy has been here for many years, and our clients are integrating our data and, you know, putting it into their systems so their users can access data real-time. And Datashare is one of those services where this data can be integrated into our client's systems. And there's no drop-off, there's no manual, or swivel chair type activity where you're going to multiple systems. It becomes a much more efficient process. And not only the client wins, but also an applicant gets a much more rapid decision and can go ahead and power their business. Can financial statement data play a role in helping emerging and underserved businesses grow? If so, how? [Yvon]: Yeah, for new and small businesses that haven't yet established business credit and rely on the owner's personal credit profile for lending decisions, Datashare gives them the ability to share their financial statements and show the financial health of the business. This expanded data can be used, in the decision-making process in addition to the standard bureau data to create more approvals. By permissioning data, these businesses are able to move out of an unscorable or subprime, hard money loan bracket,  into a space that helps them qualify for more traditional loans and lines of credit, with better rates in terms. [Gary]: Mike, I've got another slide here; businesses of color, have been severely impacted by COVID. You can see some of these stats here. 30% of black business owners say that access to credit is the biggest challenge in the next 12 months. 47% said they didn't apply for financing because they did not think they would be approved, only 37% received all of the financing they sought. Recently on a CBA webinar with a Experian, Janelle Williams from the Atlanta Fed was saying that 83% of the PPP loans went to white-owned businesses, but only 1.9% went to businesses of color. How can Experian DataShare help underserved businesses of color? [Mike]: Yeah, Gary, you're really touching on a key point here. You know, small businesses, power our economy. They make up the majority of businesses out there. And based on that recent stat you and Yvon just discussed about new business startups. There's more than ever, and we all have to do a better job of making sure there's an equal playing field when it comes to accessing capital, whether it's trade credit or financial packages, to help them manage their cash flow. And that became more evident than ever again, with the pandemic and the sudden shift in the economy. It became more and more challenging for many small businesses to manage their cash flow, pay their employees, and really see a path forward. So data share is exciting in a number of ways. What excites me is that it now combines a historical process that was done in a much more offline manual way. And now can be done in real-time. And if you start combining that with historical payment information, historical public record filings, in addition to real-time financials, you have a winning combination that can provide a clear view of a small business's financial status and real-time view. So not only the historical, which is a great way to predict future payment behavior, but also the most current accounts receivable accounts payable information that can really help you understand what their future holds. How can DataShare deepen the relationships between lenders and the small businesses that they serve? [Yvon]: Datashare drives operational efficiency. So, it allows the lenders to receive the financial statement data in a standardized format. This is regardless of the size of the business or the source of the accounting data. It drastically reduces the level of manual effort required to underwrite a loan. And it automates much of the time spent on the administrative tasks associated with the lending process. So this means that relationship managers and underwriters and credit teams are going to spend less time creating reports and gathering documents and chasing clients. And they're going to be able to spend more time holding,  we hope, value-driven conversations to deepen the relationships they have with their clients and help those businesses grow and expand in line with their needs. [Gary]: So those small businesses are going to experience greater efficiency, ease of doing business with their lender. The lender has more time to devote and work more closely with those clients, but also maybe the underserved businesses, the unscored businesses, they have that additional insight to see really how that business is doing and how they can grow that relationship with them. Am I right? [Yvon]: Yeah, absolutely. Time is the big commodity, and speed kills. So what Datashare allows lenders to do is negate all of the wasted time and effort spent onboarding and processing clients. And they can dig into that live transactional data, and get to understand the business, and perhaps share insights with the business owners that even the owners don't know. So, it allows both sides to work more efficiently and more profitably. [Gary]: Are you seeing a change in perception on the part of the business owners of today? I mean, a lot of the Millennial business owners they're used to mobile technology, they're used to delivery services on-demand services. Do you see a change in perception in permissioning, access to financial statement data? [Yvon]: Yeah, actually, we have. We typically see adoption rates as high as 90% when it comes to small businesses that are actively looking for financing. So these are motivated clients who typically go with a lender that can provide the quickest time to cash, and Datashare typically cuts down that time to about 65%. We live in a rapidly developing world where digital adoption is at an all-time high, and the same is true for small businesses. As we shift from the stacks of paper and filing cabinets, both lenders and borrowers see the benefits of leveraging technology to make their organizations more efficient and profitable. The annual review process is one pain point that we've heard a lot from clients. Is this something that could help clients minimize some of that pressure, that's a real stressor on bandwidth? [Yvon]: Yeah, absolutely. Datashare automates the monthly, quarterly, or annual review process. So when a small business permissions their data, lenders are able to choose the data refresh frequency, that is also permissioned. So, combined with covenant monitoring Datashare is able to flag accounts that fall out of an agreed risk threshold, which saves the credit team valuable time having to review their entire book of business and allows them to strategically focus on problem accounts and mitigate risk before it gets out of hand. [Gary]: So, any further thoughts on how Experian DataShare can help lenders or the small businesses they serve? [Yvon]: Yeah. Our focus will continue to be on finding ways to automate processes and provide insights into the financial statement data that we acquire. We'll continue to receive feedback from the clients that we've partnered with and integrate that into our roadmap to create new products and services that will benefit both lenders and the borrowers that they serve. [Mike]: Yeah. I would add Gary that we're at a really interesting time. You know, there's the speed and the in-depth view of a small business's financials at our client's fingertips that can help equal that playing field and open up financial opportunities for businesses of all shapes, sizes, and colors, and Experian Datashare helps with that. Combine that with our historical trade and public record data, and you've got a winning solution for both our clients who are offering the financial vehicles, as well as the applicants, the small businesses out there that need help. And that historically may have been on the outside looking in. Now they have an opportunity to share their financial situation, a picture that can help them move their business forward and access capital when they most need it. [Gary]: Well, this has been super informative, guys. I want to thank you for taking the time to come on Business Chat. And if you would like to learn more about how real-time financial statement data can help small businesses, Experian recently published a free ebook perspective paper. We'll provide a link and a QR code for you to download. If you have any questions, of course, feel free to reach out to your Experian account representative to get a conversation started about helping small businesses grow through innovative solutions like Experian DataShare. Thank you very much, gentlemen. Download Perspective Paper Learn more about alternative data Related Posts

In the wake of the Coronavirus Pandemic, thousands of companies were forced to go digital, transforming brick and mortar experiences to mobile-enabled, touchless digital experiences.  Whether you were a small grocery chain or a family restaurant getting plugged into a myriad of takeout ordering platforms, the choice was simple, upgrade to a fully digital experience or go extinct. When the $2.2 trillion CARES act passed in March of 2020, and with it the $350 billion Paycheck Protection Program, many banks had to work quickly to transform their SMB lending process to be more data-driven, risk-proof, scalable, and ready to deploy in a matter of weeks, rather than months. The Unqork no-code solution offers a flexible alternative. There’s a new breed of solutions that make it possible for banks to build robust, mission-critical applications without using a single line of code. Unqork is the leading no-code enterprise application development platform. With Unqork, you can manage no-code application development throughout the entire Software Development Lifecycle without having to implement traditional coding efforts, so you can move faster at a lower cost with fewer errors to future-proof your business. The Unqork platform makes it easy to power applications with Experian data using API’s. You can build powerful digital experiences without the scripting and coding you would normally expect. Curious? Watch our recent Business Chat interview with Unqork below. Digital Transformation with No-Code & API's | Business Chat Interview Transcription We interviewed Ben Smith, Head of Banking with Unqork and Carl Stronach, Senior Product Manager with Experian met during a recent Business Chat about No-Code for Enterprise Financial Services. What follows is a lightly edited transcription of their talk. [Gary]: Hello and welcome to Business Chat. So happy you could join us today. I'm Gary Stockton with Experian; I'm with Business Information Services here in North America.  We would love to know where you're joining us from. We're streaming here from Costa Mesa, California; we're live on LinkedIn and other channels via Restream. Be sure to drop us a comment and hashtag #teamlive if you're watching us live, hashtag #teamreplay, if you're catching this on the replay, and remember sharing, is caring. We would love it if you can share this chat. If you could let your colleagues know that we're talking about APIs and No-Code by sharing this live stream, that would help us expand our audience. So they were going to be talking about no-code technology and Experian API's with two great experts. Joining us from Unqork is Ben Smith. He's the head of banking, and from Experian is Carl Stronach. He's a Senior Product Manager here at Experian, and he works on API's. Welcome gentlemen. Ben, if you could take a moment, please tell us a bit about Unqork and your mission, where you're based, and how you got started. [Ben]: So we were founded in 2017 by Gary Hoberman. Gary was the CIO of MetLife, and Gary had a mission to redefine software development and focus on delivering software at the enterprise-grade faster with a lower total cost of ownership and something that could be delivered by a number of different people, not necessarily people who had a significant development talent and experience. So Gary set out in 2017 to redefine how we do it. We are a no-code platform. We are totally cloud-based and agnostic. We are deployed in over ten countries with over 70 different clients. And the other thing, part of the mission that we have here around the development is we've trained over 10,000 experts globally who can develop on the platform because we believe that the no-code environment allows for rapid adoption, and we want that adoption to be significant. [Ben]: So, what it says here is we have three major investors; we have a number of other ones. Obviously, BlackRock, Google, and Goldman Sachs are all major investors. And then, as I alluded to earlier, the mission of the firm is to develop enterprise-grade no-code solutions. So you can see at the bottom of this slide some of our major customers as well. [Gary]: Carl, could you share a little bit about your role here at Experian you've been at Experian quite a while, and how you work with companies like Unqork? [Carl]: So I've been with Experian for almost seven years, I'm focused on new product development. For the last four years, I have been focused on our APIs and bringing Experian business information into our global developer portal. In that time I've worked with a countless number of banks and FI's, and many of our clients across our verticals in their integration with Experian. In terms of how they are going to get our data in the most efficient way. I've supported a lot of them from the business side and the IT side and kind of sat in on both. And I've seen many of our clients really succeed with their integrations with us. That's just a direct integration to our rest API, and others, you know, take a long time. [Carl]: So I'm sensitive to the fact that coding to APIs as easy as we can try to make them with a rest API, and as easy as we can try to make them by adding SDKs or, or other supporting information on top, it's still difficult and time-consuming. A lot of the time to code to APIs certainly gets much more complex as we get into regulated data.  So it's definitely something that we want to narrow the timeline strategically.  How do we get access to data and query it faster than ever before?  Strategically it's something we're interested in and excited to be a part of, and working with providers like Unqork allows us to unlock some of those technologies. [Gary]: So Ben, what's the distinction between low-code and no-code, and what drives the adoption of no-code technology? [Ben]: The main difference is that everything that we develop on Unqork does not have any native code to it.  So for you, as a developer, it's a complete visual system. And the most important thing is there's no need to maintain the code once you've written it. So even in low code environments, there is, of course, the upkeep of the code, and ultimately it becomes legacy. Whereas in our system, all of our customers are on the same platform using the same environment, or sorry, using the same software to develop their solutions. And they're always up to date. That's a big difference, there's no need to develop that last bit, and there's no need to maintain it once it's out because as soon as you write a bit of code, you've got to maintain that code going forward. [Ben]: To the second point, how are people adopting it?  We see it adopted across a number of use cases. So, for exactly that reason. Many in my world as Head of Banks, many of our customers in the banking sector are looking for ways to develop both customer-facing as well as internal-facing software that digitizes their workflows, whether that be onboarding, operations. It just depends on the needs of that particular bank. But again, the rapid development, the ability to get to market faster and the ability to not have to maintain that codebase once it's up and running have been a really powerful part of our value statement. [Gary]: Carl, switching to data and API's. You work with a lot of clients in the banking industry. Can you tell me where in the customer life cycle does Experian API's fall? [Carl]: It's really across the lifecycle. From campaign targeting and finding new customers to underwriting and account acquisition and customer management, even collections. It's really across the full spectrum. To take a step back.  Everyone thinks of Experian as the consumer credit bureau. And, I am a very big fan of John Sina. So I think that's how Experian is generally known. But Experian's business goes well beyond just consumer credit. Obviously, we have business credit, and that's our focus here. But when it comes to our APIs, we bring everything together into a single global developer portal. So, what you can do through a single developer account is an interface with all Experian information, and we source data internally. So we've got our North America Business Information, Consumer Information, Automotive, Data, Quality, Decisioning, you name it, it's all available in one place. Also, we have an International focus too. So if you go there, you'll see API's from the UK, India, Singapore, all across the globe. We really try to be that shop for Experian data, making it much easier to code to us and eliminate those silos that used to exist in our own internal legacy systems. [Carl]: Now, I'm really excited by some of the things that Unqork can do. When we talk about setting up one workflow that can be shared many times and doesn't have to be re-coded over and over and over again, we see the same in working with our customers. When we work with our banking customers, a lot of them execute the same exact workflows to get to Experian data. Maybe the data they need is different. Maybe the data they find predictive is different, but it's really a lot of the same workflows. And so, as we work with Unqork we can define more of these workflows, make them predefined and hopefully just speed time to market. Really eliminate a lot of the burdens with a new integration or basically offer a new product and get it out. [Gary]: So you're finding that customers are applying these new technologies to get to market faster. I have to imagine that that was fairly active during COVID. A lot of people spinning up shopping carts and people that have brick-and-mortar stores had to innovate faster. And would you agree that platforms like Unqork are helping make that possible with API's? [Carl]: Absolutely, so that's even a part of what we're trying to do as well. As small businesses have had to transform due to COVID, they've had to adopt more digital experiences and maybe they had to. It's a restaurant and they had to change their storefront from having tables and chairs to having just a counter and offering delivery, opening up the restaurant to more kitchen space, to handle a greater number of orders coming in. I think we are also trying to capture new data assets that can tap into that business's digital transformation. So, we've done a lot to acquire more online data on businesses, more social media data on businesses, to tap into understanding what that business activity is. Are they open? Are they closed due to COVID? And so, as we start to adopt those new data sources, our clients also face the challenge of discovering them, integrating them into their services. [Gary]: Excellent. So, a two-part question for you Ben.  How are banks deploying no code and, and are there any security considerations when using a no-code platform? [Ben]: I think you know what we do here at Unqork for some of our customers, and what Unqork provides is the capability to both design a bank-specific user experience, but in a rapid way to deploy digitally. To solve problems that are rising quickly. PPP is a good example of that and other ones. Going forward, the ability to integrate with places like Experian on different data types such as social and some of the other ones that Carl spoke of. I think will be very important in terms of how banks redefine their small business and business offerings because post-COVID we're all going to be trying to figure out how to serve that segment in a way that makes sense from both a credit and a service point of view. [Gary]: Excellent. So, Carl what challenges are you seeing with lenders adopting and integrating bureau and non-traditional data? I mean, non-traditional is a hot space right now. [Carl]: Yeah. So, I think one of the challenges is just discovering the data and defining it, and being able to start working with it.  I think we experienced that, even internally, so there are just so many different data sources out there. How do you really prioritize what to go after? Having it available in a single place is really key.  If you had to continually define data and bring it into your database in order to work with it, it just becomes very challenging.  We need to find and adopt technologies that take that burden away from our customers. Gary, we can't expect every customer to define the data source. We need to do it for them and technologies like Unqork, give us the ability to do that. And so, I'm excited by that part. If we can lower the burden there, it can unleash data analysts and data scientists to really find out which data might be predictive.  So a lot of our customers want to find data that's going to be predictive of credit risk, predictive of delinquency. We need to find ways that allow them to really focus their time on finding the data, what data is actually going to be predictive. I don't want to spend all my time just defining the data just so I can test the top, a couple of fields that I have a hunch on. I want to go deeper and really find that marginal value. And technology is the key enabler that lets us do that. So go into the data. [Gary]: Thank you, Carl. So, Ben, based on what we just heard from Carl, can you share some examples of how SMB lenders can fast-track lending applications? [Ben]: Sure. We're working with banks around both customer onboarding and also around, the product development, into the origination cycle.  I think what Carl's saying is right. To the extent that we can discover this data and get it at a deeper level, get it into the risk modeling infrastructure,  through the integrations that we, as a platform can build, allows for more rapid adoption of alternative data sources. But also, better credit decisioning, you know, particularly as I sort of feel passionately about a post-COVID world and the need to take a different view as to how that credit risk moves or how credit risk is assessed. [Gary]: Well this has been very interesting guys. And folks, if you would like to learn more about no-code and how to fast track applications and integrate with Experian API's, Unqork is hosting a webinar March 24th at 12 Eastern. Experian is going to be participating in that, we're very excited to participate.  If you would like to register, you can just point your phone at the QR code or go to the link that we have there. We'll leave that in the description for this video, if you want to come back to this later. And, by all means, if you have any questions drop them in the comments. We'll be monitoring the comments in the next few days and replying to those. I want to thank both of you guys for taking time out today. I know you're both extremely busy, and looking forward to chatting with you again soon and looking forward to the webinar on the 24th. Watch Webinar Unqork + Experian: Smarter Small Business Lending  

When insurance underwriters make mistakes, bad policies can cost billions. Alternative forms of data is helping change those outcomes, particularly for insurance providers in helping them identify blind spots and accurately underwrite policies. Watch our special Insurance-focused webinar titled "Beyond Credit Risk - Understanding Alternative Data" with HazardHub. Heath Foley and Carl Stronach from Experian is joined by Bob Frady from HazardHub during this lively discussion. Alternative sources of data are growing in importance in the market. The key to our data platform is constantly investing and sourcing a wider variety of data such as geographic hazards, social media, and OSHA data in order to represent a fuller picture of the health of the business.  In this one hour talk, we walk through: Utilizing property-level hazard risk assessments The growing importance of alternative sources of data How to bring superior data to power comprehensive insights Related information What is alternative and non-traditional data/

A gastropub restaurant applies for business insurance and is approved. However, social media insights show the restaurant is declining. Even though underwriters usually take a quick look at social media postings, evaluating the trends of the business is not part of the decision process. Costly mistakes: Underwriting using only business supplied information How could something as basic as a business in decline be overlooked in the insurance underwriting process? Think about the process when reviewing a new business insurance application. The underwriter reviews the application and looks at traditional credit and public filing information. Although the underwriter checks out the company website, he doesn’t meet or interact with the company. He then must make a potentially costly business decision about its risk level. Even though the process appears thorough, it does not use the new wealth of information available. How social media provides information about business health If the insurance company had used unique and new sources of social media data, the underwriter would have seen a different picture of the restaurant. The trends in the number of reviews point to a declining business due to poor service, bland food, or increased competition.  Traditional data sources miss these subtle signs that point to a higher risk of going out of business. While one poor review shouldn’t result in a denial, a pattern of a declining business is important. This can be spotted using tools that analyze the trends in reviews and ratings for the business line. After all you cannot compare restaurants, with high volumes of social media postings, with say a dry cleaner. By correctly using social media data during the underwriting process, insurers can give an additional lift on the model to determine the risk. Social media data can also help determine more information about the business. For example, an exercise gym may have treadmills and weight machines, or it might actually be a kickboxing studio, which has a much higher level of risk and premiums. Underwriters also get a much more granular view than a typical application, such as the parking situation and the hours. Because risk is higher for businesses with a liquor license, insurers can often learn if a bar didn’t disclose this on their application. Customer photos also often tell a story not detectable on the application, such as broken stairs or a fireplace without proper screens. Using artificial intelligence to analyze social media data Looking through social media for each application takes large amounts of time. Even more importantly, humans may be subject to bias and miss word patterns in reviews. By using an artificial intelligence tool with machine learning capability to analyze social media data for business insurance applications, underwriters can gain a much more accurate picture of the risk they are assuming by insuring a business. Additionally, an AI tool can analyze business health much more quickly than an underwriter could doing the social media check manually. Insurance companies that use artificial intelligence tools to analyze social media data during the underwriting process can more accurately predict the risk of a business. Because the processing speed, adding this additional step does not slow the process down. By reviewing what other people are saying about the business, your insurance company can decrease risk and save money on claims.

For lenders, alternative data can be the factor in edging out your competitors, especially when better decisions are needed to compete for emerging businesses and startups. Both startups and emerging businesses may represent a good growth opportunity, but they may also be high risk. The challenge? Businesses with thin credit profiles can be difficult to score. Social Media Insight TM provides lenders with another layer of data that can help you better assess the direction of these businesses, score them more accurately and open new growth opportunities. After all, nobody likes to leave money on the table. For emerging businesses who have a thin credit profile but have a strong social media reputation, Social Media Insight can be a factor in gaining access to credit and resources they deserve. Social Media Insight enables you to see the activity, trends and sentiment on a business, over time. In our Experian DataLab tests, we improved overall model performance by 12 percent and new and emerging businesses by 91 percent, boosting predictive performance over traditional data sets. Social Media Insight is directly sourced data providing you with over 70 attributes including trends and sentiment along with descriptive attributes. This powerful data enables you to more accurately score or assess new and emerging business as well as long established accounts. Want to learn more? Watch our on-demand webinar or contact your Experian representative today.    

The Consumer Financial Protection Bureau (CFPB) is engaged in increasing its understanding of the opportunities and potential challenges associated with consumer permissioned account data. The agency launched a request for information on the topic in November 2016 and is currently analyzing information it received prior to the February 2017 comment deadline. In remarksat a field hearing in conjunction with the launch of the RFI, CFPB Director Cordray stated, that "access to digital financial records is critical. As with your student records or medical records, your financial records tell an important story about you. With health care, for example, if you can see your records, it is easier to participate.” Demand for financial account data goes beyond consumer loans and its use in the small business credit-granting process has been increasing. Experian recently entered a partnership with Finicity to develop new tools that will make it easier for small businesses to apply for a loan and to accelerate loan underwriting. These tools are used for authentication, verification of income and assets, and cash flow analysis. These tools improve accuracy and reduce fraud risk for lenders, thereby broadening access to loans. Experian's new Digital Verification Solutions leverage Finicity's data aggregation and insight platform. Experian is the first credit bureau to implement this technology, which gives small businesses the opportunity to secure loans with less paperwork and hassle by connecting with financial institutions digitally. While this information is currently limited in use for credit risk analyses for small business lending, Experian believes that user-permissioned account aggregation platforms will increasingly provide an opportunity to collect and analyze cash flow and recurring payment information relevant to lenders in making credit decisions. For example, user-permissioned data from a businesses’ bank account could demonstrate the entity’s payment history for utilities and telecommunications services, as well as for monthly rent. With respect to the collection and use of data obtained through account data aggregation platforms, it is important that a borrower grant permission pursuant to clear statements about how the consumer's information will be accessed and how the data will be used. Such statements should include whether the data will be shared with third-parties, and for what purposes. It is vital for market participants — both financial institutions and account aggregators — to continue to work together to develop cooperative agreements that allow data to be accessed, analyzed and shared in an efficient and secure environment. Recently, several account aggregators have formed direct agreements with financial institutions and there is ongoing work to develop best practices and industry standards for secure consumer and business access to financial data. In the past, the CFPB has promoted this ecosystem. As with other commercial credit data, financial account data can be used to make decisions throughout the credit lifecycle. This includes supporting pre-qualification when a business is prospecting for new customers; conducting credit risk analysis and verifications; managing portfolio risk; and if necessary, collecting on unpaid or overdue debts.

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.    

This year’s Marketplace Lending and Investing Conference explored issues of transparency, partnership, consistency and sustainability.  There was healthy debate on each of these topics and the audience, presenters and panelists frequently returned to the theme of the relationship between Marketplace Lenders, Fintech, Banks and Investors.  As the conference unfolded I thought about the role of small businesses in the relationship between these stakeholders.  How do mom and pop small businesses fit into these complex, rapidly evolving relationships? In mid-2015 The Federal Reserve of Cleveland published a report.  The title was “Alternative Lending Through The Eyes of ‘Mom & Pop’ Small-Business Owners: Findings from Online Focus Groups”.  The report found that the small business owners participating in the online focus groups had a number of common concerns: Marketplace Lenders’ sites are attractive … but how secure? How private is the information the small business provides? It is difficult to compare product offerings, features and pricing The small business owners bank is a source of advice but is not necessarily considered as an option for funding There are some clear parallels with the conference’s focus on transparency, partnership and sustainability.  See if any of these sound familiar: Regulators at the federal and state level are researching the Marketplace Lending industry and exploring ways and means of regulating the space. They are particularly focusing on issues of disclosure, fairness, privacy and governance.  The CFPB – Consumer Financial Protection Bureau has been particularly active.  There are two recent examples that illustrate increasing protection for small businesses. Dwolla was hit with a $100,000 fine in March of 2016, directly related to data security practices.  Then, in late September LendUp was fined $3.5 million for deceiving its customers.  The list of lenders who have strayed from fair and transparent business practices is long and growing.  Fortunately, regulatory supervision of the online marketplace is here to stay. Banks largely abandoned the small business segment post 2008. Lack of profitability is most often cited as the reason for the exodus.  Marketplace Lenders entered the space, delivered a wide range of product offerings, high levels of responsiveness and a relatively painless customer experience.   Now, eight years later, banks and Marketplace Lenders are partnering to make the most of their relative strengths – deep customer relationships and the capability to deliver exceptional customer choice and experience, through technology. Leaders in the various stakeholder organizations are still focused on surviving, meeting goals for growth, managing risk and optimizing returns.  In the past these may have conflicted with the small business owners interests.    In late 2016, they are in alignment … and that is good news for small business owners throughout the US economy. If you would like to hear more of what I learned at Marketplace Lending and Investing, check out the Live Marketplace Lending & Investing Q&A I recorded from the conference.