Credit Lending

Every morning, I wake up and walk bleary eyed to the bathroom, pop in my contacts and start my usual routine. Did I always have contacts? No. But putting on my contacts and seeing clearly has become part of my routine. After getting used to contacts, wearing glasses pales in comparison. This is how I view alternative credit data in lending. Are you having qualms about using this new data set? I get it, it’s like sticking a contact into your eye for the first time: painful and frustrating because you’re not sure what to do. To relieve you of the guesswork, we’ve compiled the top four myths related to this new data set to provide an in-depth view as to why this data is an essential supplement to your traditional credit file. Myth 1: Alternative credit data is not relevant. As consumers are shifting to new ways of gaining credit, it’s important for the industry to keep up. These data types are being captured by specialty credit bureaus. Gone are the days when alternative financing only included the payday store on the street corner. Alternative financing now expands to loans such as online installment, rent-to-own, point-of-sale financing, and auto-title loans. Consumers automatically default to the financing source familiar to them – which doesn’t necessarily mean traditional financial institutions. For example, some consumers may not walk into a bank branch anymore to get a loan, instead they may search online for the best rates, find a completely digital experience and get approved without ever leaving their couches. Alternative credit data gives you a lens into this activity. Myth 2: Borrowers with little to no traditional credit history are high risk. A common misconception of a thin-file borrower is that they may be high risk. According to the CFPB, roughly 45 million Americans have little to no credit history and this group may contain minority consumers or those from low income neighborhoods. However, they also may contain recent immigrants or young consumers who haven’t had exposure to traditional credit products. According to recent findings, one in five U.S. consumers has an alternative financial services data hit– some of these are even in the exceptional or very good credit segments. Myth 3: Alternative credit data is inaccurate and has poor data quality. On the contrary, this data set is collected, aggregated and verified in the same way as traditional credit data. Some sources of data, such as rental payments, are monthly and create a consistent look at a consumer’s financial behaviors. Experian’s Clarity Services, the leading source of alternative finance data, reports their consumer information, which includes application information and bank account data, as 99.9% accurate. Myth 4: Using alternative credit data might be harmful to the consumer. This data enables a more complete view of a consumer’s credit behavior for lenders, and provides consumers the opportunity to establish and maintain a credit profile. As with all information, consumers will be assessed appropriately based on what the data shows about their credit worthiness. Alternative credit data provides a better risk lens to the lender and consumers may get more access and approval for products that they want and deserve. In fact, a recent Experian survey found 71% of lenders believe alternative credit data will help consumers who would have previously been declined. Like putting in a new pair of contact lenses the first time, it may be uncomfortable to figure out the best use for alternative credit data in your daily rhythm. But once it’s added, it’s undeniable the difference it makes in your day-to-day decisions and suddenly you wonder how you’ve survived without it so long. See your consumers clearly today with alternative credit data. Learn More About Alternative Credit Data

Picking up where we left off, online fintech lenders face the same challenges as other financial institutions; however, they continue to push the speed of evolution and are early adopters across the board. Here’s a continuation of my conversation with Gavin Harding, Senior Business Consultant at Experian. (Be sure to read part 1.) Part two of a two-part series: As with many new innovations, fintechs are early adopters of alternative data. How are these firms using alt data and what are the results that are being achieved? In a competitive market, alternative data can be the key to helping fintechs lend deeper and better reach underserved consumers. By augmenting traditional credit data, a lender has access to greater insights on how a thin-file consumer will perform over time, and can then make a credit decision based on the identified risk. This is an important point. While alternative data often helps lenders expand their universe, it can also provide quantitative risk measures that traditional data doesn’t necessarily provide. For example, alternative data can recognize that a consumer who changes residences more than once every two years presents a higher credit risk. Another way fintechs are using alternative data is to screen for fraud. Fraudsters are digitally savvy and are using technology to initiate fraud attacks on a broader array of lenders, in bigger volumes than ever before. If I am a consumer who wants to get a loan through an online fintech lender, the first thing the lender wants to know is that I am who I say I am. The lender will ask me a series of questions and use traditional data to validate. Alternative data takes authentication a step further and allows lenders to not only identify what device I am using to complete the application, but whether the device is connected to my personal account records – giving them greater confidence in validating my identity. A second example of using alternative data to screen for fraud has to do with the way an application is actually completed. Most individuals who complete an online application will do so in a logical, sequential order. Fraudsters fall outside of these norms – and identifying these patterns can help lenders increase fraud detection. Lastly, alternative data can help fintech lenders with servicing and collections by way of utilizing behavioral analytics. If a consumer has a history of making payments on time, a lender may be apt to approve more credit, at better terms. As the consumer begins to pay back the credit advance, the lender can see the internal re-payment history and recommend incremental line increases. From your perspective, what is the future of data and what should fintechs consider as they evolve their products? The most sophisticated, most successful “think tanks” have two things that are evolving rapidly together: Data: Fintechs want all possible data, from a quality source, as close to real-time as possible. The industry has moved from “data sets” to “data lakes” to “data oceans,” and now to “data universes.” Analytics: Fintechs are creating ever-more sophisticated analytics and are incorporating machine learning and artificial intelligence into their strategies. Fintechs will continue to look for data assets that will help them reach the consumer. And to the degree that there is a return on the data investment, they will continue to capitalize on innovative solutions – such as alternative data.   In the competitive financial marketplace, insight is everything. Aite Group recently conducted a new report about alternative data that dives into new qualitative research collected by the firm. Join us to hear Aite Group’s findings about fintechs, banks, and credit unions at their webinar on December 4. Register today! Register for the Webinar Click here for more information about Experian’s Alternative Data solutions. Don’t forget to check out part one of this series here.   About Gavin Harding With more than 20 years in banking and finance Gavin leverages his expertise to develop sophisticated data and analytical solutions to problem solve and define strategies across the customer lifecycle for banking and fintech clients. For more than half of his career Gavin held senior leadership positions with a large regional bank, gaining experience in commercial and small business strategy, SBA lending, credit and risk management and sales. Gavin has guided organizations through strategic change initiatives and regulatory and supervisory oversight issues. Previously Gavin worked in the business leasing, agricultural and construction equipment sectors in sales and credit management roles.

In 2011, data scientists and credit risk managers finally found an appropriate analogy to explain what we do for a living. “You know Moneyball? What Paul DePodesta and Billy Beane did for the Oakland A’s, I do for XYZ Bank.” You probably remember the story: Oakland had to squeeze the most value out of its limited budget for hiring free agents, so it used analytics — the new baseball “sabermetrics” created by Bill James — to make data-driven decisions that were counterintuitive to the experienced scouts. Michael Lewis told the story in a book that was an incredible bestseller and led to a hit movie. The year after the movie was made, Harvard Business Review declared that data science was “the sexiest job of the 21st century.” Coincidence?   The importance of data Moneyball emphasized the recognition, through sabermetrics, that certain players’ abilities had been undervalued. In Travis Sawchik’s bestseller Big Data Baseball: Math, Miracles, and the End of a 20-Year Losing Streak, he notes that the analysis would not have been possible without the data. Early visionaries, including John Dewan, began collecting baseball data at games all over the country in a volunteer program called Project Scoresheet. Eventually they were collecting a million data points per season. In a similar fashion, credit data pioneers, such as TRW’s Simon Ramo, began systematically compiling basic credit information into credit files in the 1960s. Recognizing that data quality is the key to insights and decision-making and responding to the demand for objective data, Dewan formed two companies — Sports Team Analysis and Tracking Systems (STATS) and Baseball Info Solutions (BIS). It seems quaint now, but those companies collected and cleaned data using a small army of video scouts with stopwatches. Now data is collected in real time using systems from Pitch F/X and the radar tracking system Statcast to provide insights that were never possible before. It’s hard to find a news article about Game 1 of this year’s World Series that doesn’t discuss the launch angle or exit velocity of Eduardo Núñez’s home run, but just a couple of years ago, neither statistic was even measured. Teams use proprietary biometric data to keep players healthy for games. Even neurological monitoring promises to provide new insights and may lead to changes in the game. Similarly, lenders are finding that so-called “nontraditional data” can open up credit to consumers who might have been unable to borrow money in the past. This includes nontraditional Fair Credit Reporting Act (FCRA)–compliant data on recurring payments such as rent and utilities, checking and savings transactions, and payments to alternative lenders like payday and short-term loans. Newer fintech lenders are innovating constantly — using permissioned, behavioral and social data to make it easier for their customers to open accounts and borrow money. Similarly, some modern banks use techniques that go far beyond passwords and even multifactor authentication to verify their customers’ identities online. For example, identifying consumers through their mobile device can improve the user experience greatly. Some lenders are even using behavioral biometrics to improve their online and mobile customer service practices.   Continuously improving analytics Bill James and his colleagues developed a statistic called wins above replacement (WAR) that summarized the value of a player as a single number. WAR was never intended to be a perfect summary of a player’s value, but it’s very convenient to have a single number to rank players. Using the same mindset, early credit risk managers developed credit scores that summarized applicants’ risk based on their credit history at a single point in time. Just as WAR is only one measure of a player’s abilities, good credit managers understand that a traditional credit score is an imperfect summary of a borrower’s credit history. Newer scores, such as VantageScore® credit scores, are based on a broader view of applicants’ credit history, such as credit attributes that reflect how their financial situation has changed over time. More sophisticated financial institutions, though, don’t rely on a single score. They use a variety of data attributes and scores in their lending strategies. Just a few years ago, simply using data to choose players was a novel idea. Now new measures such as defense-independent pitching statistics drive changes on the field. Sabermetrics, once defined as the application of statistical analysis to evaluate and compare the performance of individual players, has evolved to be much more comprehensive. It now encompasses the statistical study of nearly all in-game baseball activities.   A wide variety of data-driven decisions Sabermetrics began being used for recruiting players in the 1980’s. Today it’s used on the field as well as in the back office. Big Data Baseball gives the example of the “Ted Williams shift,” a defensive technique that was seldom used between 1950 and 2010. In the world after Moneyball, it has become ubiquitous. Likewise, pitchers alter their arm positions and velocity based on data — not only to throw more strikes, but also to prevent injuries. Similarly, when credit scores were first introduced, they were used only in originations. Lenders established a credit score cutoff that was appropriate for their risk appetite and used it for approving and declining applications. Now lenders are using Experian’s advanced analytics in a variety of ways that the credit scoring pioneers might never have imagined: Improving the account opening experience — for example, by reducing friction online Detecting identity theft and synthetic identities Anticipating bust-out activity and other first-party fraud Issuing the right offer to each prescreened customer Optimizing interest rates Reviewing and adjusting credit lines Optimizing collections   Analytics is no substitute for wisdom Data scientists like those at Experian remind me that in banking, as in baseball, predictive analytics is never perfect. What keeps finance so interesting is the inherent unpredictability of the economy and human behavior. Likewise, the play on the field determines who wins each ball game: anything can happen. Rob Neyer’s book Power Ball: Anatomy of a Modern Baseball Game quotes the Houston Astros director of decision sciences: “Sometimes it’s just about reminding yourself that you’re not so smart.”  

This is an exciting time to work in big data analytics. Here at Experian, we have more than 2 petabytes of data in the United States alone. In the past few years, because of high data volume, more computing power and the availability of open-source code algorithms, my colleagues and I have watched excitedly as more and more companies are getting into machine learning. We’ve observed the growth of competition sites like Kaggle, open-source code sharing sites like GitHub and various machine learning (ML) data repositories. We’ve noticed that on Kaggle, two algorithms win over and over at supervised learning competitions: If the data is well-structured, teams that use Gradient Boosting Machines (GBM) seem to win. For unstructured data, teams that use neural networks win pretty often. Modeling is both an art and a science. Those winning teams tend to be good at what the machine learning people call feature generation and what we credit scoring people called attribute generation. We have nearly 1,000 expert data scientists in more than 12 countries, many of whom are experts in traditional consumer risk models — techniques such as linear regression, logistic regression, survival analysis, CART (classification and regression trees) and CHAID analysis. So naturally I’ve thought about how GBM could apply in our world. Credit scoring is not quite like a machine learning contest. We have to be sure our decisions are fair and explainable and that any scoring algorithm will generalize to new customer populations and stay stable over time. Increasingly, clients are sending us their data to see what we could do with newer machine learning techniques. We combine their data with our bureau data and even third-party data, we use our world-class attributes and develop custom attributes, and we see what comes out. It’s fun — like getting paid to enter a Kaggle competition! For one financial institution, GBM armed with our patented attributes found a nearly 5 percent lift in KS when compared with traditional statistics. At Experian, we use Extreme Gradient Boosting (XGBoost) implementation of GBM that, out of the box, has regularization features we use to prevent overfitting. But it’s missing some features that we and our clients count on in risk scoring. Our Experian DataLabs team worked with our Decision Analytics team to figure out how to make it work in the real world. We found answers for a couple of important issues: Monotonicity — Risk managers count on the ability to impose what we call monotonicity. In application scoring, applications with better attribute values should score as lower risk than applications with worse values. For example, if consumer Adrienne has fewer delinquent accounts on her credit report than consumer Bill, all other things being equal, Adrienne’s machine learning score should indicate lower risk than Bill’s score. Explainability — We were able to adapt a fairly standard “Adverse Action” methodology from logistic regression to work with GBM. There has been enough enthusiasm around our results that we’ve just turned it into a standard benchmarking service. We help clients appreciate the potential for these new machine learning algorithms by evaluating them on their own data. Over time, the acceptance and use of machine learning techniques will become commonplace among model developers as well as internal validation groups and regulators. Whether you’re a data scientist looking for a cool place to work or a risk manager who wants help evaluating the latest techniques, check out our weekly data science video chats and podcasts.

There’s no shortage of buzz around fintechs shifting from marketplace challengers to industry collaborators. Regardless of fintech’s general reputation as market disruptors, a case can certainly be made for building partnerships with traditional financial institutions by leveraging the individual strengths of each organization. According to the World FinTech Report 2018, 75.5% of fintechs surveyed selected “collaborate with traditional firms” as their main objective. Whereas fintechs have agility, a singular focus on the customer, and an absence of legacy systems, traditional Financial Institutions have embedded infrastructure, scale, reach, and are well-versed with regulatory requirements. By partnering together, fintechs and other Financial Institutions can combine strengths to generate real business results and impact the customer experience. New stories are emerging – stories that illustrate positive outcomes beyond efforts exerted by one side alone. A recent report sponsored by Experian and conducted by the Filene Research Institute further explores the results of fintech and traditional FI partnerships by examining the experiences of six organizations: The outcomes of these relationships are sure to encourage more collaborative partnerships. And while leveraging each organization’s strength is a critical component, there’s much more to consider when developing a strategic approach. In the fast-moving, disruptive world of fintech, just what are the key elements to building a successful collaboration with traditional Financial Institutions? Click here to learn more. More Info on Marketplace Lending Read the Filene Report

Fintechs take on banks, technology, and finance as we know It. In the credit space, their reputation as a market disruptor precedes their definition. But now, as they infiltrate headlines and traditional finance as many know it – serving up consumer-centric, convenience-touting, access-for-all online marketplace lending – fintechs aren’t just becoming a mainstay within the financial spectrum’s vernacular. With their increasing foothold in the marketplace, they are here and they are gaining momentum. Since their initial entry to the marketplace in 2006, these technology-driven online platforms flaunt big data, actionable analytics and originations growing at exponential rates. Fintechs hang their hats on their ability to be the “anti-bank” of sorts. The brainchild of finance plus technology, their brands promise simple but powerful deliverables – all centered on innovation. And they market themselves as filling in the gaps commonly accepted as standard practices by traditional financial institutions. Think paperwork, less-than-instant turnaround times, a history of unwavering tradition, etc. Fintechs deliver a one-two punch, serving the marketplace as both lending companies and technology gurus – two pieces that financial institutions want and consumers crave. Now, as they grow more prominent within the marketplace, some are starting to pivot to test strategic partnerships and bring their strengths – technological infrastructure, speed and agility – to credit unions and other traditional financial institutions. According to the World FinTech Report 2018, 75.5% of fintechs surveyed want to collaborate with traditional financial services firms. The challenge, is that both fintechs and traditional financial institutions struggle with finding the right partners, efficiently working together and effectively scaling innovation. From competitors to collaborators, how can fintechs and traditional institutions strike a partnership balance? A recent report sponsored by Experian and conducted by the Filene Research Institute, explores this conundrum by examining the experiences of six financial institutions – some fintechs and some traditional FIs – as they seek to collaborate under the common goal of better serving customers. The results offer up key ingredients for fostering a successful collaboration between fintechs and traditional financial institutions – to generate real impact to the customer experience and the bottom-line. Rest assured, that in the fast-moving, disruptive world of fintech, effective partnerships such as these will continue to push boundaries and redefine the evolving financial services marketplace. Learn More About Online Marketplace Lending Download the Filene Report

Unsecured lending is increasing. And everyone wants in. Not only are the number of personal loans increasing, but the share of those loans originated by fintech companies is increasing. According to Experian statistics, in August 2015, 890 new trades were originated by fintechs (or 21% of all personal loans). Two years later, in August 2017, 1.1 million trades belonged to fintechs (making up 36% of trades). This increase is consistent over time even though the spread of average loan amount between traditional loans and fintech is tightening. While convenience and the ability to apply online are key, interest rates are the number one factor in choosing a lender. Although average interest rates for traditional loans have stabilized, fintech interest rates continue to shift higher – and yet, the upward momentum in fintech loan origination continues. So, who are the consumers taking these loans? A common misconception about fintechs is that their association with market disruption, innovation and technology means that they appeal vastly to the Millennial masses. But that’s not necessarily the case. Boomers represent the second largest group utilizing fintech Marketplace loans and, interestingly, Boomers’ average loan amount is higher than any other generational group – 85.9% higher, in fact, from their Millennial counterparts. The reality is the personal loan market is fast-paced and consumers across the generational spectrum appear eager to adopt convenience-based, technology-driven online lending methods – something to the tune of $35.7 million in trades. For more lending insights and statistics, download Experian’s Q2 2018 Personal Loans Infographic here.   Learn More About Online Marketplace Lending Download Lending Insights

If your company is like many financial institutions, it’s likely the discussion around big data and financial analytics has been an ongoing conversation. For many financial institutions, data isn’t the problem, but rather what could or should be done with it. Research has shown that only about 30% of financial institutions are successfully leveraging their data to generate actionable insights, and customers are noticing. According to a recent study from Capgemini,  30% of US customers and 26% of UK customers feel like their financial institutions understand their needs. No matter how much data you have, it’s essentially just ones and zeroes if you’re not using it. So how do banks, credit unions, and other financial institutions who capture and consume vast amounts of data use that data to innovate, improve the customer experience and stay competitive? The answer, you could say, is written in the sand. The most forward-thinking financial institutions are turning to analytical environments, also known as a sandbox, to solve the business problem of big data. Like the name suggests, a sandbox is an environment that contains all the materials and tools one might need to create, build, and collaborate around their data. A sandbox gives data-savvy banks, credit unions and FinTechs access to depersonalized credit data from across the country. Using custom dashboards and data visualization tools, they can manipulate the data with predictive models for different micro and macro-level scenarios. The added value of a sandbox is that it becomes a one-stop shop data tool for the entire enterprise. This saves the time normally required in the back and forth of acquiring data for a specific to a project or particular data sets. The best systems utilize the latest open source technology in artificial intelligence and machine learning to deliver intelligence that can inform regional trends, consumer insights and highlight market opportunities. From industry benchmarking to market entry and expansion research and campaign performance to vintage analysis, reject inferencing and much more. An analytical sandbox gives you the data to create actionable analytics and insights across the enterprise right when you need it, not months later. The result is the ability to empower your customers to make financial decisions when, where and how they want. Keeping them happy keeps your financial institution relevant and competitive. Isn’t it time to put your data to work for you? Learn more about how Experian can solve your big data problems. >> Interested to see a live demo of the Ascend Sandbox? Register today for our webinar “Big Data Can Lead to Even Bigger ROI with the Ascend Sandbox.”

Big Data is no longer a new concept. Once thought to be an overhyped buzzword, it now underpins and drives billions in dollars of revenue across nearly every industry. But there are still companies who are not fully leveraging the value of their big data and that’s a big problem. In a recent study, Experian and Forrester surveyed nearly 600 business executives in charge of enterprise risk, analytics, customer data and fraud management. The results were surprising: while 78% of organizations said they have made recent investments in advanced analytics, like the proverbial strategic plan sitting in a binder on a shelf, only 29% felt they were successfully using these investments to combine data sources to gather more insights. Moreover, 40% of respondents said they still rely on instinct and subjectivity when making decisions. While gut feeling and industry experience should be a part of your decision-making process, without data and models to verify or challenge your assumptions, you’re taking a big risk with bigger operations budgets and revenue targets. Meanwhile, customer habits and demands are quickly evolving beyond a fundamental level. The proliferation of mobile and online environments are driving a paradigm shift to omnichannel banking in the financial sector and with it, an expectation for a customized but also digitized customer experience. Financial institutions have to be ready to respond to and anticipate these changes to not only gain new customers but also retain current customers. Moreover, you can bet that your competition is already thinking about how they can respond to this shift and better leverage their data and analytics for increased customer acquisition and engagement, share of wallet and overall reach. According to a recent Accenture study, 79% of enterprise executives agree that companies that fail to embrace big data will lose their competitive position and could face extinction. What are you doing to help solve the business problem around big data and stay competitive in your company?

With Hispanic Heritage Awareness Month underway and strategic planning season in full swing, the topic of growing membership continues to take front stage for credit unions. Miriam De Dios Woodward (CEO of Coopera Consulting) is an expert on the Hispanic opportunity, working with credit unions to help them grow by expanding the communities they serve. I asked Miriam if she could provide her considerations for credit unions looking to further differentiate their offerings and service levels in 2019 and beyond.   There’s never been a better time for credit unions to start (or grow) Hispanic engagement as a differentiation strategy. Lending deeper to this community is one key way to do just that. Financial institutions that don’t will find it increasingly difficult to grow their membership, deposits and loan balances. As you begin your 2019 strategic planning discussions, consider how your credit union could make serving the Hispanic market a differentiation strategy. Below are nine ways to start. 1.  Understand your current membership and market through segmentation and analytics. The first step in reaching Hispanics in your community is understanding who they are and what they need. Segment your existing membership and market to determine how many are Hispanic, as well as their language preferences. Use this segmentation to set a baseline for growth of your Hispanic growth strategy, measure ongoing progress and develop new marketing and product strategies. If you don’t have the bandwidth and resources to conduct this segmentation in-house, seek partners to help. 2.  Determine the product gaps that exist and where you can deepen relationships. After you understand your current Hispanic membership and market, you will want to identify opportunities to improve the member experience, including your lending program. For example, if you notice Hispanics are not obtaining mortgages at the same rate as non-Hispanics, look at ways to bridge the gaps and address the root causes (i.e., more first-time homebuyer education and more collaboration with culturally relevant providers across the homebuying experience). Also, consider how you might adapt personal loans to meet the needs of consumers, such as paying for immigration expenses or emergencies with family in Latin America. 3.  Explore alternative credit scoring models. Many credit products accessible to underserved consumers feature one-size-fits-all rates and fees, which means they aren’t priced according to risk. Just because a consumer is unscoreable by most traditional credit scoring models doesn’t mean he or she won’t be able to pay back a loan or does not have a payment history. Several alternative models available today can help  lenders better evaluate a consumer’s ability to repay. Alternative sources of consumer data, such as utility records, cell phone payments, medical payments, insurance payments, remittance receipts, direct deposit histories and more, can be used to build better risk models. Armed with this information – and with the proper programs in place to ensure compliance with regulatory requirements and privacy laws – credit unions can continue making responsible lending decisions and grow their portfolio while better serving the underserved. 4.  Consider how you can help more Hispanic members realize their desire to become homeowners. In 2017, more than 167,000 Hispanics purchased a first home, taking the total number of Hispanic homeowners to nearly 7.5 million (46.2 percent of Hispanic households). Hispanics are the only demographic to have increased their rate of homeownership for the last three consecutive years. What’s more, 9 percent of Hispanics are planning to buy a house in the next 12 months, compared to 6 percent of non-Hispanics. This means Hispanics, who represent about 18 percent of the U.S. population, may represent 22 percent of all new home buyers in the next year. By offering a variety of home loan options supported by culturally relevant education, credit unions can help more Hispanics realize the dream of homeownership.   5.  Go beyond indirect lending for auto loans. The number of cars purchased by Hispanics in the U.S. is projected to double in the period between 2010 and 2020. It’s estimated that new car sales to Hispanics will grow by 8 percent over the next five years, compared to a 2 percent decline among the total market. Consider connecting with local car dealers that serve the Hispanic market. Build a pre-car buying relationship with members rather than waiting until after they’ve made their decision. Connect with them after they’ve made the purchase, as well.   6.  Consider how you can help Hispanic entrepreneurs and small business owners. Hispanics are nine times more likely than whites to take out a small business loan in the next five years. Invest in products and resources to help Hispanic entrepreneurs, such as small business-friendly loans, microloans, Individual Taxpayer Identification Number (ITIN) loans, credit-building loans and small-business financial education. Also, consider partnering with organizations that offer small business assistance, such as local Hispanic chambers of commerce and small business incubators.   7.  Rethink your credit card offerings. Credit card spending among underserved consumers has grown rapidly for several consecutive years. The Center for Financial Services Innovation (CFSI) estimates underserved consumers will spend $37.6 billion on retail credit cards, $8.3 billion on subprime credit cards and $0.4 billion on secured credit cards in 2018. Consider mapping out a strategy to evolve your credit card offerings in a way most likely to benefit the unique underserved populations in your market. Finding success with a credit-builder product like a secured card isn’t a quick fix. Issuers must take the necessary steps to comply with several regulations, including Ability to Repay rules. Cards and marketing teams will need to collaborate closely to execute sales, communication and, importantly, cardmember education plans. There must also be a good program in place for graduating cardmembers into appropriate products as their improving credit profiles warrant. If offering rewards-based products, ensure the rewards include culturally relevant offerings. Work with your credit card providers.­   8.  Don’t forget about lines of credit. Traditional credit lines are often overlooked as product offerings for Hispanic consumers. These products can provide flexible funding opportunities for a variety of uses such as making home improvements, helping family abroad with emergencies, preparing families for kids entering college and other expenses. Members who are homeowners and have equity in their homes have a potential untapped source to borrow cash.   9.  Get innovative. Hispanic consumers are twice as likely to research financial products and services using mobile apps. Many fintech companies have developed apps to help Hispanics meet immediate financial needs, such as paying off debt and saving for short-term goals. Others encourage long-term financial planning. Still other startups have developed new plans that are basically mini-loans shoppers can take out for specific purchases when checking out at stores and online sites that participate. Consider how your credit union might partner with innovative fintech companies like these to offer relevant, digital financial services to Hispanics in your community.   Next Steps Although there’s more to a robust Hispanic outreach program than we can fit in one article, credit unions that bring the nine topics highlighted above to their 2019 strategic planning sessions will be in an outstanding position to differentiate themselves through Hispanic engagement.   Experian is proud to be the only credit bureau with a team 100% dedicated to the Credit Union movement and sharing industry best practices from experts like Miriam De Dios Woodward. Our continued focus is providing solutions that enable credit unions to continue to grow, protect and serve their field of membership. We can provide a more complete view of members and potential members credit behavior with alternative credit data. By pulling in new data sources that include alternative financing, utility and rental payments, Experian provides credit unions a more holistic picture, helping to improve credit access and decisioning for millions of consumers who may otherwise be overlooked.   About Miriam De Dios Woodward Miriam De Dios Woodward is the CEO of Coopera, a strategy consulting firm that helps credit unions and other organizations reach and serve the Hispanic market as an opportunity for growth and financial inclusion. She was named a 2016 Woman to Watch by Credit Union Times and 2015 Latino Business Person of the Year by the League of United Latin American Citizens of Iowa. Miriam earned her bachelor’s degree from Iowa State University, her MBA from the University of Iowa and is a graduate of Harvard Business School’s Leading Change and Organizational Renewal executive program.

Last Updated: January 2019 Traditional credit data has long been the end-all-be-all ruling the financial services space. Like the staple black suit or that little black dress in your closet, it’s been the quintessential go-to for decades. Sure, the financial industry has some seasonality, but traditional credit data has reigned supreme as the reliable pillar. It’s dependable. And for a long time, it’s all there was to the equation. But as with finance, fashion and all things – evolution has occurred. Specifically, how consumers are managing their money has evolved, which calls for deeper insights that are still defensible and disputable. Alternative credit data is the new black. Alternative credit data is increasingly integrated in credit talks for lenders across the country. Much like that LBD, it is becoming a lending staple – that closet (or portfolio) must-have – to leverage for better decisioning when determining credit worthiness. So, what is alternative credit data? In our data-driven industry, “alternative” data as a whole may best be summed up as FCRA-compliant credit data that is not typically included in traditional credit reports. For traditional data, think loan and inquiry data on bankcards, auto, mortgage and personal loans; typically trades with a term of 12 months or greater. Some examples of alternative credit data include alternative financial services data, rental data, full-file public records and account aggregation. These insights can ultimately improve credit access and decisioning for millions of consumers who may otherwise be overlooked. Alternative or not, every bit of information counts – and consumers are willing to share this data. An Experian survey revealed that 70% of consumers are willing to provide additional financial information to a lender if it increases their chance for approval or improves their interest rate for a mortgage or car loan. In addition, the data also revealed that 71% of lenders believe consumers will increasingly allow access to their data for lending decisions if they are empowered to turn it on and off. FCRA-compliant, user permissioned data allows lenders to easily verify assets and income electronically without consumer permission, thereby giving lenders more confidence in their decision and allowing consumers to gain access to lower-cost financing. From a risk management perspective, alternative credit data can also help identify riskier consumers, by identifying information like the number of payday loans acquired within a year, number of first-payment defaults, number of inquiries within the past 30-90 days and overall stability of an applicant. Alternative credit data can give supplemental insight into a consumer’s stability, ability and willingness to repay that is not available on a traditional credit report that can help lenders avoid risk or price accordingly. From closet finds that refresh your look to that LBD, alternative credit data gives lenders more transparency into their consumers, and gives consumers seeking credit a greater foundation to help their case for creditworthiness. It really is this season’s – and every season’s – must-have. Get Started Today

Machine learning (ML), the newest buzzword, has swept into the lexicon and captured the interest of us all. Its recent, widespread popularity has stemmed mainly from the consumer perspective. Whether it’s virtual assistants, self-driving cars or romantic matchmaking, ML has rapidly positioned itself into the mainstream. Though ML may appear to be a new technology, its use in commercial applications has been around for some time. In fact, many of the data scientists and statisticians at Experian are considered pioneers in the field of ML, going back decades. Our team has developed numerous products and processes leveraging ML, from our world-class consumer fraud and ID protection to producing credit data products like our Trended 3DTM attributes. In fact, we were just highlighted in the Wall Street Journal for how we’re using machine learning to improve our internal IT performance. ML’s ability to consume vast amounts of data to uncover patterns and deliver results that are not humanly possible otherwise is what makes it unique and applicable to so many fields. This predictive power has now sparked interest in the credit risk industry. Unlike fraud detection, where ML is well-established and used extensively, credit risk modeling has until recently taken a cautionary approach to adopting newer ML algorithms. Because of regulatory scrutiny and perceived lack of transparency, ML hasn’t experienced the broad acceptance as some of credit risk modeling’s more utilized applications. When it comes to credit risk models, delivering the most predictive score is not the only consideration for a model’s viability. Modelers must be able to explain and detail the model’s logic, or its “thought process,” for calculating the final score. This means taking steps to ensure the model’s compliance with the Equal Credit Opportunity Act, which forbids discriminatory lending practices. Federal laws also require adverse action responses to be sent by the lender if a consumer’s credit application has been declined. This requires the model must be able to highlight the top reasons for a less than optimal score. And so, while ML may be able to deliver the best predictive accuracy, its ability to explain how the results are generated has always been a concern. ML has been stigmatized as a “black box,” where data mysteriously gets transformed into the final predictions without a clear explanation of how. However, this is changing. Depending on the ML algorithm applied to credit risk modeling, we’ve found risk models can offer the same transparency as more traditional methods such as logistic regression. For example, gradient boosting machines (GBMs) are designed as a predictive model built from a sequence of several decision tree submodels. The very nature of GBMs’ decision tree design allows statisticians to explain the logic behind the model’s predictive behavior. We believe model governance teams and regulators in the United States may become comfortable with this approach more quickly than with deep learning or neural network algorithms. Since GBMs are represented as sets of decision trees that can be explained, while neural networks are represented as long sets of cryptic numbers that are much harder to document, manage and understand. In future blog posts, we’ll discuss the GBM algorithm in more detail and how we’re using its predictability and transparency to maximize credit risk decisioning for our clients.

Millennials have been accused of “killing” a lot of things. From napkins and fabric softener to cable and golf, the generation which makes up the largest population of the United States (aka Gen Y) is cutting a lot of cords. Despite homeowning being listed as one of the notorious generational group’s casualties, it’s one area that millennials want to keep alive, according to recent statistics. In fact, a new Experian study revealed 86% of millennials believe that buying a house is a good financial investment. However, only 15% have a mortgage today. One explanation for this gap may be that they appear too risky. Younger millennials (age 22-28) have an average near prime score of 652 and older millennials (age 29-35) have a prime score of 665. Both subsets fall below the average VantageScore® credit score* of U.S. consumers – 677. Yes, with the majority of millennials having near prime or worse credit scores, we can agree that they will need need to improve their financial hygiene to improve their overall credit rankings. But their dreams of homeownership have not yet been dashed. Seemingly high aspirations (of homeownership), disrupted by a reality of limited assets, low scores, and thin credit files, create a disconnect that suggests a lack of resources to get into their first homes – rather than a lack of interest. Or, maybe not. Maybe, after surviving a few first-time credit benders that followed soon after opening the floodgates to credit, millennials feel like the combination of low scores and the inability to get any credit is only salt in their wounds from their lending growing pains. Or maybe it’s all the student loans. Or maybe it’s the fact that so many of them are underemployed. But maybe there’s still more to the story. This emerging generation is known for having high expectations for change and better frictionless experiences in all areas of their life. It turns out, their borrowing behavior is no different. Recent research by Experian reveals consumers who use alternative financial services (AFS) are 11 years younger on average than those that do not. What’s the attraction? Financial technology companies have contributed to the explosive growth of AFS lenders and millennials are attracted to those online interactions. The problem is many of these trades are alternative finance products and are not reported to traditional credit bureaus. This means they do nothing to build credit experience in the eyes of traditional lenders and millennials with good credit history find it difficult to get access to credit well into their 20s. Alternative credit data provides a deeper dive into consumers, revealing their transactions and ability to pay as evidenced by alternative finance data, rental, utility and telecom payments. Alt data may make some millennials more favorable to lenders by revealing that their three-digit credit score (or lack there of) may not be indicative of their financial stability. By incorporating alternative financial services data (think convenient, tech-forward lenders that check all the boxes for bank removed millennials, not just payday loan recipients), credit-challenged millennials have a chance at earning recognition for their experience with alternative financial services that may help them get their first mortgage. Society may have preconceived notions about millennials, but lenders may want to consider giving them a second look when it comes to determining creditworthiness. In a national Experian survey, 53% of consumers said they believe some of these alternative sources would have a positive effect on their credit score. We all grow up sometime and as our needs change, there may come a day when millennials need more traditional financial services. Lenders who take a traditional view of risk may miss out on opportunities that alternative credit data brings to light. As lending continues to evolve, combining both traditional credit scores and alternative credit data appears to offer a potentially sweet (or rather, home sweet home) solution for you and your customers.   *Calculated on the VantageScore® credit score model. Your VantageScore® credit score from Experian indicates your credit risk level and is not used by all lenders, so don't be surprised if your lender uses a score that's different from your VantageScore® credit score.

Consumer confidence is nearing an 18-year high. Unemployment figures are at record lows. Retail spend is healthy, and expected to stay that way through the back-to-school and holiday shopping booms. Translation for credit card issuers? The swiping and spending continue. In fact, credit card openings were up 4% in the first quarter of 2018 compared to the same time last year, and card utilization is hovering around 20.5%. Even with the Fed’s gradual 2018 rate hikes, consumers are shopping. In a new Mintel report, outstanding credit card debt is now $1.03 trillion (as of the end of Q1, 2018), and the number of consumers with credit cards is growing fastest among people aged 18 to 34. In the retail card arena specifically, boomers and Gen X’ers are leading the charge, opening 45% and 27% of new retails cards, respectively. “A stronger economy always bodes well for credit cards,” said Kelley Motley, director of analytics for Experian. “Now is the time for card issuers to zero in on their most loyal consumers and ensure they are treating them with the right offers, rewards and premium benefits.” Consumer data reveals the top incentives when selecting a rewards-based card includes cash back, gas rewards and retail cards (including travel rewards and airfare). In fact, for younger consumers, offering rewards has proven to be the most effective way to get them to switch from debit to credit cards. Cash back was the most preferred reward for consumers aged 18 to 44 when asked about their motivation to apply for a new card. For individuals 45 and older, 0% interest was the top motivator. Of course, beyond credit card opens, the ideal is to engage with the consumers who are utilizing the card the most. From a segmentation standpoint, the loyal retail cardholder has an average VantageScore® credit score of 671 with an average total balance of $1,633. They use the card regularly and consistently make payments. Finding more loyalists is the goal and can be achieved with informed segmentation insights and targeted prescreen campaigns. On the flip side, insights can inform card issuers with data, helping them to avoid wasting marketing dollars on consumers who merely want to game a quick credit card offer and then close an account. A batch and blast marketing approach no longer works in the credit card marketing game. “Consumers expect you to know them and their financial needs,” said Paul DeSaulniers, senior director of Experian’s segmentation solutions. “The data exists and tells you exactly who to target and how to structure the offer – you just need to execute.”

As more financial institutions express interest and leverage alternative credit data sources to decision and assess consumers, lenders want to be assured of how they can best utilize this data source and maintain compliance. Experian recently interviewed Philip Bohi, Vice President for Compliance Education for the American Financial Services Association (AFSA), to learn more about his perspective on this topic, as well as to gain insights on what lenders should consider as they dive into the world of alternative credit data. Alternative data continues to be a hot topic in the financial services space. How have you seen it evolve over the past few years? It’s hard to pinpoint where it began, but it has been interesting to observe how technology firms and people have changed our perceptions of the value and use of data in recent years. Earlier, a company’s data was just the information needed to conduct business. It seems like people are waking up to the realization that their business data can be useful internally, as well as to others.  And we have come to understand how previously disregarded data can be profoundly valuable. These insights provide a lot of new opportunities, but also new questions.  I would also say that the scope of alternative credit data use has changed.  A few years ago, alternative credit data was a tool to largely address the thin- and no-file consumer. More recently, we’ve seen it can provide a lift across the credit spectrum. We recently conducted a survey with lenders and 23% of respondents cited “complying with laws and regulations” as the top barrier to utilizing alternative data. Why do you think this is the case? What are the top concerns you hear from lenders as it relates to compliance on this topic? The consumer finance industry is very focused on compliance, because failure to maintain compliance can kill a business, either directly through fines and expenses, or through reputation damage. Concerns about alternative data come from a lack of familiarity. There is uncertainty about acquiring the data, using the data, safeguarding the data, selling the data, etc. Companies want to feel confident that they know where the limits are in creating, acquiring, using, storing and selling data. Alternative data is a broad term. When it comes to utilizing it for making a credit decision, what types of alternative data can actually be used?  Currently the scope is somewhat limited. I would describe the alternative data elements as being analogous to traditional credit data. Alternative data includes rent payments, utility payments, cell phone payments, bank deposits, and similar records. These provide important insights into whether a given consumer is keeping up with financial obligations. And most importantly, we are seeing that the particular types of obligations reflected in alternative data reflect the spending habits of people whose traditional credit files are thin or non-existent.  This is a good thing, as alternative data captures consumers who are paying their bills consistently earlier than traditional data does. Serving those customers is a great opportunity. If a lender wants to begin utilizing alternative credit data, what must they know from a compliance standpoint? I would begin with considering what the lender’s goal is and letting that guide how it will explore using alternative data. For some companies, accessing credit scores that include some degree of alternative data along with traditional data elements is enough. Just doing that provides a good business benefit without introducing a lot of additional risk as compared to using traditional credit score information. If the company wants to start leveraging its own customer data for its own purposes, or making it available to third parties, that becomes complex very quickly.  A company can find itself subject to all the regulatory burdens of a credit-reporting agency very quickly. In any case, the entire lifecycle of the data has to be considered, along with how the data will be protected when the data is “at rest,” “in use,” or “in transit.” Alternative data used for credit assessment should additionally be FCRA-compliant. How do you see alternative credit data evolving in the future? I cannot predict where it will go, but the unfettered potential is dizzying. Think about how DNA-based genealogy has taken off, telling folks they have family members they did not know and providing information to solve old crimes. I think we need to carefully balance personal privacy and prudent uses of customer data. There is also another issue with wide-ranging uses of new data. I contend it takes time to discern whether an element of data is accurately predictive.  Consider for a moment a person’s utility bills. If electricity usage in a household goes down when the bills in the neighborhood are going up, what does that tell us? Does it mean the family is under some financial strain and using the air conditioning less? Or does it tell us they had solar panels installed? Or they’ve been on vacation?  Figuring out what a particular piece of data means about someone’s circumstances can be difficult. About Philip Bohi Philip joined  AFSA in 2017 as Vice President, Compliance Education. He is responsible for providing strategic direction and leadership for the Association’s compliance activities, including AFSA University, and is the staff liaison to the Operations and Regulatory Compliance Committee and Technology Task Forces. He brings significant consumer finance legal and compliance experience to AFSA, having served as in-house counsel at Toyota Motor Credit Corporation and Fannie Mae. At those companies, Philip worked closely with compliance staff supporting technology projects, legislative tracking, and vendor management. His private practice included work on manufactured housing, residential mortgage compliance, and consumer finance matters at McGlinchey Stafford, PLLC and Lotstein Buckman, LLP. He is a member of the Virginia State Bar and the District of Columbia Bar. Learn more about the array of alternative credit data sources available to financial institutions.