Data & Analytics
Are You #TeamTrended or #TeamAlternative? There’s no such thing as too much data, but when put head to head, differences between the data sets are apparent. Which team are you on? Here’s what we know: With the entry and incorporation of alternative credit data into the data arena, traditional credit data is no longer the sole determinant for credit worthiness, granting more people credit access. Built for the factors influencing financial health today, alternative credit data essentially fills the gaps of the traditional credit file, including alternative financial services data, rental payments, asset ownership, utility payments, full file public records, and consumer-permissioned data – all FCRA-compliant data. Watch this video to see more: Trended data, on the other hand shows actual, historical credit data. It provides key balance and payment data for the previous 24 months to allow lenders to leverage behavior trends to determine how individuals are utilizing their credit. Different splices of that information reveal particular behavior patterns, empowering lenders to then act on that behavior. Insights include a consumer’s spend on all general purpose credit and charge cards and predictive metrics that identify consumers who will be in the market for a specific type of credit product. In the head-to-head between alternative credit data and trended data, both have clear advantages. You need both on your roster to supplement traditional credit data and elevate your game to the next level when it comes to your data universe. Compared to the traditional credit file, alternative credit data can reveal information differentiating two consumers. In the examples below, both consumers have moderate limits and have making timely credit card payments according to their traditional credit reports. However, alternative data gives insight into their alternative financial services information. In Example 1, Robert Smith is currently past due on his personal loan, whereas Michelle Lee in Example 2 is current on her personal loan, indicating she may be the consumer with stronger creditworthiness. Similarly, trended data reveals that all credit scores are not created equal. Here is an example of how trended data can differentiate two consumers with the same score. Different historical trends can show completely different trajectories between seemingly similar consumers. While the traditional credit score is a reliable indication of a consumer’s creditworthiness, it does not offer the full picture. What insights are you missing out on? Go to Infographic Get Started Today
Experian Boost gives consumers greater control over their credit profiles by allowing them to add non-traditional credit information to their Experian credit file.
From the time we wake up to the minute our head hits the pillow, we make about 35,000 conscious and unconscious decisions a day. That’s a lot of processing in a 24-hour period. As part of that process, some decisions are intuitive: we’ve been in a situation before and know what to expect. Our minds make shortcuts to save time for the tasks that take a lot more brainpower. As for new decisions, it might take some time to adjust, weigh all the information and decide on a course of action. But after the new situation presents itself over and over again, it becomes easier and easier to process. Similarly, using traditional data is intuitive. Lenders have been using the same types of data in consumer credit worthiness decisions for decades. Throwing in a new data asset might take some getting used to. For those who are wondering whether to use alternative credit data, specifically alternative financial services (AFS) data, here are some facts to make that decision easier. In a recent webinar, Experian’s Vice President of Analytics, Michele Raneri, and Data Scientist, Clara Gharibian, shed some light on AFS data from the leading source in this data asset, Clarity Services. Here are some insights and takeaways from that event. What is Alternative Financial Services? A financial service provided outside of traditional banking institutions which include online and storefront, short-term unsecured, short-term installment, marketplace, car title and rent-to-own. As part of the digital age, many non-traditional loans are also moving online where consumers can access credit with a few clicks on a website or in an app. AFS data provides insight into each segment of thick to thin-file credit history of consumers. This data set, which holds information on more than 62 million consumers nationwide, is also meaningful and predictive, which is a direct answer to lenders who are looking for more information on the consumer. In fact, in a recent State of Alternative Credit Data whitepaper, Experian found that 60 percent of lenders report that they decline more than 5 percent of applications because they have insufficient information to make a loan decision. The implications of having more information on that 5 percent would make a measurable impact to the lender and consumer. AFS data is also meaningful and predictive. For example, inquiry data is useful in that it provides insight into the alternative financial services industry. There are also more stability indicators in this data such as number of employers, unique home phone, and zip codes. These interaction points indicate the stability or volatility of a consumer which may be helpful in decision making during the underwriting stage. AFS consumers tend to be younger and less likely to be married compared to the U.S. average and traditional credit data on File OneSM . These consumers also tend to have lower VantageScores, lower debt, higher bad rates and much lower spend. These statistics lend themselves to seeing the emerging consumer; millennials, immigrants with little to no credit history and also those who may have been subprime or near prime consumers who are demonstrating better credit management. There also may be older consumers who may have not engaged in traditional credit history in a while or those who have hit a major life circumstance who had nowhere else to turn. Still others who have turned to nontraditional lending may have preferred the experience of online lending and did not realize that many of these trades do not impact their traditional credit file. Regardless of their individual circumstances, consumers who leverage alternative financial services have historically had one thing in common: their performance in these products did nothing to further their access to traditional, and often lower cost, sources of credit. Through Experian’s acquisition and integration of Clarity Services, the nation’s largest alternative finance credit bureau, lenders can gain access to powerful and predictive supplemental credit data that better detect risk while benefiting consumers with a more complete credit history. Alternative finance data can be used across the lending cycle from prospecting to decisioning and account review to collections. Alternative data gives lenders an expanded view of consumer behavior which enables more complete and confident lending decisions. Find out more about Experian’s alternative credit data: www.experian.com/alternativedata.
2019 is here — with new technology, new regulations and new opportunities on the docket. What does that mean for the financial services space? Here are the five trends you should keep your eye on and how these affect your credit universe. 1. Credit access is at an all-time high With 121 million Americans categorized as credit-challenged (subprime scores and a thin or nonexistent credit file) and 45 million considered credit-invisible (no credit history), the credit access many consumers take for granted has appeared elusive to others. Until now. The recent launch of Experian BoostTM empowers consumers to improve their credit instantly using payment history from their utility and phone bills, giving them more control over their credit scores and making them more visible to lenders and financial institutions. This means more opportunities for more people. Coupled with alternative credit data, which includes alternative financial services data, rental payments, and full-file public records, lenders and financial institutions can see a whole new universe. In 2019, inclusion is key when it comes to universe expansion goals. Both alternative credit and consumer-permissioned data will continue to be an important part of the conversation. 2. Machine learning for the masses The financial services industry has long been notorious for being founded on arguably antiquated systems and steeped in compliance and regulations. But the industry’s recent speed of disruption, including drastic changes fueled by technology and innovation, may suggest a changing of the guard. Digital transformation is an industry hot topic, but defining what that is — and navigating legacy systems — can be challenging. Successfully integrating innovation is the convergence at the center of the Venn diagram of strategy, technology and operations. The key, according to Deloitte, is getting “a better handle on data to extract the greatest value from technology investments.” How do you get the most value? Risk managers need big data, machine learning and artificial intelligence strategies to deliver market insights and risk evaluation. Between the difficulty of leveraging data sets and significant investment in time and money, it’s impossible for many to justify. To combat this challenge, the availability and access to an analytical sandbox (which contains depersonalized consumer data and comparative industry intel) is crucial to better serve clients and act on opportunities in lenders’ credit universe and beyond. “Making information analysis easily accessible also creates distinct competitive advantages,” said Vijay Mehta, Chief Innovation Officer for Experian’s Consumer Information Services, in a recent article for BAI Banking Strategies. “Identifying shifts in markets, changes in regulations or unexpected demand allows for quick course corrections. Tightening the analytic life cycle permits organizations to reach new markets and quickly respond to competitor moves.” This year is about meaningful metrics for action, not just data visualization. 3. How to fit into the digital-first ecosystem With so many things available on demand, the need for instant gratification continues to skyrocket. It’s no secret that the financial services industry needs to compete for attention across consumers’ multiple screens and hours of screen time. What’s in the queue for 2019? Personalization, digitalization and monetization. Consumers’ top banking priorities include customized solutions, omnichannel experience improvement and enhancing the mobile channel (as in, can we “Amazonize” everything?). Financial services leaders’ priorities include some of the same things, such as enhancing the mobile channel and delivering options to customize consumer solutions (BAI Banking Strategies). From geolocation targeting to microinteractions in the user experience journey to leveraging new strategies and consumer data to send personalized credit offers, there’s no shortage of need for consumer hyper-relevance. 33 percent of consumers who abandon business relationships do so because personalization is lacking, according to Accenture data for The Financial Brand. This expectation spans all channels, emphasizing the need for a seamless experience across all devices. 4. Keeping fraudsters out Many IT professionals regard biometric authentication as the most secure authentication method currently available. We see this technology on our personal devices, and many companies have implemented it as well. Biometric hacking is among the predicted threats for 2019, according to Experian’s Data Breach Industry Forecast, released last month. “Sensors can be manipulated and spoofed or deteriorate with too much use. ... Expect hackers to take advantage of not only the flaws found in biometric authentication hardware and devices, but also the collection and storage of data,” according to the report. 5. Regulatory changes and continued trends Under the Trump Administration, the regulatory front has been relatively quiet. But according to the Wall Street Journal, as Democrats gain control of the House of Representatives, lawmakers may be setting their sights on the financial services industry — specifically on legislation in response to the credit data breach in 2017. The Democratic Party leadership has indicated that the House Financial Services Committee will be focused on protecting consumers and investors, preserving sector stability, and encouraging responsible innovation in financial technology, according to Deloitte. In other news, the focus on improving accuracy in data reporting, transparency for consumers in credit scoring and other automated decisions can be expected to continue. Consumer compliance, and specifically the fair and responsible treatment of consumers, will remain a top priority. For all your needs in 2019 and beyond, Experian has you covered. Learn more
Subprime originations hit the lowest overall share of the market seen in 11 years, but does that mean people are being locked out car ownership? Not necessarily, according to the Q3 State of the Automotive Finance Market report.To gain accurate insights from the vast amount of data available, it’s important to look at the entire picture that is created by the data. The decrease in subprime originations is due to many factors, one of which being that credit scores are increasing across the board (average is now 717 for new and 661 for used), which naturally shifts more consumers into the higher credit tiers. Loan origination market share are just one of the trends seen in this quarter’s report. Ultimately, examining the data can help inform lenders and help them make the right lending decisions. Exploring options for affordability While consumers analyze different possibilities to ensure their monthly payments are affordable, leasing is one of the more reasonable options in terms of monthly payments. In fact, the difference between the average new lease payment and new car payment usually averages more $100—and sometimes well over—which is a significant amount for the average American budget. In fact, leases of new vehicles are hovering around 30 percent, which is one of the factors that is aiding in new car sales. In turn, this then helps the used-vehicle market, as the high number of leases create a larger supply of quality use vehicles when they come off-lease and make their way back into the market. On-time payments continue to improve As consumer preferences continue to trend towards more expensive vehicles, such as crossovers, SUVs, and pickups, affordability will continue to be a topic of discussion. But consumers appear to be managing the higher prices, as in addition to the tactics mentioned above, 30- and 60-day delinquency rates declined since Q3 2017, from 2.39 percent to 2.23 percent and 0.76 percent to 0.72 percent, respectively. The automotive finance market is one where the old saying “no news is good news” continues to remain true. While there aren’t significant changes in the numbers quarter over quarter, this signals that the market is at a good place in its cycle. To learn more about the State of the Automotive Finance Market report, or to watch the webinar, click here.
“We don’t know what we don’t know.” It’s a truth that seems to be on the minds of just about every financial institution these days. The market, not-to-mention the customer base, seems to be evolving more quickly now than ever before. Mergers, acquisitions and partnerships, along with new competitors entering the space, are a daily headline. Customers expect the same seamless user experience and instant gratification they’ve come to expect from companies like Amazon in just about every interaction they have, including with their financial institutions. Broadly, financial institutions have been slow to respond both in the products they offer their customers and prospects, and in how they present those products. Not surprisingly, only 26% of customers feel like their financial institutions understand and appreciate their needs. So, it’s not hard to see why there might be uncertainty as to how a financial institution should respond or what they should do next. But what if you could know what you don’t know about your customer and industry data? Sound too good to be true? It’s not—it’s exactly what Experian’s Ascend Analytical Sandbox was built to do. “At OneMain we’ve used Sandbox for a lot of exploratory analysis and feature development,” said Ryland Ely, a modeler at Experian partner client, OneMain Financial and a Sandbox user. For example, “we’ve used a loan amount model built on Sandbox data to try and flag applications where we might be comfortable with the assigned risk grade but we’re concerned we might be extending too much or too little credit,” he said. The first product built on Experian’s big data platform, Ascend, the Analytical Sandbox is an analytics environment that can have enterprise-wide impact. It provides users instant access to near real-time customer data, actionable analytics and intelligence tools, along with a network of industry and support experts to drive the most value out of their data and analytics. Developed with scalability, flexibility, efficiency and security at top-of-mind, the Sandbox is a hybrid-cloud system that leverages the high availability and security of Amazon Web Services. This eliminates the need, time and infrastructure costs associated with creating an internally hosted environment. Additionally, our web-based interface speeds access to data and tools in your dedicated Sandbox all behind the protection of Experian’s firewall. In addition to being supported by a revolutionized tech stack backed by an $825 million annual investment, Sandbox enables use of industry-leading business intelligence tools like SAS, RStudio, H2O, Python, Hue and Tableau. Where the Ascend Sandbox really shines is in the amount and quality of the data that’s put into it. As the largest, global information services provider, the Sandbox brings the full power of Experian’s 17+ years of full-file historical tradeline data, boasting a data accuracy rate of 99.9%. The Sandbox also allows users the option to incorporate additional data sets including commercial small business data and soon real estate data, among others. Alternative data assets add to the 50 million consumers who use some sort of financial service, in addition to rental and utility payments. In addition to including Experian’s data on the 220+ million credit-active consumers, small business and other data sets, the Sandbox also allows companies to integrate their own customer data into the system. All data is depersonalized and pinned to allow companies to fully leverage the value of Experian’s patented attributes and scores and models. Ascend Sandbox allows companies to mine the data for business intelligence to define strategy and translate those findings into data visualizations to communicate and win buy-in throughout their organization. But here is where customers are really identifying the value in this big data solution, taking those business intelligence insights and being able to take the resulting models and strategies from the Sandbox directly into a production environment. After all, amassing data is worthless unless you’re able to use it. That’s why 15 of the top financial institutions globally are using the Experian Ascend Sandbox for more than just benchmarking and data visualization but also risk modeling, score migration, share of wallet, market entry, cross-sell and much more. Moreover, clients are seeing time-savings, deeper insights and reduced compliance concerns as a result of consolidating their production data and development platform inside Sandbox. “Sandbox is often presented as a tool for visualization or reporting, sort of creating summary statistics of what’s going on in the market. But as a modeler, my perspective is that it has application beyond just those things,” said Ely. To learn more about the Experian Ascend Analytical Sandbox and hear more about how OneMain Financial is getting value out of the Sandbox, watch this on-demand webinar.
Your model is only as good as your data, right? Actually, there are many considerations in developing a sound model, one of which is data. Yet if your data is bad or dirty or doesn’t represent the full population, can it be used? This is where sampling can help. When done right, sampling can lower your cost to obtain data needed for model development. When done well, sampling can turn a tainted and underrepresented data set into a sound and viable model development sample. First, define the population to which the model will be applied once it’s finalized and implemented. Determine what data is available and what population segments must be represented within the sampled data. The more variability in internal factors — such as changes in marketing campaigns, risk strategies and product launches — and external factors — such as economic conditions or competitor presence in the marketplace — the larger the sample size needed. A model developer often will need to sample over time to incorporate seasonal fluctuations in the development sample. The most robust samples are pulled from data that best represents the full population to which the model will be applied. It’s important to ensure your data sample includes customers or prospects declined by the prior model and strategy, as well as approved but nonactivated accounts. This ensures full representation of the population to which your model will be applied. Also, consider the number of predictors or independent variables that will be evaluated during model development, and increase your sample size accordingly. When it comes to spotting dirty or unacceptable data, the golden rule is know your data and know your target population. Spend time evaluating your intended population and group profiles across several important business metrics. Don’t underestimate the time needed to complete a thorough evaluation. Next, select the data from the population to aptly represent the population within the sampled data. Determine the best sampling methodology that will support the model development and business objectives. Sampling generates a smaller data set for use in model development, allowing the developer to build models more quickly. Reducing the data set’s size decreases the time needed for model computation and saves storage space without losing predictive performance. Once the data is selected, weights are applied so that each record appropriately represents the full population to which the model will be applied. Several traditional techniques can be used to sample data: Simple random sampling — Each record is chosen by chance, and each record in the population has an equal chance of being selected. Random sampling with replacement — Each record chosen by chance is included in the subsequent selection. Random sampling without replacement — Each record chosen by chance is removed from subsequent selections. Cluster sampling — Records from the population are sampled in groups, such as region, over different time periods. Stratified random sampling — This technique allows you to sample different segments of the population at different proportions. In some situations, stratified random sampling is helpful in selecting segments of the population that aren’t as prevalent as other segments but are equally vital within the model development sample. Learn more about how Experian Decision Analytics can help you with your custom model development needs.
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.
Fintechs seem to be the new Joneses. Everyone’s trying to keep up. I sat down with Gavin Harding, Senior Business Consultant with Experian Advisory Services, to tap into some of his insight on online fintech lenders. What are they doing to push the envelope when it comes to evolving the financial industry. How are they addressing the topics that all lenders have – including strategy, regulations, credit invisibles, etc.? Here’s what he had to say. Part one of a two-part series: Fintechs have their own way of doing things across the financial industry. How is alternative data defined in that space? There are many different definitions of “alt data.” Let’s start with the fundamentals. When we think about “traditional” or “mainstream” data, that typically includes the bureau data that we are all familiar with. Bureau data has been around for a long time and is used extensively throughout the credit and loan lifecycle. This data typically includes a consumer’s credit history, such as a summary of inquiries, tradelines, and balances. But, this information doesn’t necessarily provide a holistic consumer snapshot that allows lenders to fully assess credit and risk. What about the so-called “credit invisibles” with little to no credit history? This is what we mean when we talk about alternative data – within the consumer lending ecosystem, it is anything that is not traditional credit bureau data. Alternative data combined with traditional data allows lenders to expand their universe by reaching underserved markets – which means more access to more credit for more consumers. Let’s briefly discuss three examples: alternative financial services data, transactional data and rental data. Alternative financial services data provides insight into alternative sources of financing that are quickly becoming mainstream. This data set contains small-dollar installment loans, point-of-sale financing, rent-to-own, online installment loans and auto-title lending. Transactional data illustrates how a consumer uses their checking account, or in other words, how many deposits have been accumulated in a month, what is the average deposit, and what bills have been paid from the account. This type of data provides a better picture of a consumer’s financial health and ability to repay. Rental data can serve a similar purpose. Consistent and steady trends of a consumer making good on their rental payment month-after-month, year-after-year, speaks to their ability and intent to pay. If I am a thin-file consumer with limited credit history, alternative data – such as transactional data and rental data – gives the lender more information to make an informed credit decision. Compliance with regulatory requirements is a key concern for any financial institution. What should fintech lenders take into consideration when incorporating nontraditional data into their strategy? Users of alternative data – whether traditional financial institution or fintech – must ensure compliance with applicable lending regulations. To fall under the Fair Reporting Act (FCRA) compliant umbrella, alternative credit data must be displayable, disputable and correctable. Keep in mind, alternative data is often used to augment traditional data to get from a declined credit application, to an approved credit application. Simply put, alternative data is incremental data. As long as fintechs use it in a consistent and compliant way – it works. Always an advocate for new thought leadership, Experian recently sponsored a report conducted by Aite Group. This third-party report about alternative data 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 Stay tuned for part two of this series. And click here for more information about Experian’s Alternative Data solutions. 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® 4.0, 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.”
While electric vehicles remain a relatively niche part of the market, with only 0.9 percent of the total vehicle registrations through June 2018, consumer demand has grown quite significantly over the past few years. As I mentioned in a previous blog post, electric vehicles held just 0.5 percent in 2016. Undoubtedly, manufacturers and retailers will look to capitalize on this growing segment of the population. But, it’s not enough to just dig into the sales number. If the automotive industry really wants to position itself for success, it’s important to understand the consumers most interested in electric vehicles. This level of data can help manufacturers and retailers make the right decisions and improve the bottom line. Based on our vehicle registration data, below is detailed look into the electric vehicle consumer. Home Value Somewhat unsurprisingly, the people most likely to purchase an electric vehicle tend to own more expensive homes. Consumers with homes valued between $450,000-$749,000 made up 25 percent of electric vehicle market share. And, as home values increase, these consumers still make up a significant portion of electric vehicle market. More than 15 percent of the electric vehicle market share was made up by those with homes valued between $750,000-$999,000, and 22.5 percent of the share was made up by those with home values of more than $1 million. In fact, consumers with home values of more than $1 million are 5.9 times more likely to purchase an electric vehicle than the general population. Education Level Breaking down consumers by education level shows another distinct pattern. Individuals with a graduate degree are two times more likely to own an electric vehicle. Those with graduate degrees made up 28 percent of electric vehicle market share, compared to those with no college education, which made up just 11 percent. Consumer Lifestyle Segmentation Diving deeper into the lifestyles of individuals, we leveraged our Mosaic® USA consumer lifestyle segmentation system, which classifies every household and neighborhood in the U.S. into 71 unique types and 19 overachieving groups. Findings show American Royalty, who are described as wealthy, influential couples and families living in prestigious suburbs, led the way with a 17.8 percent share. Following them were Silver Sophisticates at 11.9 percent. Those in this category are described as mature couples and singles living an upscale lifestyle in suburban homes. Rounding out the top three were Cosmopolitan Achiever, described as affluent middle-aged and established couples and families who enjoy a dynamic lifestyle in metro areas. Their share was 10.1 percent. If manufacturers and retailers go beyond just the sales figures, a clearer picture of the electric vehicle market begins to form. They have an opportunity to understand that wealthier, more established individuals with higher levels of education and home values are much more likely to purchase electric vehicles. While these characteristics are consistent, the different segments represent a dynamic group of people who share similarities, but are still at different stages in life, leading different lifestyles and have different needs. As time wears on, the electric vehicle segment is poised for growth. If the industry wants to maximize its potential, they need to leverage data and insights to help make the right decisions and adapt to the evolving marketplace.
I believe it was George Bernard Shaw that once said something along the lines of, “If economists were laid end-to-end, they’d never come to a conclusion, at least not the same conclusion.” It often feels the same way when it comes to big data analytics around customer behavior. As you look at new tools to put your customer insights to work for your enterprise, you likely have questions coming from across your organization. Models always seem to take forever to develop, how sure are we that the results are still accurate? What data did we use in this analysis; do we need to worry about compliance or security? To answer these questions and in an effort to best utilize customer data, the most forward-thinking financial institutions are turning to analytical environments, or sandboxes, to solve their big data problems. But what functionality is right for your financial institution? In your search for a sandbox solution to solve the business problem of big data, make sure you keep these top four features in mind. Efficiency: Building an internal data archive with effective business intelligence tools is expensive, time-consuming and resource-intensive. That’s why investing in a sandbox makes the most sense when it comes to drawing the value out of your customer data.By providing immediate access to the data environment at all times, the best systems can reduce the time from data input to decision by at least 30%. Another way the right sandbox can help you achieve operational efficiencies is by direct integration with your production environment. Pretty charts and graphs are great and can be very insightful, but the best sandbox goes beyond just business intelligence and should allow you to immediately put models into action. Scalability and Flexibility: In implementing any new software system, scalability and flexibility are key when it comes to integration into your native systems and the system’s capabilities. This is even more imperative when implementing an enterprise-wide tool like an analytical sandbox. Look for systems that offer a hosted, cloud-based environment, like Amazon Web Services, that ensures operational redundancy, as well as browser-based access and system availability.The right sandbox will leverage a scalable software framework for efficient processing. It should also be programming language agnostic, allowing for use of all industry-standard programming languages and analytics tools like SAS, R Studio, H2O, Python, Hue and Tableau. Moreover, you shouldn’t have to pay for software suites that your analytics teams aren’t going to use. Support: Whether you have an entire analytics department at your disposal or a lean, start-up style team, you’re going to want the highest level of support when it comes to onboarding, implementation and operational success. The best sandbox solution for your company will have a robust support model in place to ensure client success. Look for solutions that offer hands-on instruction, flexible online or in-person training and analytical support. Look for solutions and data partners that also offer the consultative help of industry experts when your company needs it. Data, Data and More Data: Any analytical environment is only as good as the data you put into it. It should, of course, include your own client data. However, relying exclusively on your own data can lead to incomplete analysis, missed opportunities and reduced impact. When choosing a sandbox solution, pick a system that will include the most local, regional and national credit data, in addition to alternative data and commercial data assets, on top of your own data.The optimum solutions will have years of full-file, archived tradeline data, along with attributes and models for the most robust results. Be sure your data partner has accounted for opt-outs, excludes data precluded by legal or regulatory restrictions and also anonymizes data files when linking your customer data. Data accuracy is also imperative here. Choose a big data partner who is constantly monitoring and correcting discrepancies in customer files across all bureaus. The best partners will have data accuracy rates at or above 99.9%. Solving the business problem around your big data can be a daunting task. However, investing in analytical environments or sandboxes can offer a solution. Finding the right solution and data partner are critical to your success. As you begin your search for the best sandbox for you, be sure to look for solutions that are the right combination of operational efficiency, flexibility and support all combined with the most robust national data, along with your own customer data. Are you interested in learning how companies are using sandboxes to make it easier, faster and more cost-effective to drive actionable insights from their data? Join us for this upcoming webinar. Register for the Webinar
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.
Electric vehicles are here to stay – and will likely gain market share as costs reduce, travel ranges increase and charging infrastructure grows.
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