Industries

Auto dealers need to move their dealerships forward through hyper-local advertising that helps them connect with the right audiences at the right time.

With the new year just days behind us, and as the uptick in holiday spending comes back down, debt consolidation will take precedence along with the making (and breaking) of new year’s resolutions. Personal loans were the fastest growing unsecured lending product for much of last year. From debt consolidation to major purchases, consumers are increasingly choosing these flexible, easy-access loans over credit cards throughout the course of the year. Recent Experian research highlighted the trends around this fast-paced lending product: Previously, while industry experts had predicted a leveling off of personal loans originations, Experian data shows steady growth. Additionally, there were 35.7 million personal loan trades in the second quarter, the highest number to date since Q1 of 2007. What is driving this growth? Observations suggest growth trends across the industry as a whole – not just in the personal loans segment. And the numbers prove it. Growth is occurring across the board. Experian statistics show: Consumer confidence is up 5.6% year over year Investor confidence remains high – up 18% year over year since 1987 Unemployment remains low and continued decrease is forecasted in the near future With increased confidence and increased spending often comes increased personal loans. More financial institutions are bringing personal loans under their roofs. As many consumers enter each new year as part of a “debt consolidation nation” per se, focus for many will be on personal loans as they seek to consolidate revolving debt. Since this is a known trend, lenders across the board – from traditional financial institutions to fintechs – need to be strategic with their marketing efforts in order to reach the right consumers with the right products at the right time. Consumers consider important factors in choosing the lender(s) for their personal loans including interest rate and the ability to apply online among others. These factors see differences across generations as well. These factors and others should influence lenders’ marketing strategies, on top of their best practices. Experian partnered with Mintel Group for their insights on the 2019 trends and best practices for digital credit marketing. Register for our upcoming webinar to learn more about Digital Credit Marketing 2019 Trends and Best Practices. Register for the Webinar

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.

What if you had an opportunity to boost your credit score with a snap of your fingers? With the announcement of Experian BoostTM, this will soon be the new reality. As part of an increasingly customizable and instant consumer reality in the marketplace, Experian is innovating in the space of credit to allow consumers to contribute information to their credit profiles via access to their online bank accounts. For decades, Experian has been a leader in educating consumers on credit: what goes into a credit score, how to raise it and how to maintain it. Now, as part of our mission to be the consumer’s bureau, Experian is ushering in a new age of consumer empowerment with Boost. Through an already established and full-fledged suite of consumer products, Experian Boost is the next generation offering a free online platform that places the control in the consumers’ hands to influence their credit scores. The platform will feature a sign-in verification, during which consumers grant read-only permission for Experian Boost to connect to their online bank accounts to identify utility and telecommunications payments. After they verify their data and confirm that they want the account information added to their credit file, consumers will receive an instant updated FICO® Score. The history behind credit information spans several centuries from a group of London tailors swapping information on customers to keeping credit files on index cards being read out to subscribers over the telephone. Even with the evolution of the credit industry being very much in the digital age today, Experian Boost is a significant step forward for a credit bureau. This new capability educates the consumer on what types of payment behavior impacts their credit score while also empowering them to add information to change it. This is a big win-win for consumers and lenders alike. As Experian is taking the next big step as a traditional credit bureau, adding these data sources is a new and innovative way to help consumers gain access to the quality credit they deserve as well as promoting fair and responsible lending to the industry. Early analysis of Experian’s Boost impact on the U.S. consumer credit scores showed promising results. Here’s a snapshot of some of those findings: These statistics provide an encouraging vision into the future for all consumers, especially for those who have a limited credit history. The benefit to lenders in adding these new data points will be a more complete view on the consumer to make more informed lending decisions. Only positive payment histories will be collected through the platform and consumers can elect to remove the new data at any time. Experian Boost will be available to all credit active adults in early 2019, but consumers can visit www.experian.com/boost now to register for early access. By signing up for a free Experian membership, consumers will receive a free credit report immediately, and will be one of the first to experience the new platform. Experian Boost will apply to most leading consumer credit scores used by lenders. To learn more about the platform visit www.experian.com/boost.

It’s the holiday season — time for jingle bells, lighting candles, shopping sprees and credit card fraud. But we’re prepared. Our risk analyst team constantly monitors our FraudNet solution performance to identify anomalies our clients experience as millions of transactions occur this month. At its core, FraudNet analyzes incoming events to determine the risk level and to allow legitimate events to process without causing frustrating friction for legitimate customers. That ensures our clients can recognize good customers across digital devices and channels while reducing fraud attacks and the need for internal manual reviews. But what happens when things don’t go as planned? Here’s a recent example. One of our banking clients noticed an abnormally high investigation queue after a routine risk engine tuning. Our risk analyst team looked further into the attacks to determine the cause and assess whether it was a tuning issue or a true fraud attack. After an initial analysis, the team learned that the events shared many of the same characteristics: Came from the same geo location that has been seen in previous attacks on clients Showed suspicious device and browser characteristics that were recognized by Experian’s device identification technology Identified suspicious patterns that have been observed in other recent attacks on banks The conclusion was that it wasn’t a mistake. FraudNet had correctly identified these transactions as suspicious. Experian® then worked with our client and recommended a strategy to ensure this attack was appropriately managed. This example highlights the power of device identification technology as a mechanism to detect emerging fraud threats, as well as link analysis tools and the expertise of a highly trained fraud analyst to uncover suspicious events that might otherwise go unnoticed. In addition to proprietary device intelligence capabilities, our clients take advantage of a suite of capabilities that can further enhance a seamless authentication experience for legitimate customers while increasing fraud detection for bad actors. Using advanced analytics, we can detect patterns and anomalies that may indicate a fraudulent identity is being used. Additionally, through our CrossCore® platform businesses can leverage advanced innovation, such as physical and behavioral biometrics (facial recognition, how a person holds a phone, mouse movements, data entry style), email verification (email tenure, reported fraud on email identities), document verification (autofill, liveliness detection) and digital behavior risk indicators (transaction behavior, transaction velocity), to further advance their existing risk mitigation strategies and efficacy.   With expanding partnerships and capabilities offered via Experian’s CrossCore platform, in conjunction with consultative industry expertise, businesses can be more confident during the authentication process to ensure a superb, frictionless customer experience without compromising security.

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.

As our society becomes ever more dependent on everything mobile, criminals are continually searching for and exploiting weaknesses in the digital ecosystem, causing significant harm to consumers, businesses and the economy.  In fact, according to our 2018 Global Fraud & Identity Report, 72 percent of business executives are more concerned than ever about the impact of fraud. Yet, despite the awareness and concern, 54 percent of businesses are only “somewhat confident” in their ability to detect fraud. That needs to change, and it needs to change right away.  Our industry has thrived by providing products and services that root out bad transactions and detect fraud with minimal consumer friction. We continue to innovate new ways to authenticate consumers, apply new cloud technologies, machine learning, self-service portals and biometrics. Yet, the fraud issue still exists. It hasn’t gone away. How do we provide effective means to prevent fraud without inconveniencing everyone in the process? That’s the conundrum. Unfortunately, a silver bullet doesn’t exist. As much as we would like to build a system that can detect all fraud, eliminate all consumer friction, we can’t. We’re not there yet. As long as money has changed hands, as long as there are opportunities to steal, criminals will find the weak points – the soft spots.  That said, we are making significant progress. Advances in technology and innovation help us bring new solutions to market more quickly, with more predictive power than ever, and the ability to help clients to turn  these services on in days and weeks. So, what is Experian doing? We’ve been in the business of fraud detection and identity verification for more than 30 years. We’ve seen fraud patterns evolve over time, and our product portfolio evolves in lock-step to counter the newest fraud vectors. Synthetic identity fraud, loan stacking, counterfeit, identity theft; the specific fraud attacks may change but our solution stack counters each of those threats. We are on a continuous innovation path, and we need to be. Our consumer and small business databases are unmatched in the industry for quality and coverage, and that is an invaluable asset in the fight against fraud. It used to be that knowing something about a person was the same as authenticating that same person. That’s just not the case today. But, just because I may not be the only person who knows where I live, doesn’t mean that identity information is obsolete. It is incredibly valuable, just in different ways today. And that’s where our scientists come into their own, providing complex predictive solutions that utilize a plethora of data and insight to create the ultimate in predictive performance. We go beyond traditional fraud detection methods, such as knowledge-based authentication, to offer a custom mix of passive and active authentication solutions that improve security and the customer experience. You want the latest deep learning techniques? We have them. You want custom models scored in milliseconds alongside your existing data requests. We can do that. You want a mix of cloud deployment, dedicated hosted services and on-premise? We can do that too. We have more than 20 partners across the globe, creating the most comprehensive identity management network anywhere. We also have teams of experts across the world with the know how to combine Experian and partner expertise to craft a bespoke solution that is unrivaled in detection performance. The results speak for themselves: Experian analyzes more than a billion credit applications per year for fraud and identity, and we’ve helped our clients save more than $2 billion in annual fraud losses globally. CrossCore™, our fraud prevention and identity management platform, leverages the full breadth of Experian data as well as the data assets of our partners. We execute machine learning models on every decision to help improve the accuracy and speed with which decisions are made. We’ve seen CrossCore machine learning result in a more than 40 percent improvement in fraud detection compared to rules-based systems. Our certified partner community for CrossCore includes only the most reputable leaders in the fraud industry. We also understand the need to expand our data to cover those who may not be credit active. We have the largest and most unique sets of alternative credit data among the credit bureaus, that includes our Clarity Services and RentBureau divisions. This rich data helps our clients verify an individual’s identity, even if they have a thin credit file. The data also helps us determine a credit applicant’s ability to pay, so that consumers are empowered to pursue the opportunities that are right for them. And in the background, our models are constantly checking for signs of fraud, so that consumers and clients feel protected. Fraud prevention and identity management are built upon a foundation of trust, innovation and keeping the consumer at the heart of every decision. This is where I’m proud to say that Experian stands apart. We realize that criminals will continue to look for new ways to commit fraud, and we are continually striving to stay one step ahead of them. Through our unparalleled scale of data, partnerships and commitment to innovation, we will help businesses become more confident in their ability to recognize good people and transactions, provide great experiences, and protect against fraud.

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

Synthetic identities come from accounts held not by actual individuals, but by fabricated identities created to perpetrate fraud. It often starts with stealing a child’s Social Security number (SSN) and then blending fictitious and factual data, such as a name, a mailing address and a telephone number. What’s interesting is the increase in consumer awareness about synthetic identities. Previously, synthetic identity was a lender concern, often showing itself in delinquent accounts since the individual was fabricated. Consumers are becoming aware of synthetic ID fraud because of who the victims are — children. Based on findings from a recent Experian survey, the average age of child victims is only 12 years old. Children are attractive victims since fraud that uses their personal identifying information can go for years before being detected. I recently was interviewed by Forbes about the increase of synthetic identities being used to open auto loans and how your child’s SSN could be used to get a phony auto loan. The article provides a good overview of this growing concern for parents and lenders. A recent Javelin study found that more than 1 million children were victims of fraud. Most upsetting is that children are often betrayed by people close to them -- while only 7 percent of adults are victimized by someone they know, 60 percent of victims under 18 know the fraudster. Unfortunately, when families are in a tight spot financially they often resort to using their child’s SSN to create a clean credit record. Fraud is an issue we all must deal with — lenders, consumers and even minors — and the best course of action is to protect ourselves and our organizations.

There are four reasons why the auto industry should be enthusiastic about the electric vehicle segment’s future.

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.

Where are electric vehicles most popular? During the first half of the year, 3.6 percent of all new registrations in California were EVs.

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.”  

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