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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