Industries

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.

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

Vehicle prices are going up, yet consumers seem unfazed. Despite consumers taking out larger loan amounts, they continue to make their monthly payments on time. But, affordability remains a point of industry interest.  As vehicle prices hit record highs, how long will consumers have an appetite for them? According to Experian’s latest State of Automotive Finance Market report, delinquency rates continued a downward trend, as 30- and 60-day delinquencies were 2.11 and 0.64 percent, respectively, at the end of Q2. Those numbers demonstrate that car owners are making timely payments despite rising vehicle costs, which is an encouraging sign for the market. The average loan amount for a new vehicle is now $30,958, a $724 increase from last year. Additionally, consumers are now making monthly payments of about $525 on a new car loan, an all-time high that has seen a $20 year over year increase. The auto market shows little to no sign of declining costs, but vehicles aren’t the only cost to consider – interest rates have increased by 56 basis points since last year.  When combined with the rising manufacturer costs, long-term affordability is a continued concern within the industry. The data points to consumers offsetting the expense by taking out longer loan terms. In Q2, the most common loan length was 72 months—which equates to six years—for both new and used financing. While this lowers the monthly payment, it leaves them subject to paying higher interest over time, as well as the potential for individuals to be upside down on their loan for a longer period of time. The key takeaway from this data is that costs continue to rise, but consumers appear to be doing a better job of managing their finances. This insight can help OEMs, dealers, and lenders make strategic decisions with a better understanding of consumer borrowing and credit habits, and think about how to make car ownership more inviting, through incentive or loyalty programs. For consumers, continuing to take steps to actively improve your credit score is one of the key ways to ensure that you’re able to negotiate the right deal when it comes to financing. Ultimately, for everyone involved, it comes down to leveraging the power of data to make more informed decisions, which can help make vehicle ownership more accessible and affordable. To learn more about the State of the Automotive Finance Market report, or to watch the webinar, click here.

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

How a business prices its products is a dynamic process that drives customer satisfaction and loyalty, as well as business success.  In the digital age, pricing is becoming even more complex.  For example, companies like Amazon may revise the price of a hot item several times per day. Dynamic pricing models for consumer financial products can be especially difficult for at least four reasons: A complex regulatory environment. Fair lending concerns. The potential for adverse selection by risky consumers and fraudsters. The direct impact the affordability of a loan may have on both the consumer’s ability to pay it and the likelihood that it will be prepaid. If a lender offered the same interest rate and terms to every customer for the same loan product, low-risk customers would secure better rates elsewhere, and high-risk customers would not. The end result? Only the higher-risk customers would select the product, which would increase losses and reduce profitability. For this reason, the lending industry has established risk-based pricing. This pricing method addresses the above issue, since customers with different risk profiles are offered different rates. But it’s limited. More advanced lenders also understand the price elasticity of customer demand, because there are diverse reasons why customers decide to take up differently priced loans. Customers have different needs and risk profiles, so they react to a loan offer in different ways. Many factors determine a customer’s propensity to take up an offer — for example, the competitive environment and availability of other lenders, how time-critical the decision is, and the loan terms offered. Understanding the customer’s price elasticity allows a business to offer the ideal price to each customer to maximize profitability. Pricing optimization is the superior method assuming the lender has a scientific, data-driven approach to predicting how different customers will respond to different prices. Optimization allows an organization to determine the best offer for each customer to meet business objectives while adhering to financial and operational constraints such as volume, margin and credit risk. The business can access trade-offs between competing objectives, such as maximizing revenue and maximizing volume, and determine the optimal decision to be made for each individual customer to best meet both objectives. In the table below, you can see five benefits lenders realize when they improve their pricing segmentation with an optimization strategy. Interested in learning more about pricing optimization? Click here to download our full white paper, Price optimization in retail consumer lending.

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

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

In the aftermath of Hurricane Florence, Experian is here to help. As a first line of defense against purchasing a flood-damaged vehicle, people can download our free Vehicle Flood Risk Check app.

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

Traditional credit data has long been the end-all-be-all ruling the financial services space. Like the staple black suit or that little black dress in your closet, it’s been the quintessential go-to for decades. Sure, the financial industry has some seasonality, but traditional credit data has reigned supreme as the reliable pillar. It’s dependable. And for a long time, it’s all there was to the equation. But as with finance, fashion and all things – evolution has occurred. Specifically, how consumers are managing their money has evolved, which calls for deeper insights that are still defensible and disputable. Alternative credit data is the new black. It's increasingly integrated in credit talks for lenders across the country. Much like that LBD, it's become a lending staple – that closet (or portfolio) must-have – to leverage for better decisioning when determining creditworthiness. What is alternative data? In our data-driven industry, “alternative” data as a whole may best be summed up as FCRA-compliant credit data that isn't typically included in traditional credit reports. For traditional data, think loan and inquiry data on bankcards, auto, mortgage and personal loans; typically trades with a term of 12 months or greater. Some examples of alternative credit data include alternative financial services data, rental data, full-file public records and account aggregation. These insights can ultimately improve credit access and decisioning for millions of consumers who may otherwise be overlooked. Alternative or not, every bit of information counts FCRA-compliant, user permissioned data allows lenders to easily verify assets and income electronically, thereby giving lenders more confidence in their decision and allowing consumers to gain access to lower-cost financing. From a risk management perspective, alternative credit data can also help identify riskier consumers by identifying information like the number of payday loans acquired within a year or number of first-payment defaults. Alternative credit data can give supplemental insight into a consumer’s stability, ability and willingness to repay that is not available on a traditional credit report that can help lenders avoid risk or price accordingly. From closet finds that refresh your look to that LBD, alternative credit data gives lenders more transparency into their consumers, and gives consumers seeking credit a greater foundation to help their case for creditworthiness. It really is this season’s – and every season’s – must-have. Learn more

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

The August 2018 LinkedIn Workforce Report states some interesting facts about data science and the current workforce in the United States. Demand for data scientists is off the charts, but there is a data science skills shortage in almost every U.S. city — particularly in the New York, San Francisco and Los Angeles areas. Nationally, there is a shortage of more than 150,000 people with data science skills. One way companies in financial services and other industries have coped with the skills gap in analytics is by using outside vendors. A 2017 Dun & Bradstreet and Forbes survey reported that 27 percent of respondents cited a skills gap as a major obstacle to their data and analytics efforts. Outsourcing data science work makes it easier to scale up and scale down as needs arise. But surprisingly, more than half of respondents said the third-party work was superior to their in-house analytics. At Experian, we have participated in quite a few outsourced analytics projects. Here are a few of the lessons we’ve learned along the way: Manage expectations: Everyone has their own management style, but to be successful, you must be proactively involved in managing the partnership with your provider. Doing so will keep them aligned with your objectives and prevent quality degradation or cost increases as you become more tied to them. Communication: Creating open and honest communication between executive management and your resource partner is key. You need to be able to discuss what is working well and what isn’t. This will help to ensure your partner has a thorough understanding of your goals and objectives and will properly manage any bumps in the road. Help external resources feel like a part of the team: When you’re working with external resources, either offshore or onshore, they are typically in an alternate location. This can make them feel like they aren’t a part of the team and therefore not directly tied to the business goals of the project. To help bridge the gap, performing regular status meetings via video conference can help everyone feel like a part of the team. Within these meetings, providing information on the goals and objectives of the project is key. This way, they can hear the message directly from you, which will make them feel more involved and provide a clear understanding of what they need to do to be successful. Being able to put faces to names, as well as having direct communication with you, will help external employees feel included. Drive engagement through recognition programs: Research has shown that employees are more engaged in their work when they receive recognition for their efforts. While you may not be able to provide a monetary award, recognition is still a big driver for engagement. It can be as simple as recognizing a job well done during your video conference meetings, providing certificates of excellence or sending a simple thank-you card to those who are performing well. Either way, taking the extra time to make your external workforce feel appreciated will produce engaged resources that will help drive your business goals forward. Industry training: Your external resources may have the necessary skills needed to perform the job successfully, but they may not have specific industry knowledge geared towards your business. Work with your partner to determine where they have expertise and where you can work together to providing training. Ensure your external workforce will have a solid understanding of the business line they will be supporting. If you’ve decided to augment your staff for your next big project, Experian® can help. Our Analytics on DemandTM service provides senior-level analysts, either onshore or offshore, who can help with analytical data science and modeling work for your organization.