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

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

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