Using Data to Understand a Credit Union’s Underserved Members

Every credit union has underserved members, and data can help credit union leaders understand how to service them better. For a moment, let’s depart from the more standard definitions of “underserved” and consider the empathetic side of this group. A member may be underserved for a variety of reasons – it may be economic, education-related or a choice, just to name a few. By understanding these reasons through the lens of data, a leader can gain insight on how to meet the needs of their underserved members. This article outlines some approaches.

For purposes of this article, the underserved are those members whom a credit union should have a more engaged relationship with, but does not. This brings an interesting perspective when the empathetic element is considered, but it all boils down to the “why.” Each reason for being underserved will likely have its own “why,” which should form a hypothesis, and a savvy leader can use their data to prove or disprove the hypothesis. A credit union leader can perform this exercise with a variety of tools ranging from Excel and pivot charts to a data visualization platform fueled by a dedicated data warehouse. Often these hypotheses will lead to changes in perspective, and ultimately action that influences holistic member engagement rather than products and services alone.

The First Why: Economic

Consider the economic reason for being an underserved member. What’s the “why” behind this? Is it because the member’s income, credit or account history is below the institution’s serviceable standard? Or, more commonly, perceived standards? Or are alternative solutions such as payday loans perceived to be more accessible? To begin the comparison, it is important to have an external context to go along with internal data. Pulling local census data, along with a credit union’s key metrics such as average daily balance as a ratio of direct deposit (from who and how much), overdraft usage (graduating from when to why), and the types of products used versus types of products qualified for, are good places to start. With this data, given context with geographical census data, a credit union can visualize the proportion of members who might be underserved for economic reasons.

If the member group is disproportional to the community the credit union serves, this suggests there may be an opportunity to serve this group better. By leveraging this information, leaders and product strategists can begin to understand the needs of the underserved, and map or configure current product offerings to fit the needs of the underserved. If the member group is proportional to the local populace, which should be expected, then a leader may consider other products that appeal to this member group and position them more prominently. This can be done by making them more accessible, and facilitating education on the features and benefits of the credit union’s mission and services contrasted against the rest of the wallet, or lack thereof.

The Second Why: Education

The education-related reason for being an underserved member is a little more challenging and stems from several underlying inputs. A first look might involve reviewing data for disproportionate draft versus non-draft balances, which suggests an opportunity to educate on better products for those members. Other common ratios that can produce extremes to the bell-curve are transactions by merchant category, standard deviation of aggregate average daily balances, and direct deposit amount to product usage or transactions. Once identified, those members can receive marketing information about CDs, money markets, lines of credit or other options that can enhance their financial well-being and engagement with the credit union. Once key ratios and their outliers have been identified, a credit union can run an A/B test to test the hypothesis – that a better product improves the member’s life and engagement with the credit union – as well as test the effectiveness of the marketing. It is also important to measure, in terms of dollars and effort, the cost of educating a member to set realistic ROI expectations.

What about members who are perpetually using predatory lenders? As with the economically underserved, the data can inform a leader on which members may be vulnerable to these lenders. Identifying transactions with known predatory lenders and segmenting the patterns of behavior of those members can help identify those who are likely to use predatory lenders. Examining soft credit pulls can also shed light on the financial standing of these at-risk members. These habits of at-risk members include outliers in the previously cited ratios and habitual users of overdraft. A leader then can engage with these members proactively and offer a thoughtful series of options ranging from debt consolidation loans, flexible lines of credit, savings programs and education.

The Third Why: Choice

Of these three reasons, this is the most difficult to understand. Why would a member, who is aware of all your products and offerings, choose not to use them? This is the classic “top of wallet” dilemma, which data can answer. It starts with deposits, card transactions and bill payments. Determining if a large percentage of a member’s regular income is being deposited is the first clue to whether a credit union is “top of wallet.” Obtaining reliable employer information – who to expect direct deposits from and how much – can be difficult if good data collection practices have not been regularly sustained, but there are several options for sourcing this data. Employment information can be obtained through examining past account and loan applications, simply asking your member, current ACH deposit records and a variety of third-party sources.

Looking at scheduled bill payments is another option. A member who is well served by your credit union is likely to have several scheduled transactions from their primary transactional accounts going to vital services such as utilities, government agencies and other personal accounts. Card transactions are also very telling. Compare the attributes of those members who use your card to shop at major retailers on a regular basis to those who don’t – there are often regional and national trends of those who go to your card first when buying something online or when standing in line at a particular merchant type.

In the broader market, a leader who is comfortable with data may also use other data sources to see if their credit union is missing out due to choice. Data from the census, NCUA and FDIC will tell a leader how they are performing on market share. This can be done at the city, town, ZIP code or census tract level, depending on the resolution of truth the leader is looking for. If a credit union holds a market share above 6%, it is beating the average, according to PaymentsJournal. If a credit union is below this benchmark, the data suggests there may be opportunities in marketing or product offerings.

Data helps illuminate the truth and a credit union leader conversant in working with data will outperform any peer that is not. Using data to understand your underserved member is a practical first step; from there, a leader can factually facilitate discussions about product and market positioning. While the exercises above help aid in an understanding of the human side of underserved members, the list is not exhaustive. These exercises will certainly get a leader started on the path of data fluency as they seek to understand their underserved members.

Ray K. Ragan, PMP is the co-founder of Clear Core, a data cleaning and transformation provider focusing on increasing the value and accessibility of data for financial institutions, in Tucson, Ariz.

Timothy “Buck” Strasser is the founder of Clear Core in Tucson, Ariz.