Two industry leaders share how to knock it out of the park using automation and analytics.
Hitting a home run requires a lot more than luck. For a baseball team, hitting it out of the park means honing strategy and regular practice. For credit union lenders, getting a homer means fine-tuning the vision, plus implementing best practices in both analytics and lending automation to improve the borrower experience. In our special report, two industry leaders share key strategies and smart tactics that can help CUs score.
Go Smart Yet Simple
Lending has evolved greatly over the years. But the data analysis CUs can now do is what has changed the game the most.
“The analytics are really allowing financial institutions to gain efficiency in their process,” says Stephenie Williams, VP/financial institutions product & strategy for CUES Supplier member Vericast, San Antonio, Texas. “When you think about application pipelines, they’re able to approve more deals, and at the same time, analytics is also allowing financial institutions, and credit unions in particular, to make more relevant offers to their customers and members.”
The most recent changes in analytics are the new services from credit bureaus and analytics companies in the market, according to Williams. One example is Zest AI, which works with financial institutions to fully understand their risk tolerance and map that tolerance back to credit data. In many cases, multiple credit attributes are consolidated to identify solid opportunities, allowing the CU to increase its volume of approvals or preapprovals.
“Beyond that, ... with the addition of non-bureau data factors—such as utility payments and rent payments—they (lenders) are using those as data points to give consumers more credit access. Customers who have been credit invisible—as many as 3 million consumers—are now able to obtain entry-level loans as financial institutions begin using additional data to make credit decisions.
Advancements in analytics also allow CUs to improve their lending services and offerings. Beyond saying yes to more applicants, analytics can be used to provide more value to members.
“For instance, look at your members’ credit bureau data and analyze outstanding revolving debt,” Williams says. “Using the results, make an offer to consolidate into a single term loan. When you depict a payment savings, that can be a big value to that member. The same can be done for auto loan refinancing, making recommendations depicting the payment savings for members.”
CUs can use analytics to gain an edge over banks when it comes to lending. “What is going to give them an advantage is really making the process simpler,” Williams says. Some banks are doing it, but they’re not doing it very quickly. Credit unions are nimble, and using that nimbleness will prove a real asset. Partnering with a fintech if they don’t have the resources is a great way to organically grow some of these analytic practices and automations.”
The way CUs market their loan services affects whether they get a base hit or a home run. To score, Williams recommends thinking like a member and making borrowing easy to understand by using terms like “debt consolidation” or “tuition loans.”
“Use that plain language to put proactive offers in front of your members on a regular basis,” she advises.
It’s also crucial for CUs to keep up with the times. For example, the old-fashioned way was to offer a home equity promotion in the fall and an auto promotion in the spring, but now these deals need to be more continuous, because the buyers are making these purchases year-round, notes Williams.
“When you’re putting those offers in front of members, you’re getting the chance as their financial partner to get the first consideration, because they’re going to be familiar with the fact that you’re ready to lend,” she says. “But don’t think the fact that you’re being proactive with them is going to get all their loan business as a credit union; you still need to monitor and look out for your members. You can do that with credit monitoring.”
Dig Into Data and Your Process
One of the best things about lending is that it provides CUs a great opportunity to work with their members. A mortgage application is perhaps the perfect combination of gathering member data and having a personal interaction with them.
“The mortgage application is probably the most complete data set; you have a financial picture of the entire household,” says Steve Hewins, SVP of CUESolutions provider CU Members Mortgage, Fort Worth, Texas. “The mortgage application covers everything from who lives in the house, all of their assets, their income, their debts, the number of children, the age of the children, education and so on. ... If you have the right ability and aptitude to think about it, you can plug that in” as you look for future opportunities.
If a child on a mortgage application is 10 years old, for example, they’ll be of driving age in five or six years, making that a perfect time to send the family information about car loans, Hewins explains. You can also use mortgage application data to calculate when they’ll be paying off student debts or looking for a refinancing solution.
It’s also important to look at the analytics related to measuring how well your loan process is working, Hewins says. “Look at an application and the experience that users have: How quickly do they move through the application? Are there pages that they get stuck on or drop off?” he asks. “Then use those analytics to go back and improve the user experience to hopefully get more complete applications and less dropped submissions.”
Currently, the biggest change in lending analytics is the number of players coming into the space to offer assistance, according to Hewins. “The biggest game-changer is probably finding the right partner because most credit unions are not going to have the ability to staff those kinds of positions,” he says.
Hewins also thinks that learning to interpret the goldmine of data in a mortgage application can give CUs an advantage over banks when it comes to lending.
“If you’re using data to be predictive of your members and what their needs are, you can cut off competition,” Hewins says. “But everyone has the same idea of using big data to do predictive modeling to win them over, so it’s becoming table stakes. If you’re not looking at data, if you’re not making these decisions, then you’re being left out of the game.” cues icon
Celia Shatzman has penned stories on topics ranging from beauty to fashion, finance, travel, celebrities, health and entertainment.