Forward-thinking credit unions are gathering and applying data analysis insights across functional areas.
Credit unions are becoming modern prospectors, digging not for gold or oil but for the wealth of data tucked away in systems across their and their partners’ organizations. Bringing this data together to be parsed by analytics solutions is helping to fuel advances in marketing and address diverse business problems.
Reinforcing Value Proposition
BlueShore Financial, a credit union based in Vancouver, British Columbia, has grown over the last 18 years from $950 million in assets to $5.7 billion—with roughly the same number of members.
“Our strategy is focused on deepening relationships with existing clients and doing more business with them rather than a primary focus on acquiring new clients,” says CIO Fred Cook. “We’re focused on building share of wallet with our base of 40,000 members.”
Despite reservations among some observers in the financial services sector that credit unions can compete in the wealth management space, BlueShore Financial has carved out a strong niche in this area. In a fiercely competitive market, “processing transactions is just table stakes. It’s really about how you differentiate yourself,” Cook notes. “It’s about the conversation you have with your members, and we lead with the wealth management discussion.”
With its core purpose of “passionately improving our clients’ financial well-being in an interconnected, digital world,” BlueShore has deployed data analytics to inform member conversations with a clearer view of what products and services will best serve their needs.
The credit union began using data to guide interactions with members in 2000, Cook says. “Gaining insights from our CRM (customer relationship management) system created a thirst to learn more and get more into the art of data, but more importantly, to discover [how to] get results from that and use them to better serve our members.”
In 2010, after converting to the new core banking system of CUES Supplier member Temenos, BlueShore Financial also began using its partner’s data analytics system. This allowed the credit union “to combine data on how members were using financial products, insurance and wealth management services, which in turn provided a broader, 360-degree view of their lifestyles to facilitate a deeper conversation,” Cook explains.
Bringing together information from its core banking, CRM and other internal systems along with data supplied by its investment, insurance and card partners provides a more complete picture of members’ financial standing. This supports “relaxed, professional and frank discussions” rather than an interrogation session, Cook says.
“Money is an emotional topic when members are talking about their savings and financial wellness,” he notes. “Financial planning conversations with a BlueShore advisor factor in both immediate needs and long-term goals and always keeps the clients’ best interests in mind.
“We consider our Temenos Analytics engine to be strategic to our growth,” Cook says. “A lot of financial institutions are based around their core banking systems. We look at our transaction system as a product service engine, but it’s not an insight engine.”
Managing Tough Business Problems
Soon after $409 million Our Community Credit Union, Shelton, Washington, began using the OnApproach data analytics solution (now offered by CUES Supplier member Trellance) in 2015, its original sponsor, Simpson Lumber, sold its three mills, and the new owner immediately issued termination notices for all 330 employees. Over the following weekend, CIO Andrew Bertrand put the new system to work identifying the percentage of members who were now unemployed, based on ACH direct deposit data, to recalculate default probabilities.
“By Monday, we had a report to share with the board and examiners on the environmental risks and what we should put in reserves,” Bertrand says.
In the years since then, the credit union—serving 33,100 members—has deployed data analytics across its operations:
- to adjust loan rates and credit tiers, based on data on historical losses and a view of competitors’ offerings. The result has been “lower rates and improved yields, because we’ve made more loans with less charge-offs,” Bertrand notes.
- to evaluate transaction volume and wait time at locations with shared branch services, with additional data from the CRM system. Some branches now have a teller station designated for shared branch transactions to speed up service for the Our Community CU members in other teller lines.
- to manage indirect loan volume more nimbly. With the ability to monitor this channel on a daily basis rather than through monthly reports, loan managers recently saw indirect loans grow from an average $2.3 million monthly to $4.2 million and then trend up to around $8 million. “If something changes that drastically, it warrants attention,” Bertrand says. “We quickly had a meeting to adjust our pricing and some of our dealer incentives to get volume down to a better level for us in managing interest-rate and other risks.”
Our Community CU’s primary objectives in wielding data analytics are “to reduce costs, improve efficiencies and increase revenue” by bringing together data from its core, marketing customer information file, CRM, online banking and other systems, he explains. To support marketing, the credit union relies on member engagement, next-best product and attrition models that tie into daily emails generated by specified triggers.
A Strategic Pivot
Idaho Central Credit Union, Pocatello, has grown from $400 million to $4.5 billion in assets since CIO Mark Willden, CCE, joined the CU team 14 years ago and now serves 363,700 members. “A lot of our growth has been through marketing, member referrals and the expansion of branch services across the state. We realize expansion of branch services is an expensive way to go and needs to change over the long haul,” says Willden, a CUES member. “We need to become more sophisticated with how we target new and existing members.”
Toward that end, Idaho Central CU signed on with CUESolutions provider AdvantEdge Analytics, Madison, Wisconsin, in late 2017 to leverage data to identify next-best products for members, analyze products and services that are performing well and pinpoint areas that might benefit from improvements to provide a better member experience, he says.
One of its first data analytic projects is an attrition model designed to predict which members might be on the verge of moving their accounts. The credit union launched an outbound calling program to those members in June. Initial results have been positive. “Members are interested in what we have to say and are actually looking at additional products and services with us as a result of those calls,” Willden reports.
Over the years, BlueShore Financial has customized some of the algorithms offered by Temenos Analytics into its own proprietary “Blue-gorithms,” such as a client engagement score that measures members’ financial and behavioral elements. These insights allow advisors to provide informed, tailored and expert advice to members.
“Our focus on analytics has been to secure our value proposition and grow our business with the share of wallet strategy, which ripples around the edge of our data management strategy,” Cook notes.
As technology reshapes financial services, credit unions’ appetite for data analytics will increasingly expand, he says, noting an example of gathering deeper e-payments information in the Internet of Things era: CUs will benefit from working with payments processors to receive data beyond basic notifications, such as the fact that payment was authorized from the member’s internet-enabled vehicle in the coffee shop drive-up lane.
“We’re always looking for additional data outside our own collection points from business partners. It’s not just about spending patterns,” Cook adds. “Using AI and machine learning, we can learn a lot about our members’ needs and preferences. Analytics can help us assess whether we’re truly providing what members value. It can help us tighten up service delivery and better understand member personas.”
The “Blue-gorithms” go beyond helping the credit union segment its membership, test for price sensitivity and identify next-best products, says Todd Winship, Temenos North America product director/data and analytics. “BlueShore has done a good job by coining that phrase to get the whole organization to understand what they’re trying to do, how data analytics benefit the credit union and ultimately how those Blue-gorithms benefit members.”
Sharing Business Intelligence
Rather than assigning data analytics as the purview of a single department, Idaho Central CU created Report Central to encourage team members across business units to generate their own analyses and make data-based decisions. For example, a card services manager could go to the portal and generate reports on how the card portfolio has grown over time, dissecting that activity by branch or region. The portal can also facilitate operational reviews and routine functions such as balancing, Willden notes.
“Those data-based insights are helping us make better decisions, understand where growth is coming from, and see where products and services may be slowing down,” he says. “By building this portal, we’re enabling the entire organization … to leverage the power of the data warehouse. If it’s all behind the scenes and only a few isolated people can use it, you’re not unleashing its full power.”
BlueShore Financial has adopted a similar strategy to “democratize” reporting and analysis—“to push out a broader understanding and access, so that each department has a person who is intimate with the use of data analytics as it relates to their business area,” Cook says.
The IT staff gathers and cleans the data before making it available to business units, and data analytics specialists are available to assist retail teams with more complex applications, he notes. But each department can generate its own reports, and branch managers and financial advisors can drill down into the data that supports their balanced scorecards.
“We’ve really adopted a data analytics culture,” he says.
A few years back, $460 million CBC Federal Credit Union relaxed its underwriting standards for credit cards and had begun to run into some delinquency issues, says Scott Norris, who left the Oxnard, California, credit union as chief sales and marketing officer in 2015 and returned as chief lending officer in 2018.
The credit union began working with CU Rise Analytics, a credit union service organization based in Vienna, Virginia, to analyze its credit card portfolio, identify higher-risk accounts and recommend a course of action. CBC FCU closed credit lines for the highest-risk accounts—those with a one-in-three likelihood to go to charge-off—to reduce its losses and began the process of reducing credit limits on several hundred other accounts, Norris says.
The CU Rise model analyzes 28 attributes, including high utilization and over-the-limit and late payment fees, to evaluate credit risk, he explains. “We were able to quickly bring that little credit bubble to a peak and turn the corner.”
Following on that initial success, CBC FCU is now planning another data analytics initiative to market its Epic checking account, which includes ID theft reimbursement insurance, smartphone insurance and a retail coupon app powered by BaZing, to members with similar profiles to existing subscribers. The credit union is also planning to offer credit card line increases totaling $2.7 million to qualifying members vetted as likely to put that extra credit to use—without the previous delinquency problems.
“You can do a whole lot with credit card data,” Norris notes, adding that CBC FCU learned a lot about its members just in the process of assembling and validating data in preparation to launch its analytics solution.
A Running Start
For many credit unions today, the key question is not whether to get into data analytics, but how, says Shazia Manus, chief strategy and business development officer for AdvantEdge Analytics. Launching these initiatives is a top strategic priority across the movement, and organizations are at various points along the “crawl, walk, run” continuum of implementation.
“They all believe in the potential of data analytics, and there is a lot of excitement, but they’re also trying to understand what is hype and what is noise so they can cut through that to leverage their data in a powerful way,” Manus says.
When Idaho Central CU went looking for a partner to build a data warehouse and launch a data analytics program, its top priority was finding a data model that was already functioning. “We had talked to a number of credit unions that built their own data model. They’d build it, and it wouldn’t quite work, so they’d build some more,” Willden says. “It would take three iterations to get to a data model that was functioning well. We didn’t want to go through that five- or six-year process.”
The credit union is still early in its journey, he adds, but just rolled out an insights module to help speed up the analytics process. The standard approach had been for teams studying operational changes to generate a data report, which prompted additional questions or a request for more in-depth information in a particular area. That led to successive rounds of reviews and more reports.
“By the time you get those answers, you might have forgotten what your initial question was,” Willden notes. “The insights module is more interactive, with the ability to filter and sort and do what-if scenarios very quickly with various product lines. We just rolled it out, and the feedback we’re getting is very positive.” “Like all technology projects, it’s taken longer than we might have hoped. We’re pulling in data from multiple sources to build these kinds of insights,” he adds. “We are involving the business lines deeply to make sure we’re building this in a way that actually meets their business needs. Data analytics and business intelligence are a journey. We are going to be doing this forever, continually tweaking and looking for new ways to use the data for deeper, better insights and new sources of information.”
Realizing Full Potential
SafeAmerica Credit Union aims to optimize the data analytics capabilities of its Marquis MCIF system and consultations with CULytics in marketing to its 37,000 members, says Steven Page, VP/IT, marketing and digital banking with the $460 million Pleasanton, California, credit union.
“Our marketers can work smarter by using information we already have about members to serve them better,” Page says. “Without data analytics, you’re just throwing darts in the dark.”
SafeAmerica CU combines product usage data from its core system with demographic and credit bureau information to target offers from auto loans and mortgages to student loan refinancing. CULytics provides “another set of eyes” and a wider industry view on its data analytics implementation and execution, he says.
The credit union has set automatic triggers in its MCIF system to automatically generate onboarding letters to new members and other offers to members based on behavioral (e.g., paying off a car loan) and life stages information. The more closely timed and attuned to member needs its data capabilities become, the better the return, Page notes.
“With every campaign, you measure and watch. You’re not going to get a bull’s eye every time, but that’s what you’re aiming for,” he adds.
Bertrand notes additional advantageous side effects of implementing a data analytics solution. “One of the main soft benefits for us was the need for clean data that will help us in the future if we’re going through a core conversion or need to change some of our frontline processes,” he says. “We found things like the fact that there are 37 different ways to spell Chevrolet, apparently. And we’ve been able to improve our processes because we can better track results. Is a new process really faster and better? We can answer that question.”
Evolving Data Regulations
Credit unions should be able to rely on their data analytics partners to help navigate new compliance requirements, Cook suggests. “As we look at new regulations coming over the horizon, especially the GDPR (General Data Protection Regulation) and PSD2 (revised Payment Services Directive) coming from Europe, we know some flavor of them will be coming to Canada. We’re just not sure how they’ll be rolled out.” The same concern applies to U.S. credit unions.
And with the emerging Open Banking Standard, “data ownership will shift, and we’ll be a caretaker of our members’ data,” he says.
“As much as people talk about bitcoin, the new digital currency is really data,” Cook adds. “If you lose control of the data, you lose control of your relationship with members. That’s the tsunami we see way out there now, and we know some form of it is going to make its way to our shores. We’re having conversations with Temenos now about how we can prepare and keep ahead of the curve.” cues icon
Karen Bankston is a long-time contributor to Credit Union Management and writes about credit unions, membership growth, marketing, operations and technology. She is the proprietor of Precision Prose, Eugene, Oregon.