The Innovation Series is celebrating 2026 with a diverse slate of finalists whose breakthroughs are reshaping member experience, data and business intelligence, lending, employee engagement, fraud prevention, and digital member engagement — all with the power to help credit unions thrive in a rapidly evolving marketplace. Register for the Innovations In Reimagining The Lending Experience webinar  on Thursday, March 12th at 2PM EST.

Read on to learn more about this year’s finalists in lending:  Cloudvirga, Enable Technologies, Fuse, Suntell.

Cloudvirga:

Describe your Innovation:

Tropos is a configurable loan point-of-sale (POS) platform that sits in front of the loan origination system, connecting the core and third-party services through a unified, API-driven experience. The platform manages borrower and sales staff interactions, data intake, document collection, and real-time validations while seamlessly passing clean, structured data into the LOS. By acting as an intelligent engagement and integration layer, Tropos improves data quality, reduces rework, and enables credit unions to deliver faster, more consistent digital lending experiences without modifying their core or downstream systems.

What opportunity or challenge does it address?

Credit unions face increasing pressure to deliver modern, digital-first lending experiences while operating on fragmented legacy systems that were not designed to work together across all loan types (residential mortgage, consumer, auto, etc). Disconnected cores, LOS platforms, and third-party services often create inconsistent member experiences, duplicate data entry, and operational inefficiencies that slow decisioning and funding. Tropos addresses this challenge by acting as a flexible point-of-sale layer that unifies borrower and staff interactions, standardizes data capture, and integrates seamlessly with existing systems — allowing credit unions to modernize lending experiences, improve data quality, and scale efficiently without costly core or LOS replacements.

How does it increase member value?

Tropos increases member value by removing friction from the lending experience and giving members a faster, more transparent path from application to funding. By unifying data capture, document collection, and third-party integrations within a single point-of-sale experience, Tropos reduces repetitive questions, minimizes errors, and shortens turnaround times. Members benefit from intuitive digital interactions, real-time status visibility, and fewer follow-ups — while credit unions deliver a consistent, high-quality experience that reinforces trust, loyalty, and long-term member relationships.

What differentiates this innovation from competitors?

Tropos is differentiated by its role as a true system-agnostic point-of-sale layer that decouples the member experience from the underlying LOS, core, and third-party providers. Unlike POS solutions that are tightly coupled to a single LOS or require time-consuming and expensive customization, Tropos uses easily configurable workflows, API-driven integrations, and real-time customizations to adapt to each credit union’s existing ecosystem. This allows credit unions to modernize the member experience, maintain flexibility to change downstream systems, and avoid vendor lock-in—while preserving control over data, compliance, and operational processes.

Enable Technologies:

Describe your innovation.

Enable is redefining origination by bringing deposits and loans together on a single, AI-driven platform. We’ve built a unified experience that works seamlessly across digital, branch, and call center channels, powered by AI that guides both end users and frontline staff in real time. It’s not incremental improvement but rather it’s a fundamental shift in how financial institutions originate full long-lasting relationships.

What opportunity or challenge does it address?

For years, credit unions have been forced to operate with fragmented systems. One for deposits, another for lending, whereby creating friction for members, staff, and operations teams. Enable eliminates that disconnect. We address the growing need for speed, simplicity, and intelligence while helping institutions keep pace with rising expectations, compliance complexity, and competition from larger banks and fintechs.

How does it increase member value?

Members and business owners get faster decisions, fewer handoffs, and a more intuitive experience whether they’re opening an account, applying for credit, or bundling products in a single journey. Behind the scenes, AI helps surface the right products, answers questions instantly, and reduces errors which ultimately translates into better service, stronger relationships, and higher satisfaction.

What differentiates this innovation from competitors?

Most platforms still treat deposits and loans as separate workflows. Enable was built from day one as a unified platform with AI embedded at the core – not bolted on. As a member of the executive leadership team, I’ve had the great privilege of seeing Enable evolve rapidly alongside our amazing client partners — such as Nuvision Federal Credit Union ($3.9B, Huntington Beach, CA) and Meriwest Credit Union ($2.1B, San Jose, CA) — incorporating real-world feedback into smarter automation, conversational AI, and flexible configuration. That pace of innovation and our ability to deliver value quickly is what truly sets Enable apart. What further differentiates Enable is the experience behind the platform. Our founders and executive leadership team bring decades of hands-on experience partnering with more than 50+ credit unions with a proven track record of delivering on commitments and turning vision into reality. That history of execution exellence and the long-standing trust we’ve built with innovative credit union partners is something we take great pride in and continue to earn every day.

Fuse:

Describe your Innovation:

Fuse is an AI native loan origination system (LOS) and account opening (AO) for credit unions and other consumer lenders. Fuse is backed with $25M+ from the top-tier investors behind Chime and Alloy, being used by 100+ institutions including Navigant Credit Union ($4.1B, Smithfield, RI) and is the one LOS that FIS re-sells to top 50 banks (selected after reviewing 10+).

Credit unions need to automate to compete with the fintechs and top 10 banks. All traditional LOSs say their platform can deliver “up to 100% automation,” but since that has not materialized in decades, credit unions are rightfully skeptical. This is why Fuse sells its proactive automation money-back guarantee:

• Proactive: Many CUs stall on automation because their staff is overwhelmed with the needs of the day to day. We know people are 71% more likely to go to the gym if there’s a personal trainer nudging them, and Fuse implemented a similar nudge. Every two weeks, the CU meets with a Fuse automation specialist who showcases the automation opportunities identified by our Proactive AI Lending Copilot, and the aligned automations get implemented by either Fuse’s team or the CU. The goal is to get 1%+ more automated every week, leading to >60% true improvements year on year.
• Money-Back Guarantee: CUs are skeptical of being promised opportunities that then do not materialize. That is why Fuse implemented the first industry money-back guarantee in both product capabilities and company practices. Our contract incorporates guarantees so they do not need to rely on salesmanship or nice words. Some examples:
  • Product Capabilities: The system is able to decision on 100% of their core data and board 100% clean applications to their core without post-boarding clean up. The system can lead to 100% auto-decision based on any field or source of data.
  • Company Practices: Integrations are free forever (install and maintain), can be self-built by the client in a 100% open ecosystem or built by Fuse with a guarantee of delivery in under one month, and can be bought directly from partners rather than through mark-ups of up to 2x.
  • Implementation risk coverage: Fuse deploys its own team to run the full implementation rather than relying on CU staff, and assumes the risk on timelines by tying commercial commitments to agreed go-live dates, including covering additional costs from the prior LOS if those contracts need to be extended.
  • Ongoing improvements: After go-live, Fuse contractually commits to meeting every two weeks to review and implement new automation opportunities and provides real-time chat support with short SLAs and an assigned team that knows the institution’s configuration and can unblock issues quickly.

What opportunity or challenge does it address?

The core issue is automation skepticism arising from failed vendor promises, forcing CUs into costly manual processes that hinder staff focus and member experience. Fuse counters this with an AI-native LOS platform offering proactive automation with a money-back guarantee. This shifts risk to Fuse, ensuring guaranteed, measurable improvements in efficiency, staff utilization, and member satisfaction through modern technology.

How does it increase member value?

• Streamlined process with no rekeying: Fuse pulls data directly from the core, so members are not asked to enter the same information twice.
• Flexible channel hopping: Members can start an application online and finish it in the branch, or the other way around, without losing progress.
• More valuable staff interactions: With staff work reduced two- or three-fold, employees can focus their time and energy on sharing advice and cross-promotions.

What differentiates this innovation from competitors?

• • Model: Legacy LOS deliver a system, then rely on tickets and internal bandwidth. Fuse takes ownership for automation outcomes, not just feature delivery.
  • Technology: Our AI copilot continuously scans underwriting and funding workflows in production to surface concrete automation opportunities. Institutions can move toward near-total automation while setting their own pace and risk thresholds.

• Operating cadence: Every two weeks, our team meets with the credit union, reviews real data and brings specific rule, workflow and integration changes, then helps implement them so improvements actually go live.
• Alignment with credit unions: We do not charge for integrations and keep an open ecosystem, so credit unions can use the partners and tools they prefer without extra friction or hidden costs.

Suntell:

Describe your Innovation:

Square 1 Credit Suite is Suntell’s purpose-built credit lifecycle platform for member business lending at credit unions. It was designed specifically to help institutions bring MBL in-house and scale it responsibly, rather than relying on adapted bank systems, outsourced workflows, or external decisioning models.

Square 1 standardizes how business credit is evaluated, documented, and reviewed, allowing lending teams to make faster loan decisions while maintaining consistent credit standards. By creating clarity across lenders, reviewers, and leadership, credit unions are better able to explain decisions, support exam expectations, and deliver a more predictable experience for business members.

The innovation behind Square 1 is its focus on credit discipline as a growth enabler. Credit unions retain ownership and control of their lending process, enabling them to modernize MBL operations in a way that strengthens member relationships rather than outsourcing them.

What opportunity or challenge does it address?

Credit unions face increasing demand for member business lending, but many struggle to grow MBL without slowing decisions or losing consistency. Manual processes, fragmented tools, and lender-to-lender variability often lead to longer turnaround times and difficulty clearly explaining outcomes to members, boards, and examiners.

To manage this complexity, institutions often rely on manual workarounds, spreadsheets, email-driven processes, or disconnected systems to bridge gaps in the lending process. While these approaches can help in the short term, they can limit control, reduce transparency, and make it difficult to scale MBL in a sustainable way.

Square 1 addresses this challenge by giving credit unions a structured framework to manage business credit internally. The opportunity it unlocks is responsible growth. Institutions can move faster, maintain disciplined credit standards, and deliver more consistent experiences for business members without introducing unnecessary complexity.

How does it increase member value?

Square 1 increases member value by improving how credit unions respond to business lending requests. By standardizing credit analysis and credit file content, lending teams reduce rework and delays, resulting in faster loan decisions and clearer communication with members.

For business members, this creates a more predictable and transparent experience. Decisions are based on consistent credit standards rather than individual interpretation, which builds trust and confidence in the credit union as a reliable financial partner. Members spend less time waiting for updates and more time focusing on their businesses.

By enabling credit unions to manage member business lending internally, Square 1 also strengthens long-term relationships. Credit unions maintain direct ownership of decisions and outcomes, allowing them to support local businesses with dependable access to credit while staying true to their cooperative mission.

What differentiates this innovation from competitors?

Square 1 is differentiated by how it aligns with the operational reality of member business lending at credit unions. In many institutions, MBL teams operate with distinct workflows and priorities that differ from enterprise-wide banking systems.

Square 1 was designed to deliver value within that environment by providing a cohesive credit lifecycle framework for underwriting, credit file consistency, and portfolio oversight. Institutions are able to improve credit discipline and decision speed without first undertaking complex, enterprise-scale integration projects.

This allows credit unions to strengthen member business lending without overengineering their technology environment. Teams can focus first on credit consistency, decision quality, and adoption, while maintaining flexibility to evolve processes and integrations over time as needs change.

Check Out The Other Innovation Series Categories

• Employee Enablement
• AI-Powered Member Experience
• Data And Decision Intelligence
• Fraud Prevention And Resolution
• Digital Member Engagement

The post Meet The Finalists For The 2026 Innovation Series: Reimagining The Lending Experience appeared first on CreditUnions.com.

As credit unions look to deepen their impact, helping members take control of their credit presents a powerful opportunity. The generational spread in average credit scores tells a compelling story.

While these numbers rise steadily with age, they also reflect disparities in access, experience, and education. Many younger members are still building financial habits, often doing so without clear or accurate guidance.

In fact, nearly one-third of U.S. consumers don’t know their credit score, and more than half don’t understand what a FICO score is. Credit misconceptions also persist, such as the belief that checking your credit can hurt your score and that more credit cards automatically equate to a higher ranking. These knowledge gaps limit financial progress, especially for younger members. Data shows that only 54% of 18–24-year-olds know their credit score compared to over 70% of adults ages 25–64.

Compounding financial pressure amid a high-interest rate environment is revealing measurable impact with younger generations. Between 2022 and 2024, average credit card debt for Gen Z rose 62%, while millennials saw a 50% increase. The percentage of consumers with subprime credit scores is also rising across both groups.

Yet, despite the challenges, younger generations are actively seeking help. Over 70% of Gen Z and millennials report having financial questions — but they struggle to access trusted advice. This is where credit unions can make a difference.

By delivering practical education, offering responsible credit-building products like secured cards, and guiding members through tools that support everyday financial decisions, credit unions can empower members to build lasting credit confidence.

However, when offering these tools in-house, credit unions often face practical constraints such as limited liquidity to absorb charge-offs, tighter risk tolerance, and operational challenges in expanding underwriting. These limitations can prevent even the most member-focused institutions from offering the kind of credit access and support many members are seeking today.

By partnering with Elan, your cardmembers gain access to a full suite of products and tools to strengthen credit literacy and support responsible usage. Download the full whitepaper to access more insights into what members know about their financial health, what they want to learn, and how credit unions can step in as trusted partners to build financial confidence.

The post Bridging The Credit Confidence Gap Across Generations appeared first on CreditUnions.com.

Top-Level Takeaways

• Credit unions experienced increased share growth in early 2024, particularly through higher-rate CDs, which helped them reduce borrowing despite rising deposit costs.
• Langley FCU shifted its focus from overall deposit growth to helping more members achieve modest savings.
• It introduced a high-yield savings account and is using behavioral economics and digital nudges to encourage systematic saving among members.

In an era where financial stability often feels as fleeting as a trending hashtag, Americans are finding refuge in an increasingly popular place: credit unions. But while many financial cooperatives are focused on attracting deposits with high-rate certificates, Langley Federal Credit Union ($5.4B, Newport News, VA) is taking a different tack.

Share growth at credit unions nationwide picked up slightly in the beginning of 2024 as members flocked to higher-rate CDs. Although growth in certificates far outpaced the rest of the share portfolio —  43.0% year-over-year versus -9.8% for money market accounts and -9.4% for regular shares and deposits — balances in all share categories increased in the first quarter.

That growth gave credit unions enough wiggle room to reduce borrowing, which is traditionally a more expensive way to raise liquidity. However, after several years of relatively stable deposit costs, these, too, are on the rise.

“Much of our deposit growth — as is the case across the industry — has been through CDs,” says Curtis Baker, senior vice president and chief lending officer at Langley FCU. “You’re really just buying the money. You throw out good CD rates, and you try to win the market. It’s very expensive.”

But that’s not the only problem. Buying money also presents a philosophical quandary for institutions whose mission is to serve members of modest means. That’s because those high CD rates tend to attract more affluent depositors putting larger balances onto the books.

That didn’t sit right with Langley. So, at the end of 2023, the Virginia cooperative challenged itself to think more broadly about its deposit strategy.

“We made significant changes to our strategic goals for 2024,” Baker says. “The first was to focus less on overall deposit growth and more on helping many more members achieve a modest amount of savings.”

New Challenges Require New Ways Of Thinking. After a mid-decade push to rightsize its balance sheet, a Virginia cooperative understands how to pivot when times get tough. Read more in “In An Uncertain Economy, Langley FCU Rewrites Its Playbook Again,” available today on the Callhan client portal.

A High Yield For Low Savers

A look at its membership revealed roughly 100,000 members — or slightly more than one-quarter — held more than $400 in savings. Put another way, nearly 75% of its membership would have trouble covering an emergency expense of a few hundred dollars.

The credit union knew it could do a better job helping the average member build a safety net.

In late 2023, Langley FCU introduced a high-yield savings account with a 5% APY that maxes out at $10,000.

“It’s the best yielding product we have,” Baker says. “Our goal is to get as many people as we can to that product, but it’s not for those who are going to put in $100,000, $200,00, or $1 million. We’re extending our best rate to a more modest amount of savings.”

That 5% rate is on par with CD offers from other financial institutions that require an initial deposit of $500 or more. At Langley FCU, high-yield savings requires no minimum balance; it does, however, require enrollment in e-statements.

As it’s a reverse-tier product, balances higher than $10,000 receive 0.05% APY. The same rate applies if withdrawals exceed one per month. Notably, savers can only open the account digitally — no branch visits or phone calls — and accounts are capped at one per member.

Penny By Penny, Dollar By Dollar

Langley’s high-rate savings account encourages members to systematically save a little scratch every month. That’s an essential component of this product because these account holders haven’t built a savings habit and might not have much to set aside at the end of the day.

“It’s going to take some time to get to $400 or more because we serve a working poor demographic,” Baker says.

To that end, the credit union is looking to behavioral economics for strategies to nudge members to save through digital banking and other means. It is testing such strategies via a monthly email to members that includes multiple “nudging insights” to open a high-yield savings account, set up a recurring/automated savings plan in digital banking, set corresponding savings goals, join the credit’s savings challenge, or take advantage of Langley FCU’s step-up savings plan.

The credit union also provides members with their credit score every month and alerts them via email when there’s a change. This reminds them to keep logging in to check their score and keep track of their products and transactions.

“We want to encourage our members to use our digital channels, especially for transactions,” Baker says. “Members should be engaged with their balances and their products and services.”

Of course, sometimes, a gentle nudge just isn’t enough. That’s why the current product encourages setting up a systematic savings effort through automated transfer in digital banking.

“We’re thinking about how we might get more aggressive in forcing our members to confront this issue, which is lack of savings,” Baker says.

Performance And Results

To date, total balances for the 9,500 members who have opened a high-yield savings account exceed $57 million.

At $12,238, Langley FCU’s average share balance per member as of March 31, 2024, was lower than its peer group of credit unions over $1 billion in assets, according to data from Callahan & Associates. That’s to be expected of a credit union that serves a “working poor” demographic and is why Langley introduced high-yield savings to begin with.

AVERAGE DEPOSIT BALANCE
FOR U.S. CREDIT UNIONS
© Callahan & Associates | CreditUnions.com

“More people can enjoy [high-yield savings],” Baker says. “It democratizes our impact instead of focusing efforts on growing CDs.”

Langley FCU’s high-yield savings account is still in its early stages; however, quarterly performance data suggests the credit union is helping members save more. In the first quarter of 2024, Langley’s 6.3% share growth was much higher than the nationwide average of 2.4%.

SHARE GROWTH
FOR U.S. CREDIT UNIONS
© Callahan & Associates | CreditUnions.com

The credit union is posting this growth while falling slightly below peers on earnings, with just 0.50% in assets. That drop in ROA comes from socking away more in provision expenses; again, not surprising for a credit union that serves a poorer membership. Still, with an efficiency ratio of 74.2%, Langley FCU is bringing in revenue with limited overhead.

Ultimately, the credit union is performing well and Baker is confident Langley FCU’s pivot from total deposit balances to members with more than $400 is going to pay off for both the credit union and its members.

“I wish we did this going into 2020 versus 2024, but I’m glad we did it,” Baker says. “We said we’re going to be faced with buying money in the form of CDs. Let’s not do that. Let’s take this opportunity to make a major change in the way we do business and deliver on our vision more directly.”

The post Langley FCU Offers Financial Refuge For Struggling Savers appeared first on CreditUnions.com.

Top-Level Takeaways

• Consolidated Community has launched a security deposit loan and other housing products to help borrowers in pricey Portland.

• Grant money provided by the Northwest Credit Union Foundation and CDFI Fund underpin these new housing products.

Consolidated Community Credit Union ($252.4M, Portland, OR) is an accomplished mortgage lender that is now taking on its local housing crunch by helping renters, too.

Consolidated has partnered with the Northwest Credit Union Foundation and two other local cooperatives $93 million Point West Credit Union and $120 million Trailhead FCU to offer a security deposit loan that is designed to help people find a better place to live.

“Consolidated’s website describes the loan as a way to remove the financial barriers of rental housing. The hot Portland market can make those barriers especially high,” says Consolidated president and CEO Larry Ellifritz.

According to Ellifritz, the average monthly rental of a one-bedroom, 764-square-foot apartment is $1,548; a three-bedroom house averages $2,752. And to buy? The average home price is $452,473. By comparison, Abodo puts the national median rent for a one-bedroom apartment at $1,025, and Zillow says the median home listing price in the United States today is $282,000.

It was a real eye-opener to see how underserved and almost abandoned this demographic is in our community. When a member is hugging you at the end of a loan closing, you know you’ve done something right.

Covering The Costs

“The unsecured loan covers two-thirds of move-in costs, including first and last months’ rent, pet fee, security deposit, and more,” Ellifritz says. “Borrowers can take out up to $5,000 with a term of one year and a rate of 4.99%. There’s a minimum credit score of 580 but no pre-payment penalty.”

“It’s a very well-priced product for someone who makes 60% of the average medium income in our market,” Ellifritz says.

The three credit unions, buttressed by a $150,000 grant the NWCUF was awarded by the Meyer Memorial Trust, have underwritten approximately 10 of these loans so far and plans to make 50 in the next three years.

Ellifritz says the loss ratio so far has been approximately 20%, but he doesn’t expect that to continue.

“We’ve learned a ton throughout the year and are projecting a loss ratio of 8% to 10% as the program evolves,” the CEO says.

The three Portland credit unions also are working with a group of Washington state cooperatives that want to offer the same product there, and the Portland trio has their eye toward taking the program national if possible.

Eye-Openings And Hugs

Consolidated might have thought it was simply offering a vehicle to put people into better housing, but the credit union soon learned what it was offering represented more than four walls and a roof.

“We typically don’t interact with these members on a daily basis, so it was a real eye-opener to see how underserved and almost abandoned this demographic is in our community,” Ellifritz says. “One of our most impactful loans was to a single father of two kids that wanted to move into a one-bedroom apartment. He and his kids [ages 7 and 9] had always lived in a studio apartment, so a one-bedroom unit seemed like a dream come true.”

For that member, a loan of only $2,500 helped turn his dream into a reality and made an outsized impact on his life.

“When a member is hugging you at the end of a loan closing, you know you’ve done something right,” says Ellifritz, who has worked at Consolidated for the past 25 years, at the helm for the past eight.

Spreading The Word

Consolidated is a well-established player in the local housing market, yet the rental loan is still an unusual offering because it’s geared toward renters and not buyers. It has opened the door to serve a whole new segment of borrowers but also has presented new challenges in outreach.

“This has been a struggle, finding the right entities to talk to about this loan product,” Ellifritz says.

According to the CEO, marketing so far has focused on local social media as well as community partners, nonprofits, housing authorities, other government agencies, landlords and property managers, the latter through a recently launched email campaign.

Although getting the word out has proved challenging, finding credit unions to participate with Consolidated in the loan’s pilot has not. Ellifritz says the loan was an easy sell to the three credit unions’ boards, and the clear impact the loans have had on borrowers’ lives has naturally increased the compassion of the lenders.

This product falls right in line with our values, the Consolidated CEO says.

So does a package of new mortgage products that Consolidated has rolled out to address the housing crunch in the Portland market.

That package includes:

• ITIN Mortgage Loan: For members who lack citizenship or permanent resident alien status.
• Accessory Dwelling Unit loan: Usually a second mortgage loan to build a second dwelling unit on a member’s property. Also, for rehab projects.
• The 100% Community Home Loan program: A 100% LTV loan designed to help a member who lacks a down payment.

“These are great loans that truly enable members to achieve homeownership sooner than expected,” Ellifritz says.

What’s more, each loan targets a specific situation, such as fast-rising home prices that can make it prohibitively difficult to come up with a 3.5% down payment required for an FHA loan.

“For that, we created the 100% Community Home Loan program that enables members to buy a home with no money down if the closing costs are gifted or the seller pays them,” Ellifritz says.

Using a $700,000 CDFI grant as a funding backstop for the three mortgage plans, Consolidated’s goal is to make $14 million in the 100% LTV loans in the next three years. It has made 10 totaling $3.3 million since October 2018.

“Knock on wood, they’ve all performed great,” Ellifritz says.

In fact, the Portland credit union’s first mortgage delinquency in the third quarter of 2019 was 0%, compared with 0.65% for its asset-based peer group, 0.28% for all Oregon credit unions, and 0.55% for all U.S. credit unions.

Addressing The Housing Gap

The credit union is working to make a significant impact on the affordability of housing, but Ellifritz says his shop doesn’t take credit for this creative approach to home lending.

“These programs were built by members,” he says. “We listened to their requests and built programs around their needs.”

He also lauds Sharee Adkins, the NWCUF executive director, for coming up with the idea for the security deposit loan, Ellifritz says, and pursuing the grant that made it possible.

The NWCUF is due all the credit, he says.

Together the rent deposit and mortgage loan products address what Ellifritz calls a huge product gap for first-time homebuyers and renters with less-than-perfect credit.

These groups are left to pick from a sub-standard product set, the Consolidated CEO adds. Many financial institutions prey on these individuals, and that’s unforgivable. We offer great products so they can thrive rather than start the homeownership journey with two strikes against them.

REAL ESTATE LOAN PENETRATION

FOR U.S. CREDIT UNIONS | DATA AS OF 09.30.19

Of Niches And Partnerships

Larry On Lending

Consolidated Community president and CEO Larry Ellifritz shares best practices for how to create and manage a lineup of lending products that serve an economically diverse membership.

• Hire experienced, savvy lenders that have a background in the specific loans the credit union wants to originate.
• Talk to many lenders before launching a new loan product.
• Go slow. Build in runway to make pivots in the program rather than discontinuing the program at the first pivot.
• Reserve intelligently for loan losses. Don’t be stingy.
• Pay lenders what they’re worth or they’ll find another financial institution that will.

“We’re a $250 million credit union, so we have to be good in our niche, and the niche we have chosen to specialize in is real estate lending,” Ellifritz says. “We decided it’s the way we could have the greatest impact on our members’ lives.”

And to the benefit of the credit union’s members, Consolidated takes a broad view of real estate lending, one that goes beyond its direct market and field of membership. Indeed, the cooperative operates a subsidiary, Consolidated Mortgage Group, that provides support for government-sponsored mortgage and other services to fellow credit unions in six states.

“CMG processed 854 mortgage loans for more than $250 million last year, the most it has ever processed in one year. And, for the first time in its 22-year history, more of those loans were for partner credit unions than for Consolidated itself,” Ellifritz says.

“The mix was about 60/40 partners to Consolidated loans,” Ellifritz says.

Of the subsidiary’s growth, the credit union CEO adds, “Like all things credit union, one CEO talked to another, and now we serve 40 credit unions with more than $2.5 billion in combined assets. I’m amazed how the program has grown, and we’re humbled to serve this great group of member-owned cooperatives.”

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Top-Level Takeaways

• First Financial built its own loan decisioning system that applies artificial intelligence and machine learning to data in the credit union’s warehouse.
• The AI/ML tools integrate with the credit union’s digital application processes and combine with human oversight to offer increased flexibility on loan decisions and counter offers.
• The credit union plans for a summer rollout to the general membership.

There’s nothing artificial about the intelligence First Financial Federal Credit Union ($1.0B, Lutherville, MD) is applying to the new loan decisioning solution the credit union has created in-house.

The Baltimore-area cooperative is applying artificial intelligence and machine learning techniques to the new Anytime Express lending products it plans to launch this summer.

Michael Powers, the credit union’s chief innovation and strategy officer, says trial runs of the decisioning engine have resulted in similar, but much faster, results to those yielded by his shop’s traditional LOS software.

He says that’s especially true for applications that might not fit neatly into underwriting rules and otherwise would require human intervention, a capability that will improve as the system learns from its growing data store of credit scores and payment histories.

We’re making best use of the advantages of AI and human work processes.

To test run the tool, the credit union ran an in-house promotional offer for a $1,000 loan that 32 credit union employees took out.

“When those loans pay off, we’ll have the first complete cycle,” Powers says. “This summer, we’ll move toward exposing this to the general membership.”

First Financial plans to offer low-dollar, short-term loans at first, followed by revolving and auto loans.

Powers calls the new system a significant step up in LOS integration and a first for the credit union industry.

“As far as we know, we’re the first credit union to use machine learning and AI like this to approve a loan application, especially using an in-house system,” the executive says.

Here, Powers explains the thinking and processes behind the credit union’s improved loan decisioning project.

What inspired the creation of First Financial’s in-house AI/ML system for loan decisioning?

Michael Powers: We were motivated by the shortcomings of rules-based automatic decision engines, especially for applicants with less-than-pristine credit. We created an underwriting AI using a blended neural network and decision tree architecture that could work across the credit spectrum.

We used our historical data of unsecured personal loans to train the system, and we obtained good back-tested performance on those historical applications. We now can render a loan decision and turndown reasons, if applicable, in a fraction of a second.

We offered an employee-only loan to conduct an operational test in a benign environment. Applications included credit challenges such as high debt-to-income, delinquencies, and charge-offs. Out of 32 applications, our supervisory underwriting review agreed with the AI in 29 cases. Importantly, we tuned the system to a conservative setting and the AI did not approve a loan application that we would have otherwise denied through our usual process.

Please describe your background in aerospace and how it informs your work now.

MP: I’ve worked previously at a large aerospace and defense firm specifically on the subject of robotics and more recently for a Department of Defense laboratory. One of the many things the aerospace and defense industry does well is to take on game-changing technical challenges and manage the high risk by maturing a new concept through stages described by a technology readiness level (TRL).

That’s what we’ve done here. First, we demonstrated the basic principles in a synthetic, experimental environment. Now, we have completed our first test in a realistic environment, albeit one that is moderated and limited to employees. Next, we are completing integration into a real-time production system. Credit unions can follow this model to foster a rapid pace of innovation but with the care necessary in an environment where mistakes can be very costly.

What kind of automatic decision-making tools were you using before you created your own system, and what were its shortcomings?

MP: Our baseline for comparison is a standard rules-based automatic decision engine that’s included with most modern loan origination systems. Like others, we’ve found it isn’t practical to automatically, and instantly, approve a wide diversity of loan applications in a rules-based system without accepting some compromise in underwriting rigor.

Although rules-based systems work well for pristine or near-pristine credit, the many dimensions of creditworthiness and specific credit history frustrate efforts to write rules to cover substantially all circumstances. Our machine learning system adopted the scope and depth of rules observed in practice by learning thousands of application, credit report, and historical account behavior examples.

What technology and credit risks did you assume in this strategy, and how did you mitigate those?

MP: Technology-wise, we weren’t sure that this approach would work when we first started. We followed the TRL model to mitigate the risk of technical failure. We evaluated our progress at successive TRL stages to determine if the ultimate goal was still within reach. Importantly, our IT and lending departments saw this as an opportunity and a technical risk worth taking.

In terms of credit risk, we did not necessarily see our AI program as a finer underwriting instrument but rather as a faster one that can deliver our full underwriting treatment as fast as a simplistic rules system. It hasn’t been our intent to accept more credit risk with this system. As we continue to refine our approach, we might see decreased credit losses, but that’s not our focus at this point.

How much more flexibility does this system give you in approving lower-credit score loans?

MP: Previously, we would only automatically approve a sub-set of prime credit. This system will let us automatically and instantly approve in all credit tiers. In our employee test, AI/ML correctly approved sub-prime loans and those requiring exceptions, such as debt-to-income exceptions.

We observed examples of a need for underwriting logic that is impractical to manually write into a rules-based system. Importantly, the system did not approve a loan that would have been denied in our standard process.

Does this system use different underwriting criteria than you used to? What are they, if so?

MP: Our approach was to have the system act as close to our current underwriting practice as possible. Basically, the objective was to copy the risk profile we currently accept. In the future, we might use alternative data or specific loan performance data to adjust our underwriting AI practices based on observations.

Can underwriters and loan officers override the system’s recommendations? How does that work in your shop?

MP: Our concept is to start using AL/ML as a yes machine. In other words, to speed up approvals rather than speeding up denials. Our vision is to instantly approve every approvable loan. Applications that are denied by AI get a second look from a human underwriter before we issue an adverse action.

A second review could, for example, result in a counteroffer on a secured loan product if we would not meet the member’s needs with an unsecured product. Or, to see if we could get a co-borrower or guarantor on the loan. Or, find rare exceptional circumstances. We embrace this as an example of human/AI teaming. We’re making best use of the advantages of AI and human work processes.

Describe the process of building the underwriting AI system, including how long it took.

MP: The approach was considerably more iterative and incremental than other types of software development. It is less of a feature-by-feature, build-then-fix. In fact, I compare it somewhat to the way I see agriculture or farming, where you plant, observe the outcomes, make some adjustments, and repeat. This process itself took several months. There is some underwriting intuition involved, and the help of our underwriters was indispensable.

What other systems are involved, and how did you deal with integration issues?

MP: It involved our Symitar core and NetTeller, our online banking system. What we have now is a low-fidelity prototype. We are now working on a high-fidelity prototype to production system integrating into Alkami, our new online/mobile banking provider.

You used historical data from unsecured personal loans to train the system. How long did that take? When and how did you know the system was trained well enough to use?

MP: It took several months to cultivate our system to maximize the back-tested performance. Working on data quality issues took most of that time, and it was an iterative process involving IT and Lending.

What is good enough is a good question. When we looked at discrepancies in AI versus human outcomes, we were satisfied when the discrepancies were immaterial or fell within the limits of an underwriter’s judgement; i.e., they were not something for which a human underwriter would be reprimanded.

How tech-savvy would you need to be to replicate this in another shop your size?

MP: The general programming skill sets found in IT departments are only a part of what’s required to build AI/ML systems. Adding education and experience that is heavy in mathematics and statistics makes for a far more powerful toolbox and is the difference between getting something to work in a magical sort of way versus something that is robust, well understood, and well controlled. There are plenty of open-source libraries that implement the core routines.Having an experimental mindset helps a lot.

How does this solution compare to anything else on the market for like-sized credit unions?

MP: We’re not aware of something similar to our approach at the same state of progress, but there are other efforts out there. I think it’s important for AI technical solutions to come from within the credit union community, from both in-house development and CUSOs. Although I think the hype surrounding AI/ML is overblown, I have little doubt it will be essential to efficient and effective banking operations in this decade. It’s important to have some in-house AI/ML experience to be a competent buyer of a third-party technology.

How did you win senior management and board approval for this project?

MP: Our CEO and board maintain careful enthusiasm for next-generation technologies that will give us a competitive advantage. We clearly explained how AI/ML works in terms accessible to a broad audience, and we showed we were taking a step-by-step approach to proving the system works.

What’s the biggest takeaway you can share with other credit unions?

MP: There is one huge takeaway from this that practically all credit unions need to act on immediately: Credit unions need to maintain repositories of data now to have the option to build AI/ML applications in the future. We were able to do this because we had all of the necessary data inputs accessible and organized.

It’s far more difficult, even impossible, to reconstruct data sources needed to build machine learning systems at some later point in time. It’s practically impossible to speed up the collection of particular examples to train the system.Opportunities depend mostly on the quantity and quality of data available to learn and much, much less so on algorithms and software tools.

The post First Financial Creates In-House AI/ML Lending Experience appeared first on CreditUnions.com.

Millennials will have more spending power than any other generation by 2017, according to TIME. That buying power means credit unions must design or adopt products and services that satisfy the tastes of a generation markedly different than the one that preceded it. Many credit unions have already started offering technological and operational options that satisfy millennial expectations; that’s why this Callahan Collection highlights the strides being made in lending, investments, advisory boards, and much more. 2017 isn’t that far away.

Prepare your institution. The millennials are coming.

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• The true cost of a credit card program
• Program loss trends
• Meeting member expectations

Uncovering The True Cost Of A Credit Card Program

To accurately evaluate a card program’s total return and efficiency, it is critical to burden program financial statements with all expenses not just processing costs, rewards program management, and network dues. Credit unions must also factor in expenses related to risk, fraud, collections, disputes, third-party provider relationships, compliance, and ongoing investments in product suites and digital integration. Often, credit unions find that with all factors considered, the total cost of an insourced card program is considerably higher than initially assumed.

Evaluating Loss Trends

The credit card industry has experienced historically low losses since the Great Recession, but these loss levels may not be sustainable. Self-issuers must ask themselves if they have the ability to nimbly address changing risk dynamics in their portfolio and modify underwriting criteria to manage fluctuating performance across the risk spectrum. Credit unions with an insourced credit card program should complete a sensitivity analysis on returns to test robustness and vulnerability of the portfolio against scenarios such as increased losses, and even doubled losses.

Meeting Member Expectations

Members with the best risk profile typically want the best returns from their credit card and are most likely to get ongoing offers from national issuers. These same issuers are driving up the value of rewards programs. To compete, credit unions must not only offer a strong rewards program, but must also evaluate the program and suite of cards offered over time and adjust the rewards structure to meet the changing needs of members.

Keep in mind, not every member may value rewards. Therefore, a credit union’s card program must also offer a value proposition and marketing strategy that goes beyond rewards or risk adding expense without driving spend. Digital servicing capabilities and marketing the right product to the right members can help create a more comprehensive portfolio management strategy designed to optimize results.

When To Consider An Outsourced Solution?

There are several questions credit unions can ask about the future of their card program that will help them determine if exploring an outsourced solution makes sense, including:

• Can the credit union maintain a competitive credit card program long term?
• Can the credit union invest in digital capabilities to meet member expectations?
• Does the credit union have resources to manage program credit and fraud risk in a changing economy?

If the answers to these questions are unsatisfying, it may be time to evaluate the benefits of an outsourced partnership.

Many credit unions are hesitant to explore outsourcing because they fear losing control over the member experience, but a quality partner should understand the credit union’s culture and provide program visibility and ownership. A successful partnership allows credit unions to leverage the expertise, scale, and investments of the issuing partner and experience relief from expenses and concerns related to managing a credit card program

To learn more, download the complimentary whitepaper titled Insourcing vs. Outsourcing: Would a Credit Card Partnership Be Beneficial? from Elan Financial Services.

Partnering with Elan Financial Services

Elan Financial Services offers a holistic credit card program that focuses on the individual needs of the credit union, with serving members effectively as the ultimate goal. Partner credit unions can provide members with desired digital capabilities and rewards without upfront investment. In addition, Elan’s comprehensive fraud prevention program keeps fraud losses low per event compared to other industry players. Elan also takes on the compliance and regulatory burdens associated with running a credit card program, so credit unions can focus on members. For more information, visit www.cupartnership.com.

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MeridianLink Achieve routinely performs bi-annual model validations on generic industry scores on behalf of credit lenders, and we see that the performance of generic scores vary widely between different institutions. Lender A may see a generic score provide a KS (Kolmogorov-Smirnov test) in the low 20s while Lender B may use the same generic score and observe a KS in the high 40s. For these examples, we use a KS as the measurement of the scorecard predictive power; higher KS measurements indicate better scores for predicting and delineating riskier population segments from those that are less risky.

Generic scores are not developed to provide optimal decisions for any individual lender but to work adequately across many lenders and under many conditions. Further, generic scores are generally not built on recent economic conditions but on very old economic cycles which may not be not be aligned with how consumers are behaving in the current environment. The previously mentioned KS results in the high 40s and low 20s only serve to illustrate how a score’s effectiveness in predicting risk is heavily dependent on the population on which it is used.

Custom scorecards help lenders make more efficient decisions by providing more precision in targeting applications with a lower risk profile for that specific institution. With custom risk scores, lenders can enter riskier segments but still decline the segments of the applicant population that lead to payment delinquency with a higher likelihood of leading to a loss. Here are two scenarios where we expect the generic auto risk score to return a lower performing KS and would recommend a custom risk scorecard for decisioning:

1. A lender with delinquency trends that deviate from the norm will benefit from the use of a custom scorecard. For example, very prime consumers will have transactional characteristics that may be very similar to each other but very different from an average shopper. A custom scorecard can be designed to integrate precise consumer profiles or loan features which better describe the financial capacity of the consumer.
2. Lenders often use multiple decision matrices in addition to a generic auto risk score to drill down on less risky population segments from within the low- to mid-tier score tiers. These matrices generally blend application information with additional credit information to try to improve the accuracy of the decision process but is sub-optimal. If not used properly, usage of these additional decision matrices are an inefficient addition to the decision process and add more manual review into the process. A custom scorecard can provide a more optimal system that uses a hybrid of credit and application information and eliminates the need for onerous layers of additional decision matrices. A custom scorecard delivers better efficiency through auto-decisioning, creates more optimal credit risk assessment, and presents less opportunity for an underwriter’s bias in the overall lending process.

MeridianLink Achieve can help turn acquisition and performance data into business intelligence for banks, credit unions, and finance companies. Built with proven technology and expertise, MeridianLink Achieve’s analytic decision management infrastructure enables us to design affordable, customized solutions of our clients that are:

• Multi-dimensional to manage every category of risk financial institutions face today
• Scalable to provide the analytics needed today and to lay the foundation for the analytics of tomorrow
• Expandable across the credit cycle and lending functions
• Agile for rapid return on decision-making investments

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Our success stories speak for themselves. To hear about these success stories or more about how we can help your credit union, please click the button below to schedule a consultation.

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The change in credit reporting standards is part of the National Consumer Assistance Plan (NCAP), an initiative launched by the reporting agencies in 2015 as the result of a legal settlement to improve credit report accuracy and make it easier to correct errors.

Opinions vary, but many credit unions are working under the assumption there will be a 10-point increase on average in consumer credit scores because of the CRAs meeting the new accuracy data standards.

However, LexisNexis Risk Solutions, which sells a report that will continue to list the affected judgements and liens, cites FICO data in saying the scores of 11 million consumers will increase up to 20 points and the scores of 700,000 more will increase at least 40 points.

According to LexisNexis, approximately 11% of U.S. consumers have a tax lien or civil judgment on file. The three CRAs will eliminate roughly 50% of tax lien data and 96% of civil judgments from their data reports.

This, Lexis-Nexis argues, creates a blind spot for risk underwriters that will impact lenders and disrupt the industry, especially in mortgage lending.

Without this information, it will be difficult for credit unions to properly assess members and prospective members, a LexisNexis spokesman says.

Others aren’t so sure.

Based on what we typically see when underwriting, we see this as a positive move.

According to a FICO research brief, there will be a significant change in the collection of civil judgment and tax lien public records, but the reporting agency’s analysis indicates no observed material impact to the FICO score due to expected NCAP changes.

And Joel Pruis, a senior director at Cornerstone Advisors who joined the consultancy after a dozen years with Experian, says the credit bureaus still will bear direct responsibility for the validation of their models and will have to continue providing documentation and other assurances to customers who seek it.

The Credit Union Take

Experian customers include consumer lending vice president Marcus Wertz at Greater Nevada Credit Union ($716.0, Carson City, NV). He says his conversations with the CRA leads him to believe the impact won’t be as sweeping as it sounds because not all liens and judgments will fall off.

To determine our true exposure, we might need to conduct thorough analysis to compare the before and after changes, Wertz says. This could have an impact on risk and therefore require continuous monitoring of scores and risk validation.

Adds Brandy Bruyere, NAFCU’s vice president of regulatory compliance: Credit unions are still assessing their options and whether they will adjust risk-based pricing as a result. Ultimately, it’s a credit union business decision.

If this information will not be consistently present, it could plausibly result in higher delinquencies and higher losses to our member-owned cooperatives.

We’ll remind our underwriters to look for other common sense clues or items that are still present in the credit report that might indicate a lack of willingness to pay past obligations, says Scott Arkills, president and CEO of Silver State Schools Credit Union ($731.0M, Las Vegas, NV).

Chris Kirkley, lending manager at Peninsula Credit Union ($175.5M, Shelton, WA), says he’ll be using analytics to review average credit scores over the past three months before the change as a baseline to then view credit score averages after the change.

We’ll continue to monitor, as we do now with our interest rates, for anomalies and make adjustments as needed, he adds.

Peninsula Credit Union uses Visible Equity software to analyze its loan portfolio. Find your next solution in Callahan’s online Buyer’s Guide.

No Hurdle Here

A consultant whose client list includes numerous Low-Income Credit Unions says he’s heard no concerns about the impact on his clients’ risk models.

Many credit union lenders I work with say they’re pleased with the changes since it will help them qualify more loans, says Scott Butterfield of Your Credit Union Partner.

Debbie Taverna, vice president of consumer lending at Digital Federal Credit Union ($8.0B, Marlborough, MA), says she’s not concerned about the change, either.

Based on what we typically see when underwriting, we see this as a positive move, she says, noting as an example tax liens that applicants have paid but still appear on the credit report.

Learn about lending at DCU in How To Make Lending Easy For Members

Taverna also says the effect at DCU would be muted by the fact that 50% of her loans are A and B paper.

The change also plays into the philosophy of relationship lenders, who give more weight to other factors besides credit reports and FICO scores in deciding credit worthiness.

Ankush Tewari, senior director of market planning at LexisNexis Risk Solutions, says his company is working with the Filene Research Institute to provide risk information that can enable that approach.

LexisNexis offers advice on how to lend to underbanked consumers in this whitepaper.

All About Relationships

One veteran lender notes that credit unions often move to the front of the line for troubled borrowers notably because of the cooperative philosophy of relationship lending.

Although not a factor that makes a bad credit risk good, it’s worth noting that many borrowers will repay their credit union even when they’re having difficulty repaying other creditors, including tax authorities, says John Cook, vice president of lending at Redstone Federal Credit Union ($4.9B, Huntsville, AL).

Cook also says the potential borrowers most impacted by the deletion of some judgments and liens from their credit reports will primarily already have credit scores of 600 or less, as people having problems paying property taxes or civil judgments are probably struggling to pay their other bills, too.

But the presence of liens and judgements in some instances but not others could create problems for credit unions, which often go the extra mile to understand the cause of reported credit issues, says Arkills at Silver State Schools.

If this information will not be consistently present, it could plausibly result in higher delinquencies and higher losses to our member-owned cooperatives, the CEO says.

The post Negative Histories Need Not Apply appeared first on CreditUnions.com.

Top-Level Takeaways

• The data and lending teams at Oregon Community Credit Union combined forces to create a new way to rate risk and price loans.

• Auto loan production immediately jumped after introducing new rates, and loan production has been strong since.

Over the past few years, senior managers at Oregon Community Credit Union ($1.7B, Eugene, OR) realized it was time to challenge some long-standing assumptions about auto lending.

The credit union was feeling pressure on its margins and capturing less indirect auto loan market share in its competitive market.

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Something had to change, and lending vice president Ethan Nelson knew where to start.

We hadn’t fundamentally changed the structure of our auto rate sheet in years, so we knew it was time to test some of our assumptions,says the 19-year veteran at OCCU who’s been the lending VP for the past four.

Testing Old Assumptions

To test those assumptions, the credit union undertook a multi-department project that combined the art of relationship lending with the science of data analytics to create a new rate sheet and a new understanding of risk and reward.

When it comes to risk, it turns out one of OCCU’s longstanding assumptions holds water. The project found that FICO scores are a good predictor of risk. One assumption that didn’t pan out is that loan length also is a good predictor.

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Not by itself it isn’t,says Casey Foltz, OCCU’s business intelligence manager. A 72-month loan is not riskier than a 60-month loan just because of the loan length.

Thanks to the project, OCCU can also now quantify risk according to member loyalty, giving the credit union the ability to reward loyal members with better rates.

The Results From New Rates

The project began in late 2016, and OCCU put new rates into place in May 2017. The effects were swift. The credit union posted record months in auto loan volumes and high credit scores that June and July.

Nelson attributes some of that to market conditions but says the production surge was beyond expectations.

It was high-quality paper at what we think are fair rates,he says.

Adds Foltz, We ended up with the opportunity to reprice loans according to risk.

Updating an old rate sheet with new data techniques helped OCCU post year-end loan growth of 22.4%, sharply higher than the 11.6% average for credit unions $1 billion to $10 billion in assets and the 11.6% average for Oregon credit unions. Sharp auto loan growth, particularly indirect, has driven OCCU’s overall lending to new heights.

Auto lending at OCC surged after the credit union restructured its rate sheet in May 2017.

More Than Numbers

The lending and business intelligence departments at OCCU joined forces to take on the analytics project. Foltz’s team put together 20 different models that used algorithms and machine learning techniques.

Collaboration + Process

Casey Foltz and Ethan Nelson of Oregon Community Credit Union offer advice on how to dive deep into a data analytics project.

1. Strive for collaboration. It wasn’t data science people on one side of the wall throwing a ball over the top to the other side,says Casey Foltz, OCCU’s business intelligence manager. It was all of us getting together.
2. Meet regularly. The project team at OCCU met bi-weekly for the better part of a year. It was very iterative,Foltz says. There were a lot of decision points, and we always erred on the side of being conservative to mitigate the risk. And, we followed the advice of the lending staff.
3. Don’t rush. Allow time to the let the process flow where it needs to flow, advises Ethan Nelson, vice president of lending.
4. Be open-minded. Think through your assumptions, but don’t be afraid when the data changes those assumptions, Nelson says.

They used open statistics software called R, along with Microsoft business intelligence programs and Tableau reporting tools. The lending team contributed supporting documents, testing, and validation, as well as deep institutional knowledge about members and the market.

Now, OCCU pricing incorporates such factors as overhead cost of paying the dealer reserves, probability of default, how much interest the note is likely to generate, and member loyalty.

It all goes into one simple number where we can see where we’re breaking even and where we have room to adjust,Foltz says. We use that science to support lower rates and properly price those with higher risk, so pricing as a whole is more in line with the real risks.

But OCCU’s approach to lending isn’t solely about hard numbers.

We balance that science with what our gut tells us about pricing loans, what our competitors are doing, how our dealers will react, and what we know about each member we deal with,adds chief lender Nelson.

OCCU’s auto loan pricing project was a winner in the Credit Union Analytics Challenge at last year’s CULytics Summit in Redmond, WA. This year’s summit is March 13-15.

In addition to gut checks, the use of machine learning techniques enhances OCCU’s ability to refine its rate sheet and lending criteria as time and data accrues.

We now model our pricing structures to better understand the results from changes we made and predict what future adjustments we’ll need down the road,Nelson says.

The credit union is no longer working harder for a shrinking number of loans, and it’s no longer subsidizing risker loans at the expense of more credit-worthy members.

We can write more loans to more people of higher risk,Foltz says. That is a real community service.

And the membership as a whole even those without a loan is getting something out of OCCU’s enhanced analytics.

Now, we can put more money toward investing in branches, digital channels, and other value propositions,Foltz says. We view that as returning real value to members.

Some of the modeling underway at OCCU also strongly suggests that a long-term relationship with OCCU specifically and credit unions generally tend to dramatically improve a member’s overall credit.

There are exceptions, but overall that’s pretty clearly happening,Foltz says. If we can price a loan in a way that’s fair to all our members, that higher-risk member will be in better financial shape two or three years down the line.

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