Relational underwriting plus AI lets credit unions say “yes” more often, fight predatory lending, and make financial inclusion a core growth strategy.
Most credit union leaders know this number, but it still stings: over 40% of Americans can’t handle a $400 emergency without borrowing or selling something. That stat isn’t just a headline; it’s a report card on how well our financial system serves real people.
Here’s the thing about credit unions: they were built to serve the people banks overlook. Yet many credit unions still rely on tools and processes designed for a very different kind of member and a very different era. FICO-only underwriting. One-size-fits-all loan products. Legacy digital experiences that feel nothing like the apps members use every day.
This post in the “AI for Credit Unions: Member-Centric Banking” series looks at how leaders like Seth Brickman, CEO of QCash Financial CUSO, are pushing the movement back toward its roots: local, relational, inclusive finance—but with AI and modern technology doing the heavy lifting.
We’ll break down how relational underwriting, AI-driven decisioning, and a sharper focus on financial inclusion can help your credit union:
- Serve more members (especially underserved and vulnerable groups)
- Reduce reliance on predatory lenders in your community
- Grow loan volume without blowing up risk
- Stay relevant in a crowded, digital-first financial services market
From “Part of the Community” to Data-Driven Ally
Credit unions don’t just operate in communities; they’re woven into them. That’s the mindset behind Seth Brickman’s work at QCash Financial CUSO: use technology to express that community bond at scale, not replace it.
Most institutions still treat underwriting as a narrow math problem: Does the credit score hit the threshold or not? But life doesn’t work in three-digit numbers. Members go through job transitions, medical emergencies, divorces, and caregiving responsibilities. When everything reduces to FICO, you miss context—and you miss people.
Relational underwriting starts from a different premise: your members are more than their bureau file. You already know a ton about them:
- Tenure with your credit union
- Deposit behavior and cash flow stability
- Direct deposit patterns and employer type
- Existing product usage and repayment history
- Engagement signals (digital logins, call center conversations, branch visits)
When you plug these into an AI-driven decisioning engine, you can:
- Approve small-dollar or emergency loans for members who’d be auto-declined by score-only rules
- Price risk more accurately instead of just “high score = low rate, low score = high rate”
- Respond to member needs in minutes, not days
The reality? Most credit unions already have the data they need to be more inclusive. They just aren’t using it intelligently yet.
Why Relational Underwriting Beats Score-Only Lending
Relational underwriting is the practice of using a member’s full relationship and behavior—not just their credit score—to make lending decisions. When you combine that mindset with AI, three things happen.
1. You reduce harm from predatory lending
Seth points out an uncomfortable truth: minorities and women are disproportionately targeted by predatory lenders—payday shops, high-fee online installment lenders, rent-to-own, you name it. Those products often:
- Charge triple-digit APRs
- Trap borrowers in rollover cycles
- Strip wealth out of low- and moderate-income communities
When your credit union offers instant, small-dollar, relationship-based loans, you give members a safe alternative at the exact moment they’re most vulnerable.
Practical example:
A long-time member with a 620 FICO, stable deposits, and a history of paying you on time faces a car repair. Score-only underwriting says “no.” Relational underwriting says:
This member has been with us 7 years, never overdrafts, gets steady payroll deposits, and repays our products. We’ll approve a $700 emergency loan in 60 seconds at a fair rate.
That one “yes” can:
- Stop the member from walking into a payday lender
- Deepen loyalty and product penetration
- Build positive repayment data for future decisions
2. You align with your mission and your growth goals
A lot of leaders talk about DEI and financial inclusion as if they’re separate from growth. They’re not. The underserved are your growth opportunity.
Relational underwriting powered by AI helps you:
- Say “yes” more often without ignoring risk
- Reach thin-file, no-file, and credit-rebuilding members
- Grow loan balances in a disciplined, data-informed way
Member-centric AI doesn’t replace judgment; it augments it with patterns you can’t see manually. For example:
- AI models can flag “quietly stable” members—those with modest scores but rock-solid cash flow
- You can target pre-approved emergency credit to them before they ever ask
3. You move faster than fintechs without losing your soul
Fintechs win on speed and UX. Credit unions win on trust and purpose. The sweet spot is obvious: use AI to match fintech speed while staying rooted in cooperative values.
Relational underwriting is how you operationalize that.
- Applications take minutes, not hours
- Approvals feel personalized, not arbitrary
- Members see you as a safety net, not a gatekeeper
Using AI to Listen Better, Not Just Score Faster
Seth’s core advice to leaders is simple: listen to your members. AI can actually make that easier and more consistent.
AI as a “member listening engine”
AI for credit unions isn’t just about loan algorithms. It’s about pattern recognition across every interaction:
- Member service automation: Chatbots and virtual assistants can resolve routine questions, then log patterns: What are people asking for? Where do they get stuck?
- Contact center analytics: AI can transcribe calls and spot recurring friction points—fees, declined transactions, confusing disclosures.
- Digital behavior analysis: Drop-off points in your online or mobile application flows highlight where members lose patience or get confused.
When you connect these dots, you start to see what your members are really asking you to build.
Turning signals into inclusive products
Here’s where most organizations fall down: they collect data, nod thoughtfully, then keep doing what they’ve always done.
Instead, use those AI insights to:
- Fine-tune small-dollar loan terms (amounts, terms, repayment schedules) around real cash-flow patterns
- Identify segments at higher risk of emergency borrowing, and proactively offer them safer options
- Design financial wellness nudges—like alerts that say, “You’re trending toward a negative balance in 3 days; here are your options.”
This is where AI and member-centric banking intersect: members feel like their credit union sees them, anticipates their needs, and gives them choices that respect their dignity.
Saying “Yes” More Often—Without Losing Control of Risk
One of Seth’s main themes is finding more opportunities to say “yes.” That doesn’t mean throwing risk management out the window. It means getting smarter.
Here’s how AI and relational underwriting help you say yes responsibly.
Smarter underwriting rules
Use AI-driven models to:
- Identify which attributes truly predict default for your membership (they’re often different from bureau assumptions)
- Weight relationship data more heavily for small-dollar, short-term credit
- Separate temporary cash-flow issues from chronic risk patterns
With that, you can design tiered products:
- Instant emergency loans for stable, relationship-strong members
- Slightly more structured offers for those with mixed patterns
- Clear “path to yes” programs (e.g., a secured card followed by a micro-loan offer after on-time payments)
Continuous model monitoring
Responsible AI means watching your models like a hawk:
- Check approval/decline patterns across gender, race, and neighborhood to avoid unintended bias
- Stress-test models during economic shifts (like we’re seeing with inflation and rate volatility heading into 2026)
- Maintain human override paths when the data doesn’t tell the full story
Done right, AI becomes a guardrail, not a black box. Your lending team still owns the strategy; the model just surfaces better opportunities and earlier warnings.
Practical First Steps for Credit Union Leaders
If you’re thinking, “This sounds right, but where do we start?” here’s a practical roadmap I’ve seen work.
1. Start small with emergency and small-dollar loans
These products are the perfect sandbox:
- Directly address that “$400 emergency” problem
- Have short durations, so you learn fast
- Are mission-aligned and highly visible in your community
Pilot an AI-assisted, relational underwriting model for:
- $200–$2,000 emergency loans
- Short terms (3–24 months)
- Instant or near-instant decisioning
Measure:
- Approval rates vs. traditional underwriting
- Default and loss rates
- Member satisfaction and repeat usage
2. Clean and connect your data
AI is only as good as the data it sees. Focus on:
- Consolidating member data across core, online banking, loan origination, and contact center platforms
- Standardizing key fields (income, tenure, product usage, repayment history)
- Documenting where data is incomplete or low-quality
You don’t need perfection to start. You do need enough reliable signals to train a first-generation model.
3. Build a cross-functional AI + inclusion team
Don’t leave this solely to IT or to lending. Form a small working group with:
- Lending leadership
- IT/data/analytics
- Compliance and risk
- DEI/financial inclusion champion
- Member experience/marketing
Charge them with one mandate: design AI-powered credit products that expand inclusion while protecting the balance sheet. Meet often, iterate fast, and keep the board informed.
Your Next Chapter in Member-Centric Banking
Financial inclusion isn’t a side project for credit unions; it’s the whole point. If 40% of Americans can’t weather a $400 emergency, that’s not just a macroeconomic stat—that’s a daily reality for thousands of your members.
Relational underwriting, supported by AI, gives you a way to act on your values at scale:
- Offer safer alternatives to predatory lenders
- Say “yes” to more members based on who they are with you, not just a score
- Strengthen loyalty by showing up at the exact moment they need help
As this “AI for Credit Unions: Member-Centric Banking” series continues, the through-line stays the same: AI should make you more human, not less. It should free your teams to focus on empathy, judgment, and relationships—while the algorithms handle the repetitive, pattern-heavy work.
If your credit union wants to stay relevant in a saturated market, this is the path: serve the underserved, listen harder, and use AI to turn your mission into daily practice. The tech is ready. The question is whether your strategy is.