AI-Powered Lending Experiences for Every Member

Most credit unions don’t have a technology problem. They have an experience problem.

Loan origination systems, online banking, mobile apps – they’re all in place. Yet members still hit friction: slow decisions, repetitive questions, clunky handoffs between channels, and inconsistent communication. Meanwhile, big banks and fintechs are training consumers to expect instant, personalized service.

This matters because member-centric banking is quickly becoming AI-centric banking. And as Jack Imes from Allied Solutions likes to say, credit unions are in a perfect spot to help people grow and stay relevant – if they evolve the experience.

This article builds on themes from Jack’s conversation on The CUInsight Network and connects them to a very practical question: how can credit unions use AI to evolve lending and member experiences across generations, without losing the human touch that defines the movement?

We’ll look at concrete ways AI is reshaping credit union lending, how to think about your tech stack, and what a realistic roadmap looks like for 2025 planning.

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AI is now the backbone of member-centric lending

AI in credit unions isn’t just about chatbots and fancy dashboards. The real value is in turning scattered data and fragmented processes into a coherent, member-friendly experience.

Here’s what that means in practice:

• Members get faster, fairer lending decisions
• Staff get better insights instead of more reports
• The board sees growth without eroding risk discipline

When Jack talks about technology as the “key to a better member experience,” this is what he’s pointing to. Credit unions that win in the next 3–5 years will treat AI as infrastructure for:

• Credit decisioning and pricing
• Fraud and risk monitoring
• Member service automation
• Personalized financial wellness guidance

The reality? It’s simpler than you think, if you tackle it in the right order.

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From manual to intelligent: AI in lending workflows

If you only apply AI in one area over the next 12–18 months, make it lending.

Lending is where member expectations are highest and where small experience improvements translate directly into revenue. I’ve seen credit unions improve loan pull-through rates by double digits just by reducing friction in a few key steps.

1. Smarter loan decisioning

AI-driven underwriting models can evaluate risk more precisely and consistently than legacy scorecards alone. They’re not replacing a credit union’s lending philosophy; they’re sharpening it.

Practical use cases:

• Augmented underwriting: Use machine learning models as a second opinion to support or challenge automated approvals/declines.
• Thin-file and nontraditional borrowers: Bring in alternative data (cashflow patterns, payment behavior, historical relationship data) to responsibly serve members that traditional credit scores penalize.
• Risk-based pricing insights: Identify where your current rate tiers are leaving approved, good-risk loans on the table – especially in auto and HELOC portfolios.

For community-focused lenders, this is where AI aligns with the mission. It helps distinguish between “no” and “not yet,” so more members can move toward approval with clear steps.

2. AI-optimized lending operations

Speed isn’t just a tech issue; it’s an operations issue. AI helps by automating the “gray space” work that slows everything down:

• Document classification and data extraction from paystubs, bank statements, tax forms
• Income and employment verification routing
• Detecting incomplete applications and triggering targeted follow-ups

If your team is still manually keying data from PDFs into your LOS, that’s a perfect place to start. AI document tools can reduce processing time by 30–60% while improving accuracy. Members feel that as faster decisions; staff feel it as less drudgery.

3. Proactive cross-sell that feels helpful, not pushy

Jack talks about customized, diversified product portfolios. AI is how you actually operationalize that vision.

Instead of generic “you’re preapproved for a credit card” blasts, AI models can:

• Identify members who would save money by refinancing an auto loan
• Spot mortgage-ready renters based on payment history and savings behavior
• Flag members likely to need small business credit or equipment loans

Then your credit union can:

• Push timely, contextual offers in digital channels
• Equip MSRs and lenders with member-specific talking points in branch or over the phone

The difference is dramatic: relevant, 1:1 offers feel like guidance, not sales.

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Supporting every generation with AI-driven experiences

Jack makes a crucial point: technology has to work for every generation of member – not just digital natives.

The challenge for most credit unions is designing experiences that are:

• Digital-first for those who want it
• Human-first for those who need it
• Consistent for everyone

AI helps bridge these expectations instead of forcing tradeoffs.

Digital members: speed and control

For younger or more digitally engaged members, the bar is clear: fast, self-service, mobile. AI can power:

• 24/7 AI member service: Intelligent virtual assistants that answer questions about loans, rates, status updates, and next steps without sending members through maze-like menus.
• Real-time pre-qualification: Quick “what if” scenarios for auto, personal, or HELOC loans based on soft pulls and relationship data.
• Smart notifications: Status updates, e-sign prompts, and reminders timed for when members are most likely to respond.

When these experiences are built well, the member barely notices the AI – they just notice that it “works.”

Traditional members: consistency and confidence

Older generations or less tech-comfortable members aren’t asking for AI. They’re asking for clarity and trust.

AI supports that by:

• Providing staff with 360° member views: Relationship depth, product mix, financial stress signals, and past interaction notes in one place.
• Suggesting next best actions: “This member was declined last year for an auto loan but has improved cashflow and credit score since then – consider outreach.”
• Standardizing explanations: Decisioning tools that generate plain-language reasoning for approvals, counteroffers, or declines.

Members still get the familiar branch or phone experience, but everything feels smoother and more informed.

The shared expectation: personalization

Across generations, one expectation is universal: “Know me, don’t treat me like an account number.” AI is the only realistic way to personalize at scale without ballooning headcount.

This is where the series theme of AI for credit unions: member-centric banking really comes together. Member-centric isn’t a slogan; it’s the ability to:

• See a member’s financial story in real time
• Anticipate needs instead of reacting
• Deliver guidance that respects their goals and constraints

AI systems can do that pattern recognition in the background so your people can bring empathy and judgment to the moments that matter.

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Building an AI-ready tech stack without starting from scratch

Jack’s role at Allied Solutions centers on one big idea: your tech stack should feel seamless to the member, even if it’s made of multiple vendors behind the scenes.

You don’t need a single mega-platform to use AI effectively. You need a stack that behaves like one.

Core principles of an AI-friendly stack

When I look at credit unions that are actually getting value from AI, their environments tend to share a few traits:

1. APIs and data access
   Systems can exchange data in near real time. Your core, LOS, CRM, and digital banking aren’t Great Walls.

2. Centralized data model
   There’s a clear “source of truth” for member data, even if it’s fed by multiple systems.

3. Modular AI components
   AI tools are plugged in where they matter most – underwriting, fraud, member service – without requiring a forklift upgrade.

4. Clear governance
   Data usage, model oversight, and compliance expectations are defined, not assumed.

This is exactly where partners like Allied Solutions fit: they sit between your strategy and your stack, curating products and integrations so you don’t have to build everything yourself.

Avoiding the ‘shiny object’ trap

The biggest mistake I see? Buying AI tools that don’t line up with a business problem.

A better approach:

1. Rank your pain points: Slow lending decisions, high manual effort, fraud losses, contact center volume, etc.
2. Match problems to outcomes: “Reduce indirect auto decision time by 50%” is far more actionable than “implement AI underwriting.”
3. Look for integrated solutions: Tools that already connect to your core, LOS, or online banking provider.
4. Pilot, don’t boil the ocean: Start with a well-bounded use case, measure impact, then expand.

Allied’s model of customizing portfolios for each credit union exists for a reason: a 20,000-member shop in rural America simply doesn’t need the same AI footprint as a 300,000-member metro institution.

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Practical roadmap: where to start in the next 12 months

If you’re planning your 2025–2026 roadmap, here’s a pragmatic way to enter or expand AI in your credit union.

Step 1: Clean up the data layer

None of this works if your data is scattered, stale, or unusable. Focus on:

• Consolidating key member and loan data fields across systems
• Establishing data quality checks (duplicates, missing values, outdated contact info)
• Creating at least a basic “member profile” accessible to lending and member service teams

You don’t need a full-blown data warehouse to start. You do need a consistent way to answer: “What’s the real picture of this member?”

Step 2: Pick one high-impact AI use case

For most credit unions, the best entry points are:

• AI-assisted underwriting for consumer loans
• Intelligent fraud detection on cards and accounts
• AI member service assistant to deflect routine calls and chats

Choose one, define a clear success metric (e.g., “reduce average decision time from 24 hours to 2 hours”), then identify partners who’ve done this in similar-sized credit unions.

Step 3: Train your people alongside your models

The most overlooked part of AI implementation is staff enablement.

• Explain what the AI does – and doesn’t do
• Clarify how human judgment overrides or interacts with AI outputs
• Show staff the “before and after” workflow so they see the benefit, not just the change

When people understand that AI is removing drudgery, not replacing their value, adoption goes up fast.

Step 4: Build a feedback loop

Your first AI solution won’t be perfect. That’s fine. What matters is:

• Tracking performance monthly
• Gathering feedback from staff and members
• Adjusting rules, thresholds, and workflows

Think of AI in your credit union like coaching a talented new hire. The more feedback and clear expectations you give it, the better it performs.

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Where member-centric AI is heading next

Jack Imes’s perspective is grounded in 35+ years with credit unions: they’re uniquely positioned to combine AI-driven efficiency with human-centered service. Big banks can match the technology; they can’t easily match the trust.

Over the next few years, the credit unions that stand out will be those that:

• Use AI to speed up and simplify lending, especially for everyday members, not just prime borrowers
• Turn fraud and risk tools into visible protection and reassurance, not just back-office controls
• Treat AI as a way to free staff to build deeper relationships, not as a replacement for those relationships

If you’re responsible for lending, technology, or member experience, the next step is straightforward: pick one area – lending decisions, fraud detection, or member service – and design a pilot that directly improves the member journey.

There’s a better way to approach AI in credit unions than big, risky transformation projects. Start small, stay member-centric, and use partners who understand both technology and cooperative values. That’s how you evolve experiences without losing what makes your credit union different in the first place.