From “Every Person Is a Risk” to “Every Person Gets Credit”

“When you look at the underlying data, you are able to tell a better economic story of members.” – Pankaj Jain, President & Co‑Founder, Scienaptic AI

Most credit unions are leaving approvals, relationships, and non-interest income on the table because their decisioning models still treat members like credit scores, not people.

Here’s the thing about AI for credit unions: it’s not just a faster credit engine. Used well, it’s a storytelling engine. It tells the economic story of each member in far more detail than legacy underwriting ever could.

In this post from our AI for Credit Unions: Member-Centric Banking series, we’ll use insights from Pankaj Jain’s conversation on The CUInsight Network to unpack how AI-powered loan decisioning and alternative credit scoring can help your team move from a defensive mindset of “every person is a risk” to a proactive mindset of “every person gets credit.”

Why AI-Driven Loan Decisioning Matters Right Now

AI-driven loan decisioning matters because traditional credit models are misaligned with how members live, work, and borrow in 2025.

Most FICO-centric approaches were built for a world of long W‑2 histories, prime borrowers, and predictable careers. Your members now:

• Change jobs more frequently
• Use gig and freelance income
• Carry multiple types of digital subscriptions and wallets
• Have thin or no traditional credit files (especially Gen Z and recent immigrants)

If your underwriting only “sees” bureau scores and a handful of ratios, a huge portion of your membership shows up as incomplete. Incomplete data looks risky, so the safe move is decline or manual review. That means:

• Fewer approved loans
• Slower cycle times
• Higher manual underwriting costs
• Frustrated members who expect instant answers on their phones

Pankaj’s core argument is blunt: when you use AI to actually read the data you already have, the risk picture changes. Many members you see as marginal today are actually good, profitable borrowers once you understand their full story.

From Credit Score to Economic Story

AI for credit unions is most valuable when it turns fragmented data into coherent member stories.

What “economic story” really means

An economic story is a narrative answer to a few basic questions:

• How does this member earn, spend, and save over time?
• How stable is their income (even if it’s non‑traditional)?
• How do they behave with existing obligations—on time, late, improving, deteriorating?
• How do they respond under stress (job change, medical issues, economic shocks)?

Traditional underwriting answers some of these questions with blunt instruments: FICO range, DTI, LTV. AI opens up dozens more signals, such as:

• Account cashflow patterns: consistency of inflows, volatility of outflows, end-of-month balances
• Behavioral data: digital banking login frequency, payment patterns, use of autopay
• Alternative data: utilities, telecom payments, rental history, even subscription stability
• Credit union relationship data: tenure, product mix, savings habits, previous loan performance

The magic isn’t that AI “knows more.” It’s that AI can weigh these signals in milliseconds across thousands of members and highlight patterns your human underwriters would never have time to see.

Why this reduces risk instead of increasing it

A common fear is that approving more loans with AI means “loosening standards.” Done correctly, it’s the opposite.

AI underwriting allows you to:

• Approve more good risk that old models misclassify
• Price risk more precisely (rate and terms that match actual probability of loss)
• Identify early warning signs sooner than manual review can

Most credit unions that adopt AI decisioning platforms see two things at once:

1. Higher approval rates on the same (or better) loss levels
2. Faster decisioning without throwing more staff at underwriting queues

That’s exactly what “every person gets credit” means in practice: more appropriate credit, not blind approvals.

Alternative Credit Scoring: Expanding Access, Protecting the P&L

Alternative credit scoring is the practical bridge between your mission and your margin.

What alternative scoring adds to your toolkit

Alternative credit scoring models look beyond bureau data to understand members with thin or no traditional files. For credit unions, this is a direct path to:

• Serving Gen Z members early, before big banks do
• Supporting immigrant communities who are strong savers but invisible to bureaus
• Re‑engaging members who had past setbacks but now show stable behavior

AI models can incorporate:

• Deposit and spending history instead of just tradelines
• Micro‑trends (e.g., improving payment regularity over 6 months)
• Contextual data (e.g., one‑time delinquency during a known life event, then recovery)

When Pankaj talks about changing the narrative from risk to access, this is the engine behind that shift.

The ROI angle credit union leaders care about

This isn’t just about inclusion; it’s about math.

When AI decisioning improves your credit story for each member, three ROI levers tend to move:

1. Approval rate: You say “yes” more often without relaxing your loss targets.
2. Yield: You can tier pricing more granularly, matching rate to actual risk.
3. Cost to decision: Instant or near‑instant approvals shrink manual review time.

That combination often looks like:

• 10–30% more approvals in specific portfolios
• Stable or improved delinquency/charge‑off metrics
• Underwriting teams able to focus on exceptions and commercial/complex deals

If you’re a CEO or Chief Lending Officer, that’s not a tech story. That’s a balance sheet story.

Re‑Thinking the Digital Member Experience: Instant, Personal, Friction-Light

For Gen Z and younger millennials, “I’ll get back to you in 24–48 hours” is a decline in disguise.

Instant decisions as a member expectation

Scienaptic and similar AI platforms enable instant loan decisioning that feels natural inside your digital ecosystem:

• Member starts an application in mobile banking
• AI model evaluates hundreds of variables in seconds
• Decision (approve, counteroffer, refer) appears immediately

This matters because the member experience and credit quality are now linked:

• Fast, transparent answers increase acceptance and satisfaction
• Better data and models make those fast answers safer

When you deliver that experience, especially for Gen Z, you’re competing with fintechs on equal footing instead of asking members to tolerate older processes out of loyalty.

Personalization without creepiness

A member‑centric AI strategy doesn’t just approve or decline; it recommends.

Based on a member’s economic story, AI can:

• Offer a lower‑cost consolidation loan to a member with multiple high‑rate cards
• Suggest a credit builder or secured product to a thin‑file young member
• Trigger outreach when it detects rising financial stress signals

The line you don’t want to cross is “creepy personalization.” The remedy is straightforward:

• Be explicit in disclosures about how data is used
• Focus on clear member benefit in offers
• Provide easy opt‑outs for targeted recommendations

Done right, members feel “understood,” not “watched.”

Practical Steps to Bring AI Underwriting into Your Credit Union

Adopting AI decisioning doesn’t have to be a big‑bang core transformation. The reality? A phased, controlled rollout works better.

1. Start with a focused use case

Pick one high‑volume, relatively simple portfolio, for example:

• Indirect auto
• Direct auto
• Unsecured personal loans
• Credit cards

Define a clear business goal such as:

• “Increase approvals on members with 580–680 scores without increasing losses”
• “Reduce decision time from 4 hours to under 1 minute for 80% of applications”

2. Use shadow mode before going live

Most AI credit platforms support shadow testing:

• Run the AI model alongside your existing policy
• Compare what it would’ve approved/declined vs your current decisions
• Track predicted performance vs actuals over a few months

This builds confidence with the board, regulators, and staff before production use.

3. Build explainability and fairness into the process

Regulators and members will want to know “why” a decision was made.

Insist on:

• Clear reason codes that align with fair lending requirements
• Documentation that shows which variables are used (and which are excluded)
• Regular model audits for disparate impact and bias

An AI model that can’t explain itself is a liability, not an asset, in credit unions’ trust-based environment.

4. Train your people, not just your model

The most successful credit unions treat AI as a co‑pilot for underwriters and frontline staff.

Practical steps:

• Train lending staff on how model outputs are generated
• Give underwriters tools to override or escalate with reason
• Share success stories internally: approvals you would’ve missed, members won, losses avoided

This shifts culture from “AI is here to replace us” to “AI helps us serve members better and faster.”

Staying Ahead: Using AI to Thrive, Not Just Survive

The financial services industry isn’t waiting for credit unions to catch up. Fintechs and big banks are already using AI to personalize offers, speed decisions, and capture the next generation of borrowers.

The credit unions that will thrive over the next decade are the ones that:

• Use AI to tell better economic stories about their members
• Treat alternative credit data as a member service, not an exotic feature
• Align digital experiences with what Gen Z already expects from other apps
• Build governance, fairness, and transparency into every AI project

Your mission has always been member-centric banking. AI is just the next set of tools to live that mission at scale.

So the real question isn’t whether you’ll use AI in loan decisioning. It’s whether you’ll use it soon enough to turn “every person is a risk” into “every person gets credit” in your field of membership.

If you’re planning your AI roadmap for 2026 budget cycles, loan decisioning and alternative credit scoring deserve a top spot on that list.