The Scariest Member Is the One Who Looks Fine 👻
Why financial vulnerability can hide in plain sight—and what credit union data can reveal before a member reaches the breaking point.
Maria looks like a pretty good member.
Her paycheck arrives by direct deposit.
Her loan payments are current.
She uses digital banking.
She has multiple products with the credit union.
Her credit score is respectable.
She's never called the contact center asking for financial help.
Nothing about Maria is setting off an alarm.
No delinquency.
No collections.
No dramatic drop in deposits.
No obvious financial emergency.
If Maria appeared on a traditional credit union dashboard, we'd probably put her in the “doing fine” category.
There's just one problem.
Maria might not be fine at all.
And that may be one of the scariest things hiding in our member data.
Financial Stress Doesn't Always Look Like Financial Distress
We are pretty good at recognizing members after something goes wrong.
A payment becomes 30 days delinquent.
An account starts overdrawing.
A credit score drops.
A loan moves toward collections.
Savings disappear.
Those are important signals.
But they're also often lagging indicators.
By the time they appear, the member may have been struggling for months.
Financial vulnerability can look very different before the breaking point.
It can look like someone who is still paying every bill.
Still showing up for work.
Still making the mortgage payment.
Still paying the auto loan.
Still buying groceries.
Still keeping everything together.
Barely.
That's the member I think we need to get better at seeing.
Let's Look at Maria Again
Now let's move beyond Maria's products and look at her financial life.
Her paycheck has increased.
Good news.
But so have her groceries.
Her housing payment consumes a substantial portion of her income.
Her auto payment goes to another lender every month.
Several credit card payments follow shortly after payday.
A few BNPL payments have begun appearing.
And her savings balance isn't growing.
Nothing here, individually, proves Maria is financially vulnerable.
But put those signals together and we begin to see something different.
Maria isn't necessarily failing.
She's losing financial capacity.
That's an important distinction.
A Member Can Be Current and Still Be Struggling
This is where traditional measures can fool us.
We tend to associate financial health with the absence of negative events.
Current = good.
Delinquent = bad.
Positive balance = good.
Overdrawn = bad.
High credit score = good.
Low credit score = bad.
Those indicators matter.
But financial strength isn't binary.
There is a huge space between financially strong and financially distressed.
That's where many members live.
They're coping.
They're making trade-offs.
They're moving money around.
They're postponing things.
They're absorbing higher costs.
They're using credit to bridge gaps.
They're keeping everything paid.
Until they can't.
And if we're only looking for the moment when something breaks, we may miss every signal that came before it.
Maria Didn't Change. Our View of Maria Did.
This is the part I find fascinating.
Maria was the same member when we looked at her products.
She was the same member when we looked at her credit score.
She was the same member when we looked at her payment history.
What changed?
Our view of Maria.
Instead of asking only:
What products does Maria have with us?
We started asking:
What does Maria's financial life look like?
And suddenly, different questions emerge.
Is transportation affordable for her?
Is housing consuming too much of her income?
Could she absorb an unexpected expense?
How much of her paycheck is already committed to debt?
Is she building anything for retirement?
Is her credit position improving or deteriorating?
And perhaps most importantly:
Does Maria feel financially confident about her future?
Those questions tell us something product penetration never could.
Seven Signals. One Member.
This is one of the reasons we developed the CU Power Core 7.
Instead of trying to define member financial strength through a single number, we look across seven dimensions:
Transportation
Can the member afford reliable access to transportation without it overwhelming their financial capacity?
Housing
Can the member consistently maintain affordable, stable shelter?
Emergency Savings
Does the member have enough liquid savings to absorb an unexpected financial shock?
Debt Stress
How much pressure are debt obligations putting on the member's income?
Retirement
Is the member building toward longer-term financial security?
Credit Health
What do credit scores, payment behaviors and changes over time tell us about financial strength?
Financial Confidence
And beyond what the data says, how does the member actually feel about their financial situation?
No single indicator tells us whether Maria is financially strong.
But together, they give us a much more complete picture.
The Goal Isn't to Predict Maria's Failure
This distinction matters.
The goal isn't to build an algorithm that declares:
“Maria is going to become delinquent.”
That's not the point.
The opportunity is much more human.
Can we recognize where Maria's financial capacity is becoming constrained—and find a way to help?
Maybe her $725 outside auto payment can be refinanced to $600.
That's $125 returned to Maria's monthly budget.
Maybe consolidating higher-cost debt reduces another monthly obligation.
Maybe an automated savings strategy begins rebuilding her financial cushion.
Maybe a conversation reveals something the transaction data never could.
The intervention will be different for every member.
But the objective is the same:
Leave the member financially stronger than we found them.
That's a very different goal from simply selling another product.
From Reacting to Recognizing
Credit unions have always been good at helping members in moments of need.
That's part of what makes the cooperative model special.
But data gives us an opportunity to move the moment of intervention earlier.
Instead of:
Problem → Member asks for help → Credit union responds
What if we could move toward:
Recognize → Act → Measure
Recognize a meaningful financial signal.
Act in a way that improves the member's financial position.
Then measure whether it actually worked.
Did the monthly obligation decline?
Did savings increase?
Did debt pressure decrease?
Did credit health improve?
Did the member become more financially confident?
That's where data stops being something we use simply to understand the business.
It becomes something we use to understand our impact on the member.
That's the Bigger CU Power Question
Credit unions have always said that we improve financial lives.
I believe we do.
But increasingly, I think our opportunity is to demonstrate it.
That's the question behind CU Power:
Can we use the data a credit union already has to create measurable evidence that members are becoming financially stronger?
Over the past several months, we've started putting that question to the test using real credit union data.
And one thing is becoming increasingly clear:
The answer isn't hiding in one product.
Or one balance.
Or one credit score.
It's in the connections between the signals.
The member's financial life doesn't happen in silos.
Our measurement of it shouldn't either.
Look for the Member Who Looks Fine
So this Halloween, here's the member who scares me most.
It's not necessarily the member who's already delinquent.
We can see them.
It's not the member whose account is repeatedly overdrawn.
We can see that too.
It's the member whose financial capacity is slowly disappearing while every traditional indicator still says:
Fine.
Because that member may not raise their hand.
They may not ask for help.
They may simply keep making trade-offs until there aren't any trade-offs left to make.
And somewhere inside the data, there may already be clues.
Groceries.
Housing.
Transportation.
Debt.
Savings.
Credit.
Cash flow.
Individually, they're transactions, balances and scores.
Together, they can tell us something much more important.
They can help us see the member.
And once we can see them differently, we have an opportunity to serve them differently.
Because the scariest thing hiding in your member data isn't necessarily financial distress.
It's financial vulnerability you haven't learned to see yet.
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