Overdraft is shifting from fee engine to financial safety net. Here’s how credit unions can use AI to build member-centric, compliant overdraft programs that build trust.
Most credit unions are rethinking overdraft right now—not because regulators told them to, but because members are quietly voting with their feet.
Overdraft fees dropped by roughly 50% across U.S. banks and credit unions between 2019 and 2024 as institutions pulled back from the old fee-heavy model. That’s not a small tweak. It’s a sign that overdraft, as members knew it, is being rebuilt from the ground up.
Here’s the thing about overdraft programs: they sit at the intersection of trust, financial wellness, and compliance. If you get them wrong, you don’t just lose fee income—you lose credibility. If you get them right, you become the institution members rely on when money is tight.
This post builds on themes from Cheryl Lawson’s conversation on The CUInsight Network about responsible overdraft solutions and member education, and connects them to where credit unions are heading next: AI-driven, member-centric overdraft management.
We’ll look at how to design overdraft programs that are:
- Fair, transparent, and compliant
- Powered by AI and data instead of guesswork
- Focused on financial wellness, not fee extraction
What a “Responsible” Overdraft Program Looks Like Now
A responsible overdraft program today is simple to understand, transparent to members, and defensible with regulators.
Cheryl Lawson puts it plainly:
“It’s all about meeting the needs of members.”
That sounds obvious, but most overdraft programs were historically built to meet the needs of the balance sheet first. The modern approach flips that.
Core principles of a modern overdraft solution
If you’re evaluating your current program, these are the non‑negotiables:
-
Plain-language member communication
Members should be able to explain your overdraft program to someone else in under 30 seconds. That means:- Clear disclosures in human language, not legalese
- Simple examples (“If you spend $65 with only $50 in your account…”)
- Consistent messaging across online banking, mobile, contact center, and branches
-
Predictable, transparent fees
Fee structures that surprise members—tiered schedules, unclear posting order, unclear limits—are exactly what drive complaints and litigation. A responsible program:- Uses a flat, clearly disclosed fee when possible
- Minimizes “gotchas” like multiple fees on the same bad day
- Surfaces fees in real time at point of decision when the member can still say no
-
Data-backed eligibility and limits
Overdraft should reflect the member’s real relationship with the credit union:- Income patterns and deposit history
- Tenure and product mix
- Historical repayment behavior
This is where AI for credit unions starts to shine—turning those signals into smart, dynamic overdraft limits instead of static one-size-fits-all thresholds.
-
Compliance baked in, not bolted on
A responsible program aligns with regulatory guidance on:- Clear opt-in practices for one-time debit and ATM transactions
- Accurate, non-deceptive marketing claims
- Reasonable and explainable fee practices
A strong compliance review function (like the one Lawson leads at JMFA) doesn’t just check documents—it shapes program design.
Where AI Fits: From Fee Engine to Financial Safety Net
AI turns overdraft from a blunt instrument into a targeted support tool for members who actually need it—and can reasonably handle it.
When you apply AI to overdraft management, three big things change: who gets overdraft, how it’s priced, and how it’s presented.
1. Smarter eligibility and dynamic limits
Traditional overdraft:
- Uses static rules (e.g., “90 days in good standing, $500 standard limit”)
- Ignores nuance in member behavior
AI-driven overdraft:
- Analyzes years of transaction history, income volatility, and repayment habits
- Assigns personalized overdraft limits that adjust over time
- Identifies members who shouldn’t be offered overdraft because it will likely harm them
This is better for:
- Members, because they’re less likely to get overextended
- Regulators, because your decisioning is explainable and risk-based
- The credit union, because losses and complaints drop when limits are realistic
2. Real-time risk and wellness signals
AI can monitor account activity continuously and flag emerging risks like:
- Increasing frequency of overdraft use
- Growing reliance on overdraft as a de facto line of credit
- Income instability or missed deposits
Once those signals fire, your system can:
- Lower or pause limits to prevent harm
- Trigger proactive outreach from your contact center
- Offer alternatives like small-dollar loans, payment plans, or budgeting tools
This turns overdraft from “we’ll pay it and charge you later” into “we see you’re struggling, here’s a safer option.”
3. Personalized member communication in the moment
Overdraft is ultimately a communication problem. Members get angry when they feel blindsided.
AI-powered digital experiences can:
- Send just-in-time alerts: “This purchase may overdraw your account and incur a $X fee. Do you still want to proceed?”
- Offer context-aware suggestions: “If you move $40 from savings, you’ll avoid a fee.”
- Tailor tone and channel based on member preference (push, SMS, email, in-app messages)
When members feel in control—even when money is tight—they’re far more likely to view overdraft as a service instead of a penalty.
Designing AI-Driven Overdraft with Compliance at the Center
Responsible AI in overdraft is not optional. Overdraft has already been a magnet for class-action lawsuits around unclear practices and misleading disclosures.
The next wave of overdraft litigation is likely to focus on opaque algorithms and unfair outcomes. The good news: you can design around that.
Compliance review for AI-based overdraft
A strong compliance review program for AI overdraft should address:
-
Explainability
You should be able to answer, in plain language:- Why did this member get a $700 limit while another with similar income got $300?
- Why was this transaction paid while another was declined?
If your team can’t explain it, neither will members—or regulators.
-
Bias and fairness testing
Regular audits should check whether your AI models:- Produce materially different outcomes for protected classes
- Rely on proxy variables that could introduce discrimination
If issues show up, you either adjust the model or add business rules that enforce fairness.
-
Clear consumer disclosures
Members don’t need a whitepaper on your AI model, but they do need clarity on:- What factors generally drive eligibility and limits
- How their limit might change over time
- How to opt out, ask questions, or file a complaint
-
Robust documentation
For every model you use in overdraft decisioning, maintain:- Purpose and scope
- Training data sources
- Validation results and ongoing monitoring
This is exactly the type of thing Cheryl Lawson and her peers mean when they talk about “responsible compliance programs.”
Making Overdraft Member-Centric with AI: Practical Moves
If you’re leading a credit union right now, you don’t need a sci-fi roadmap. You need a practical, phased approach to overdraft modernization.
Here’s one that works.
Phase 1: Get your current house in order
Before you add AI, make sure your existing overdraft solution is:
- Transparent: Clean up disclosures and member-facing copy
- Consistent: Align policies across products and channels
- Measurable: Track complaints, fee concentration, and repeat overdrafters
Baseline metrics to track:
- % of members using overdraft in a year
- % of overdraft fees paid by the top 10% of users
- Complaint rate related to overdraft
You’ll need these to prove that AI-powered changes actually help.
Phase 2: Add intelligent alerts and education
This is often the easiest, highest-ROI starting point.
Use AI and analytics to:
- Predict accounts at risk of overdrawing in the next 7 days
- Send tailored alerts with options: transfer from savings, skip a payment, or talk to a coach
- Surface short, targeted educational content in your app when members are close to zero
You’re already aligning with the member-centric banking theme here: support before fee.
Phase 3: Implement AI-based limits and wellness safeguards
Once you’re comfortable with the data and patterns, roll out:
- Dynamic overdraft limits updated monthly (or even weekly)
- Automatic protective caps on chronic users
- Pathways out of frequent overdraft, such as:
- Graduated limit reductions
- Automatic eligibility checks for lower-cost credit products
Keep humans in the loop for edge cases—this is where your branch and call center teams can shine.
Phase 4: Use AI for strategic insights
Beyond individual decisions, AI can help you answer bigger questions:
- Which member segments actually benefit from overdraft?
- Where are members substituting overdraft for personal loans or lines of credit?
- How does overdraft behavior correlate with member loyalty, product depth, and attrition?
When you see overdraft as one component of a financial wellness ecosystem, your decisions change. It stops being a separate fee bucket and becomes part of a broader member relationship strategy.
Staying Relevant: Accessibility, Innovation, and Trust
Cheryl Lawson talks about two things that keep credit unions relevant: staying open to innovation and being accessible to all members.
AI for credit unions is only useful if it supports those goals.
- Accessibility means making sure overdraft decisions don’t quietly shut out lower-income or thin-file members just because their data looks messy. Smart design and human oversight prevent that.
- Innovation means testing tools like AI-driven coaching, adaptive limits, and hyper-personalized alerts—but doing it in a way that’s explainable and respectful.
Here’s the reality: members don’t care whether you’re using AI or not. They care whether you’re there when their car breaks down two days before payday—and whether you treat them fairly in that moment.
A member-centric, AI-informed overdraft solution does exactly that:
- Predicts when they might need help
- Offers clear options instead of silent penalties
- Uses data to say “yes” more safely, not “no” more often
Credit unions were built on people helping people. AI just gives you better tools to keep that promise at scale.
If you’re revisiting your overdraft program for 2026, the best question to start with isn’t “How do we replace fee revenue?” It’s: “How do we use data and AI to make overdraft feel like support, not punishment?”
Answer that honestly, and the rest of the strategy comes into focus.