Two key shifts are defining financial services. First, payments are accelerating. Faster rails compress the time between “something feels off” and “the funds are gone,” which means your defenses must operate in real time. Second, social engineering and deepfakes have professionalized fraud to the point where traditional controls (static rules, static training) aren’t enough. If you lead a community institution, this isn’t an “IT problem.” It’s a balance sheet and brand equity problem that demands CEO, CFO, and CIO collaboration.
Banks and credit unions have a superpower bigger institutions envy: trust capital inside the served communities. AI is an amplifier — it can extend that advantage or corrode it. The difference is governance and intent. The institutions that win will innovate boldly and govern relentlessly.
How are financial institutions using AI?
AI finds its best footing when it tackles concrete operational pain. Financial institutions need to avoid hype and focus on measurable wins, such as:
- Fraud/Anti-money laundering (AML). Machine learning can lower false positives, spot risky patterns earlier, and shave minutes off investigations. That means fewer customer interruptions, faster restoration when alerts are valid, and operating expenses that move in the right direction.
- Service & experience. Voice and chat assistants that actually understand member intent can offload a large share of routine calls while preserving the human touch where it matters. The result? Shorter wait times and fewer escalations.
- Risk decisions. Scoring, verification, and income estimation models can improve consistency and speed, if you can explain decisions and maintain control over the data and the model’s use.
Notice what’s not on that list: science projects. You don’t need a research lab or a seven-figure budget. You need the discipline to start where value is clearest and to hold vendors, and yourselves, to standards you should already have in place.
Risks of AI in financial services
While AI can enhance business operations for financial institutions, leaders need to take a deliberate approach to adoption and prepare for common risks, including:
- Unforced errors in fair lending. Complex models that can’t produce clear adverse action reasons create both regulatory and reputational exposure.
- Faster payments fraud. Without dynamic limits, negative lists, velocity thresholds, and contextual step-ups, the first call you get about a fraudulent push payment will be too late.
- Third-party incidents. When many local institutions share the same providers, one outage can have a neighborhoodwide effect. Contracts close that gap.
These are just a few examples of what could go wrong, but none of these are reasons to slow down. They’re reasons to move forward with controlled progression.
How to manage AI risk for examiners
You don’t need brand-new rules for AI governance. You need to apply the ones you already know to a new class of tools.
- Model risk management (MRM). Treat material AI and analytics, whether you built them or licensed them, like models. Keep an inventory, define ownership, validate in proportion to impact, document intended use and limits, and monitor performance over time.
- Third-party risk management (TPRM). Regulatory guidance is clear: plan, diligence, contract, monitor, and, if needed, exit. Contracts for AI vendors should include transparency, testing rights, change control notifications, security requirements, and incident notification timelines that meet regulatory expectations. Remember, if you can’t inventory, validate, monitor, and explain it, you rent the risk.
- GLBA safeguards & board oversight. If AI touches customer information, your InfoSec program, testing cadence, and service provider oversight all apply. Keep the board engaged at the policy and outcome level: what we use, why we use it, and how we’re controlling it.
If you want a common language for business and risk management to meet in the middle, align your AI policy to NIST’s AI Risk Management Framework. It’s pragmatic, outcomes-based, and flexible enough for smaller teams. And as a forward-looking signal, consider ISO/IEC 42001 down the road; it’s the AI analogue to ISO 27001 for information security — useful when examiners ask how you manage AI across its life cycle.
Turning AI risk into strategic advantage
- AI and fraud detection. It’s tempting to bolt AI on top of payments and call it done. But speed changes the problem. With real-time payments and FedNow, prevention and recovery windows are measured in seconds, not days. That means your fraud program needs real-time equipment:
- Limits at the network and participant level.
- Negative lists that are kept fresh.
- Velocity thresholds tuned to the customer’s typical behavior.
- Contextual “smart friction” or step-ups that trigger when risk spikes, not every time a customer pays a bill.
- If your team can show how each control reduces false positives while catching more real fraud, you’ll have something every regulator wants to see — a story about risk that ends in numbers.
- Fair lending & explainability. If AI informs a credit decision, Reg B still expects specific, accurate reasons in the adverse action notice. The “it’s just how it works” excuse doesn’t convince customers or regulators. Bake explainability into implementation by locking down the data you use, testing for bias at design and on a cadence, and ensuring your notice language reflects the true drivers of a decision. You’re not just defending a model, you’re defending the fairness of your process.
Practical steps for AI implementation
Executives ask, “What do we do this quarter?” Here’s a practical, no-drama answer:
- Approve a one-page AI policy that names owners across business, technology, risk, and compliance, and ties AI to measurable outcomes.
- Stand up a model inventory, including vendor tools. Tier by impact and validate the top tier first.
- Repaper key vendors (touting the use of AI) with transparency, testing rights, change control, and incident notification commitments aligned with regulation.
- Tune faster payments controls: limits, negative lists, velocity thresholds, and contextual step-ups. Combine rules with behavioral analytics rather than replacing one with the other.
- Operationalize Reg B for AI-assisted decisions, so adverse action reasons remain specific and defensible.
- Tabletop the 36-hour rule (or 72-hour rule if you’re a credit union) incident notification rule with your core provider, your most critical fintechs, legal, and comms. If you haven’t practiced, you’re not ready.
The leadership imperative
AI isn’t a side project. It’s a strategic lever that, done right, compounds your community advantage. The mandate is simple:
- CEOs — Set the ambition and insist on outcomes you can defend all around, to customers, examiners, and the board.
- CFOs — Tie AI to unit economics and real P&L movement. Your discipline keeps the program honest.
- CIOs/CISOs — Build for resilience. Prioritize security, privacy, monitoring, and change control from day one, not day 300.
- CRO/compliance — Translate governance into practice. Keep the cadence of validation, testing, and reporting tight but proportional.
With AI, we’re all just hitting the on ramp. It’s now time to pick the lane that leads to ambition and accountability. In a year, you’ll have the miles to prove it was worth it.
