10 AI Predictions Credit Unions Can’t Ignore in 2026 | 1/2

Plus: AI stops being a pilot, lending gets faster, and more!

Happy New Year!

As 2026 begins, let’s take a look into a crystal ball to see what AI has in store for us.

I put together my top 10 AI predictions based on upcoming tech, use cases, and industry data:

1. AI stops being a pilot and becomes core infrastructure

2. Lending gets faster, more automated, and more dependent on data you can defend

3. Autonomous agents eat 40-60% of routine work

4. Personalization becomes a deposit-retention lever

5. Trust stops being a brand promise and becomes something you have to prove

6. Payments move to digital wallets + real-time rails

7. Fraud goes synthetic

8. Governance and explainability become the blocker… and differentiator

9. The workforce shifts to exception-handling and oversight

10. Data becomes the moat

Read time: 8 minutes

Top AI Predictions for 2026

What to expect from AI this new year…

1) AI stops being a pilot and becomes core infrastructure

Agentic AI is moving from isolated experiments to becoming the main engine of Credit Union operations as members expect AI-level speed and accuracy. As a result, workflows will need end-to-end agentic automation that can read documents, extract data, make preliminary decisions, validate compliance, and escalate exceptions to complete processes.

Nearly half of Credit Unions already use chatbots/virtual assistants, more than two-thirds plan to use AI-driven lending decisioning, and ~66% plan to use AI for credit decisioning. Yet fewer than 20% say their AI deployments are “enterprise-ready.” The ROI is already showing up as infrastructure-level wins: FORUM Credit Union drove a 70% increase in loan processing capacity with agentic AI for document classification and data extraction, Suncoast CU prevented $800,000+ in fraud in six months, Teachers FCU eliminated 8 million manual clicks (13,000+ staff days), and PSCU avoided $35 million in fraud losses.

The most competitive Credit Unions will run lending, fraud, servicing, and compliance on AI-powered workflows that are governed, explainable, and connected to clean data, because legacy cores, siloed systems, and weak AI governance won’t scale past pilots. (link)

2) Lending gets faster, more automated, and more dependent on data you can defend

AI-powered decision engines and agentic workflows are collapsing loan timelines from days to minutes, and in some cases, seconds. The catch is that this speed will only be sustainable for Credit Unions with clean, permissioned data and models that can be clearly explained to regulators.

Research from Celent commissioned by Zest AI shows 67% of lenders expect to have GenAI strategies in place by 2026, with 83% planning to increase GenAI budgets in consumer lending. More than two-thirds of Credit Unions plan to adopt AI-driven lending decisioning. But legacy cores, siloed data, and weak governance continue to limit how fast and safely many institutions can move.

Early adopters like Commonwealth Credit Union illustrate what 2026-ready lending looks like, having automated 70–83% of consumer loan decisions after modernizing underwriting with AI. (link)

3) Autonomous agents eat 40-60% of routine work

Autonomous agents will move beyond assistive automation and begin fully handling high-volume, rules-driven back-office work at Credit Unions. Projections now indicate agents will handle roughly 40-60% of routine banking tasks as workflows mature.

Gartner predicts 40% of enterprise applications will embed task-specific AI agents by 2026, up from under 5% in 2025. In production today, Grasshopper Bank reports agents autonomously handling about 70% of natural-language business queries, contributing to an 18% retention lift. Capital One has also evolved its Eno assistant from a chatbot into an agentic fraud system that resolves disputes and issues refunds without human intervention. Across financial institutions, autonomous agents are already delivering 20-35% operating cost reductions by removing manual steps from back-office workflows.

As agents absorb routine volume, CUs will need fewer staff doing repetitive processing and more staff supervising agents, investigating exceptions, auditing decisions, and improving workflows. (link)

4) Personalization becomes a deposit-retention lever

Personalization is becoming a practical deposit-retention tool as AI copilots and agents anticipate needs, automate money movement, and guide financial decisions in real time. The institutions that get this right will materially reduce deposit churn.

Research from Bain & Company shows proactive, context-aware personalization can lift engagement by 25-30% and keep balances higher with fewer account switches. Industry models estimate AI-driven personalization could retain roughly 15% more checking and savings balances (more than $200B across financial institutions).

Credit Unions that can orchestrate data, personalization, and payments across channels (while maintaining trust, governance, and transparency) will turn personalization into a durable moat. (link)

5) Trust stops being a brand promise and becomes something you have to prove

Trust now needs to be more than just marketing language. It must translate into operational requirements, as autonomous agents make real decisions at scale and regulators, members, and boards demand clear evidence of how decisions were made, with what data, and under which controls.

As Alex Kwiatkowski, Director of Global Financial Services at SAS, puts it, trust is moving from “model-driven” to “proof-driven” intelligence, where every prediction, decision, and interaction must be verifiable, explainable, and auditable. Regulators are reinforcing this shift by raising expectations around transparency, digital identity, fraud controls, and AI governance.

Credit Unions will be judged on how clearly they can show members and examiners why an AI system approved a loan, blocked a payment, flagged fraud, or routed a service request. Institutions that operationalize trust, through embedded audit logs, explainable models, continuous verification, and transparent fraud-resolution processes, will turn regulation into an advantage. (link)

6) Payments move to digital wallets + real-time rails

Payments continue to shift decisively toward digital wallets, real-time rails, and tokenized money movement, with AI agents increasingly initiating transactions on behalf of users. For many members, payments now happen inside wallets and embedded payment flows, with the financial institution operating in the background rather than as the primary interface.

As agentic commerce expands, disputes and fraud increase when autonomous agents transact or are hijacked by bad actors. Adam Neiberg of SAS warns institutions will need to authenticate not just people, but AI agents using techniques like agent tokens, behavioral signatures, and dynamic risk scoring. In parallel, regulated stablecoins, tokenized deposits, and real-time payment rails are entering live pilots for settlement and treasury, promising faster settlement, lower costs, and stronger auditability. (link) (link)

7) Fraud goes synthetic

Fraud is evolving from stolen credentials and basic social engineering to synthetic identities, deepfakes, agent-driven scams, and AI-powered ACH manipulation.

AI-driven fraud is scaling faster than traditional controls: deepfake-related fraud attempts have surged more than 2,100% over the past three years, and genAI-enabled fraud losses could reach $40B annually in the U.S. by 2027. Fraud teams now face disputes triggered by autonomous agents making unauthorized purchases and criminals hijacking or mimicking legitimate agents. At the same time, Nacha’s 2026 ACH rule changes and broader payments regulation are raising expectations for continuous monitoring, faster incident reporting, and stronger controls.

The most effective defenses will come from Credit Unions that leverage their proprietary transaction history, repayment data, and member behavior to spot patterns that synthetic fraud can’t easily fake. (link)

8) Governance and explainability become the blocker… and differentiator

As AI takes on more autonomous work in lending, fraud, and compliance, the hardest part will be proving decisions are fair, explainable, and controlled as regulatory scrutiny tightens.

A Deloitte survey shows governance pressure is rising fast: 38% of AI leaders say data residency/regional compute is very important, and 49% expect it to be a significant factor within the next 1-2 years. On the Credit Union side, the readiness gap is real: fewer than 20% of Credit Unions describe their current AI deployments as “enterprise-ready,” and uncertainty around explainability and fairness is explicitly cited as a top scaling challenge. On the compliance front, institutions that can show audit trails, change controls, model oversight, and defensible decisions will deploy faster and with more confidence. (link)

9) The workforce shifts to exception-handling and oversight

The “human job” inside Credit Unions will change materially as copilots and agentic systems take over routine tasks across lending, servicing, and back-office operations. Day-to-day work will shift away from manual processing and toward handling exceptions, reviewing edge cases, and overseeing risk, compliance, and model behavior.

Forecasts suggest copilots will be embedded in as much as 80% of enterprise applications by 2026, pushing everyday work toward AI-assisted execution with human judgment layered on top. At the same time, labor impact is becoming tangible. Research from MIT, cited in multiple industry outlooks, estimates that AI could replace roughly 11.7% of U.S. jobs (over $1T in wages) with disproportionate pressure on entry-level and repetitive-task roles. Analysts also point to the rise of “agent ops” teams: dedicated roles responsible for training, monitoring, and continuously improving AI agents because reliability is still shaky for full autonomy everywhere. (link)

10) Data becomes the moat

The competitive advantage in AI will come from having the cleanest, most defensible member and transaction data. As synthetic and AI-generated data become easier to produce, the risk is that data will pollute core systems in ways that are difficult to detect but costly to unwind.

Multiple 2026 outlooks point to the same conclusion: a solid model trained on proprietary, high-quality data will outperform a more sophisticated model trained on generic inputs. Repayment histories, transaction patterns, and local market signals are hard to replicate and uniquely valuable to Credit Unions. But that advantage flips into risk if the data foundation is compromised. Synthetic data and GenAI can introduce errors and bias at scale, contaminating credit, fraud, and risk pipelines.

At the same time, previously unusable data is becoming more actionable: AI knowledge agents can access the 80%+ of enterprise data that sits in unstructured formats like text and images, growing 50-60% per year. Winners will unify and govern their data so it can safely power underwriting, fraud detection, and member-facing decisions at scale. (link)

Tips & Use Cases

Learn to apply AI…

Optiri outlines “intentional AI” readiness for Credit Unions: The IT company’s COO & CRO Shane Butcher argued that AI is already embedded in everyday tools, so Credit Unions should focus on data visibility, permissions, vendor policies, and 1-2 high-value use cases instead of chasing hype, especially as AI reshapes both cyber threats and defenses. (link)

Automated underwriting becomes a pillar of Credit Union lending strategy: CU loan balances are projected to grow just 4.5% this year (below the 7% average) before rebounding in 2026. To prepare, CUs are adopting automated decision engines to handle complex data, deliver minute-level approvals, and keep delinquency rates flat or improved while reducing manual underwriting bottlenecks. (link)

Smaller banks lag in GenAI adoption but find gains through internal use cases: Temenos data shows 79% of banks over $250B in assets have GenAI live or planned, compared to just ~40% of banks under $10B. At Grasshopper Bank, internal GenAI use cut repetitive lending follow-ups from two to three hours per loan to two to three minutes, showing how smaller institutions can start with lower-risk, staff-facing workflows. (link)

AI role-play helps Credit Unions pressure-test AI strategy: Try using an AI chatbot to role-play as an executive, member, service rep, or regulator to surface blind spots around trust, access, and where automation helps versus harms the member experience. (link)

How to use the “Frontier Firm” framework to scale AI beyond pilots: Generative AI could unlock up to $340B in annual banking value, with AI-driven banks already seeing 20% productivity gains and 40% fewer fraud false positives as industry AI spend grows to $97B by 2027. ABN AMRO, Ally Financial, and UBS show the Frontier Firm approach in action by starting with AI assistants for staff, progressing to human-led agents, and selectively automating workflows without giving up control. (link)

Why GenAI projects show fast demos but struggle in real-world rollout: Generative AI makes it easy to build impressive prototypes quickly, but many initiatives stall later when teams discover the data, workflows, testing, or staffing aren’t ready to support production. Stay focused on solving real business problems early, so promising demos turn into tools that actually work at scale. (link)

Keeping up with Tech

The latest in fintech and tools…

Visa and Fiserv expand agentic AI payments: Fiserv will enable Visa Intelligent Commerce and Trusted Agent Protocol across their merchant networks, allowing AI-driven transactions with built-in bot detection, consumer authorization, and checkout validation. Similar agentic payment protocols are also rolling out from Mastercard, Stripe, and PayPal, increasing pressure on banks to prepare for AI-initiated payments. (link)

Google rolls out December AI updates focused on speed, verification, and translation: Google launched Gemini 3 Flash across the Gemini app and Search for faster reasoning at lower cost, added AI video verification using SynthID watermarks, and introduced live speech translation in Google Translate across 70+ languages. (link)

Google highlights AI agents and reasoning breakthroughs in 2025 review: 2025 was the year AI began reasoning and acting through agentic systems, led by Gemini 3 and Gemini 3 Flash, with major gains in multimodal understanding, speed, and efficiency. The impact includes 20%+ productivity gains in AI-driven workflows and rapid adoption of AI across science, products, and infrastructure. (link)

In Other News

Related news you can learn from…

• High-agency CFOs focus on decision systems over AI outputs (link)

• Top AI stories of 2025 show where banking innovation is heading (link)

• Job-simulation academies build AI-ready talent outside traditional pipelines (link)

Community Corner

Memes and visuals…

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Thanks for reading!

Until next week,

— Credit Union AI Guy

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