Ten priorities every bank and credit union must address to grow, not retreat, in the year ahead
I’ve spent my career at the intersection of APIs and financial services, from Axway to OpenFinity, and now at Tyk, where I lead our financial services practice. If there’s one thing I’ve learned, it’s that the institutions that thrive aren’t the ones with the biggest budgets. They’re the ones that see where the industry is heading and move before their competitors do.
2026 is shaping up to be that pivotal year. The convergence of regulatory change, AI maturity, and customer expectations is creating a moment where financial institutions must either lean in or watch their relevance erode.
Here’s my perspective on what banks and credit unions need to prioritize in 2026, specifically through the lens of API strategy and AI readiness.
1. Treat Open Banking as Strategy, Not Compliance
The regulatory landscape around Section 1033 remains in flux. The CFPB is rewriting its open banking rule, and enforcement has been paused while the agency figures out its approach. Many institutions are breathing a sigh of relief.
That’s the wrong reaction.
Regardless of when, or in what form, regulation lands, open banking is happening. FDX standards are maturing. Fintechs are building. Consumers expect to connect their accounts to the apps they love. The institutions that build API infrastructure now won’t be scrambling when clarity arrives. They’ll be capturing market share from those who waited.
The question isn’t whether to invest in API infrastructure. It’s whether you’ll be ready when the market moves.
2. Move Beyond Chatbots to Agentic AI
According to recent industry surveys, 70% of banks are already deploying some form of agentic AI: systems that don’t just answer questions but take autonomous action within defined guardrails. Fraud detection, loan processing, customer onboarding, AML monitoring. These aren’t pilots anymore. They’re production systems handling real transactions.
But here’s what many institutions miss: agentic AI is only as powerful as the APIs it can access. An AI agent that can’t query your core banking system, check real-time account balances, or initiate transactions is just an expensive chatbot.
The institutions winning with AI in 2026 are those that built their API foundation first. They have standardized interfaces, proper authentication, rate limiting, and governance in place. The AI is the last mile, not the starting point.
3. Enable Embedded Finance, Built on Open Banking Rails
Open Banking and embedded finance aren’t separate strategies; they’re the same infrastructure. The standardized, consumer-permissioned data sharing you build for FDX compliance is the foundation for embedding your products in partner ecosystems.
When a fintech app offers your loan products at the moment a small business owner reconciles their books, that transaction depends on the same secure, consent-driven data flows you’re building for Open Banking. The APIs that let customers share account data with budgeting apps are the same APIs that let partners embed your payment services in their platforms.
The institutions that recognize this connection have an advantage: they build once, and service multiple business use cases. Their FDX-compliant APIs power both regulatory compliance and revenue-generating partner integrations. Their developer portals serve both third-party data aggregators and embedded finance partners. Their consent management handles both consumer data requests and partner authorization.
The institutions that treat Open Banking as a compliance checkbox and embedded finance as a separate innovation initiative will build redundant infrastructure, duplicate governance, and fragment their partner strategy. In 2026, you can’t afford that inefficiency.
4. Prepare for the AI Fraud Arms Race
Generative AI has been fully weaponized by fraudsters. Synthetic identities, deepfake videos, real-time voice impersonation: these aren’t theoretical threats. They’re happening now. UK Finance data shows fraud complaints are on a steep upward trajectory, and banks remain the institution most frequently named in fraud reports.
Traditional rule-based fraud detection is no longer sufficient. You need AI fighting AI: behavioral biometrics that analyze how users interact digitally, real-time anomaly detection that catches patterns humans would miss, and continuous verification rather than point-in-time identity checks.
This requires APIs that can share fraud signals in real-time across your systems, and ideally with other institutions. The institutions that silo their fraud data are making themselves easier targets.
5. Build API Security for an AI-First World
Gartner predicts more than 30% of the increase in demand for APIs will come from AI and tools using LLMs by 2026. That’s not just your internal AI agents. It’s your customers’ AI agents, your partners’ AI agents, and, yes, malicious AI agents probing for vulnerabilities.
Your API gateway needs to distinguish between legitimate AI agents acting on behalf of customers and unauthorized bots attempting credential stuffing or data harvesting. This requires intelligent governance at the API layer: sophisticated rate limiting, behavioral analysis of API consumers, and real-time threat detection.
FAPI 2.0 compliance becomes table stakes here. Financial-grade API security with DPoP token binding, pushed authorization requests, and proper scope management isn’t gold-plating; it’s the minimum bar for institutions that want to safely expose data to the AI ecosystem.
6. Escape the ESB and Legacy Gateway Trap
Many financial institutions are running API infrastructure designed for a pre-cloud era. Complex ESBs, heavyweight gateways with per-call licensing, platforms that require specialized consultants to configure. These systems worked when APIs were internal integration plumbing. They don’t work when APIs are your product.
The problems compound quickly: licensing costs that escalate as your API traffic grows, architectural complexity that slows every deployment, vendor lock-in that limits your technology choices. One mid-sized bank I spoke with was spending hundreds of thousands annually on a legacy platform they no longer had internal expertise to maintain. Contractors who built it had moved on, and every change required expensive external support.
Modern, cloud-native API infrastructure changes the equation. Kubernetes-native platforms scale horizontally without licensing penalties. GitOps workflows let your existing development teams manage APIs without specialized training. Transparent pricing models don’t punish success; your costs stay predictable whether you’re handling ten thousand or ten million daily requests.
The infrastructure you choose in 2026 will determine your agility for the next decade. If your current platform can’t support AI agent traffic, real-time event streaming, and global multi-region deployment without expensive professional services, it’s time to modernize.
7. Prepare for the Zero-Click Economy
Forrester predicts that by 2026, financial institutions will see human visits to their websites drop by 20% while machine-initiated traffic surges by 40%. Your customers’ AI assistants will be shopping for mortgage rates, comparing savings accounts, and executing transactions without ever loading your homepage.
To compete in this world, you need machine-readable APIs with real-time pricing, standardized data formats, and transparent terms. If an AI agent can’t programmatically query your loan rates and compare them to competitors, you won’t be in the consideration set.
This is embedded finance taken to its logical conclusion: your products distributed through ecosystems you don’t control, accessed by AI agents you didn’t build, serving customers who never interact with your brand directly.
8. Automate Lending with Explainable AI
90% of financial institutions plan to enhance their lending capabilities, with credit unions particularly focused on AI-powered underwriting. The business case is clear: faster decisions, lower operational costs, better risk assessment.
But regulators will require explainability. When you deny a loan, you need to explain why, and “the model said so” isn’t acceptable. This means building AI systems with audit trails, feature importance tracking, and human-interpretable decision logs.
The API architecture matters here too. Your lending AI needs to query credit data, income verification, account history, and fraud signals in real-time. Fragmented data across disconnected systems isn’t just an operational headache; it’s a compliance risk.
9. Invest in AI Governance and Human Oversight
Nearly 50% of banks are creating dedicated roles for supervising AI agents. This isn’t bureaucracy; it’s recognition that autonomous systems require oversight. 92% of banks report skills gaps as a major hurdle to AI deployment, second only to regulatory compliance challenges.
Here’s the insight that changes everything: LLMs excel at non-deterministic planning, reasoning through ambiguity, interpreting intent, deciding what to do next. But execution must be deterministic. When an AI agent decides to move money, query a customer record, or trigger a downstream service, that action needs to flow through infrastructure you control, with policies you define, using standards that both humans and machines understand.
The institutions that succeed will build what we call an “AI Control Stack”: a layered architecture that brings deterministic governance to non-deterministic intelligence. It separates the creative reasoning AI does well from the precise execution your regulated environment demands.
The Control Stack isn’t about limiting AI’s potential. It’s about deploying it responsibly in a regulated industry where trust is your primary product.
10. Fix Your Data Foundation
Everything I’ve described depends on one thing: clean, accessible, well-governed data. AI priorities in 2026 will include rigorous data quality management, automated testing of AI systems, and empowering decision-making with richer, more integrated data sources.
Most institutions have data scattered across dozens of systems, formats, and databases. Before you can build AI agents, deploy open banking APIs, or enable real-time fraud detection, you need to know where your data lives, how it flows, and who has access.
Map your data architecture. Implement API management that provides visibility into every integration. Build the governance layer first; the AI will thank you later.
The Bottom Line
2026 isn’t about choosing between APIs and AI. They’re two sides of the same coin. AI without APIs is a brain without a nervous system: intelligent but unable to act. APIs without AI are pipes without purpose: connected but not smart.
The institutions that will grow in 2026 are those building both in parallel: API infrastructure that enables AI to access and act on real-time data, and AI capabilities that make their API investments dramatically more valuable.
At Tyk, we’re seeing this convergence every day. Financial institutions that started with API management for compliance are now using the same infrastructure to power AI agents, enable embedded finance, and prepare for whatever regulatory framework ultimately emerges.
The future belongs to institutions that architect for it. Is your infrastructure ready?
Let’s Talk
I work with banks and credit unions every day on API strategy, FAPI compliance, Open Banking readiness, and AI enablement. If you’re thinking through any of these priorities for 2026, I’d welcome the conversation.
Reach out to me on LinkedIn or schedule a call with our financial services team at tyk.io.
About the Author
Laura Heritage is Principal Solution Architect for Financial Services at Tyk, where she leads technical sales engagements and thought leadership for the banking and fintech sectors. With deep expertise in FAPI 2.0, FDX, Open Banking, and API security, Laura helps financial institutions build API strategies that enable innovation while meeting regulatory requirements. Prior to Tyk, she held roles at Axway and OpenFinity, building a career at the intersection of APIs and financial services.
