Banking Outlook 2026: Macro developments for banks

The lending landscape in 2026 demands a fundamental shift: banks must move from manual, labour‑intensive processes to intelligent, customer‑centric operations. The path forward centres on two core innovations: digital origination and embedded finance, both powered by AI.

Digital origination transforms the customer experience from application to approval. By automating the entire journey, banks reduce friction, lower cost‑to‑serve and free teams to focus on high‑value work. Equally critical is embedding AI throughout the lending process. AI‑powered data analysis and document interpretation accelerate decisioning while reducing manual work. For example, intelligent real‑estate valuation solutions can handle complex analysis automatically, allowing lenders to focus on validation and relationship‑building. In servicing and monitoring, AI systems scan the internet for news, press releases and developments that might affect clients, triggering proactive conversations and early intervention.

These innovations deliver dual benefits: an improved customer experience and more focused, value‑added work for lenders. The foundation for success rests on three key actions. First, develop processes and product portfolios designed for automation using the latest technology. Second, build a solid data foundation that enables expansion and flexibility. Third, leverage that same foundation to unlock the power of AI.

Transformation at scale requires a structured approach. Start small with a specific product segment or portfolio, learn from early wins, build organisational capability, and collect measurable results. This approach builds confidence and momentum for scaling across the broader lending operation.

Deloitte supports banks throughout the transformation journey: programme management, product expertise, technology delivery and compliance guidance. Whether your approach is traditional core‑system modernisation or fintech‑style innovation, we tailor our support to your specific needs and ambitions.

For more insights, click here to read how a European bank reinvented its lending system in the cloud.

Resilience isn’t a compliance checkbox; it’s a holistic approach that requires a strategic outlook and a tactical capability to test, respond and execute. Banks face escalating disruption from geopolitical volatility, cyber threats, technology and power outages, and regulatory shocks. The question isn’t whether disruption will occur, but how quickly you can respond and how prepared you are to adapt and recover.

The shift from planning to practice is fundamental. Identify critical functions that must be kept up and running to serve banks’ customers: core banking activities such as payments, liquidity management, cards and accounts, alongside continuing regulatory obligations such as transaction monitoring.

Embedding resilience by design requires conducting vulnerability scans, mapping dependencies, developing plans and playbooks, performing tests and exercises, and integrating controls into development lifecycles. Step back and think strategically: what is your minimum viable bank (MVB) to survive and continue to serve customers in a disruption? If your MVB is clear, you can prioritise and make strategic decisions across scenarios, response and recovery.

Technology introduces new disruption points; managing them deliberately and with clear priorities is essential. Equally important is investing in people and end‑to‑end ecosystems. Build leadership and personal resilience through mental‑health support, adaptability training and strategic agility. Collaborate with peers and join public‑private groups to strengthen national resilience and create urgency.

Treat resilience as a strategic priority that covers people, operations and technology, reputation, environment and financials. Everyone in the ecosystem shares responsibility. Immediate actions include targeted testing and simulation exercises for high‑impact scenarios, reviewing and diversifying critical third‑party dependencies, and deploying tactical automation in high‑risk areas.

Organisations that prepare their people, systems and ecosystems will lead through the next crisis. Resilience is built through practice, not planning.

Learn more about building organisational resilience.

Payments are undergoing a structural transformation. Real‑time rails, stablecoins, open banking and agentic AI are reshaping how money moves. Simultaneously, AI‑driven fraud is escalating, and regulatory requirements from ISO 20022 to real‑time payment mandates are creating both pressure and opportunity.

Banks must act decisively to modernise with modular, API‑first or serverless cloud architectures that allow them to add real‑time capabilities, tokenisation and stablecoin support without rip‑and‑replace approaches. Running targeted pilots across BNPL, crypto acceptance and stablecoins helps to test commercial viability while building regulatory intelligence.

Hardening AI‑driven controls is equally urgent. Behavioural biometrics, dynamic risk scoring and zero‑trust security defend against sophisticated fraud while preserving customer experience. High‑impact use cases such as instant payroll on real‑time rails, cross‑border remittances using stablecoins and embedded finance at checkout all deliver measurable value today.

The winners will be those who balance speed with control by modernising around real‑time rails, tokenisation and open banking while hardening AI‑driven defences. Immediate priorities include deploying a payments orchestration layer, reinforcing identity and fraud controls, and establishing a regulatory intelligence team to monitor rules and negotiate partnerships.

To explore payments innovation in detail, see Shaping the future of payments.

AI in banking is no longer optional; it’s structural. Yet many banks remain stuck in experimentation mode. Shifting to execution and deploying AI into production requires treating AI as core infrastructure, not a side project.

The biggest opportunities lie where AI reimagines entire customer experiences rather than simply optimising individual tasks. In lending, AI enables faster credit decision‑making and dynamic pricing. In payments, it powers real‑time fraud prevention and smarter routing. Across operations, AI copilots and intelligent automation significantly improve productivity for relationship managers, underwriters and operations teams.

Success depends on embedding AI into priority value chains: lending, payments, financial crime and operations. Focus on high‑impact, measurable use cases with measurable outcomes: credit decisioning that reduces approval times from days to minutes, fraud detection that cuts false positives by double digits, and AI copilots that materially boost banker productivity.

Building strong foundations is essential. Define a clear, top‑down AI strategy with governance aligned to your risk frameworks, prioritise data quality for critical domains, and commit to scaling two or three use cases end‑to‑end. Success comes from focus, not breadth. Embed governance from day one, adopt agile delivery and modular architectures, and ensure adoption across the organisation, not just among specialists.

Immediate actions include defining AI governance, assessing data quality, and identifying high‑value use cases to deploy and scale into production. These steps reduce risk, build confidence with regulators and unlock sustainable competitive advantage.