10 tech trends reshaping financial services in 2026
14 min read 4 December 2025
The financial services landscape is evolving faster than ever. From humanised digital experiences to agentic AI and digital asset readiness, each trend represents a critical shift shaping the industry’s future.
We explore ten interconnected themes that will define success in 2026 and beyond—covering how firms can modernise architecture, embed AI responsibly, strengthen resilience, and optimise technology investment. Together, these trends form a roadmap for building trust, driving innovation, and staying competitive in a 24/7 digital economy.
We examine each of these trends through five key lenses:
- What’s changing? The shifts reshaping financial services
- What’s driving it? The forces accelerating adoption
- Where should firms focus? Practical priorities for action
- Who’s doing it now? Examples from the market
- Why does it matter? The impact on performance and growth
Trend 1: Humanised digital
Customers want choice in how they interact with financial institutions. Investing in multi-channel access in line with your distribution channel strategy, boosts experience and loyalty.
What’s changing
In 2026, financial institutions will build unified digital ecosystems that blur channel boundaries and provide seamless, contextual experiences. With more choice than ever, customers expect always-on self-service across channels, with the reassurance that human support is available at critical moments. The market has already started shifting - contact centres are evolving, mobile apps are being redesigned to flex around customer needs integrating insights from all channels to provide contextualised, holistic experiences; becoming the provider of choice lies in combining digital convenience with human empathy.
What’s driving it?
- Waning customer loyalty: Firms are already investing in a multi-channel strategy so that customers can access services on their channel of choice based on preferences and convenience.
- Digital inclusion and human empathy: There is a continued focus on ensuring digital channels are accessible to all, including underserved or vulnerable populations, with human touch embedded throughout critical journeys.
- Physical engagement for complex matters: There are moments for customers where human touch is preferred (e.g. bereavement, Power of Attorney). Financial institutions need to find a balance between branch and digital, whilst embedding more human interactions and tone throughout the customer's digital journey.
Where should firms focus?
To deliver human-centered digital experiences, several areas need to be considered:
- Channel strategy and experience: Define how digital and human interactions complement each other for a seamless customer experience.
- Flexible tech infrastructure: Build to enable integration with third-party ecosystems to deliver tailored services and become a lifelong financial partner.
- Real-time data and analytics: Aggregate customer insights across channels to personalise customer interactions, better anticipate needs and drive loyalty.
- Work-force upskilling: Equip staff with the tools, skills and real-time insight to deliver contextual and personalised customer experiences.
Who’s doing it now?
Why does it matter?
A clear channel strategy, unified technical ecosystem and access to customer data and insights will allow human and AI agents to deal with customer queries together, in a truly seamless and customer centric way. Financial institutions who embrace this will improve customer loyalty, increase cross-sell opportunities and ultimately unlock future growth.
Trend 2: Good data = scaled AI
Re-building data foundations to create trusted, connected and high-quality data estates that make AI scalable, enhance real-time analytics and make decision making faster and safer across financial services.
What’s changing?
In 2026, financial institutions are shifting from fragmented data stores to enterprise-wide data product foundations that support safe, scalable, and high value AI. Years of product launches, mergers, hybrid technology environments and regulatory shifts have created complex, siloed environments that now limit growth and operational resilience. Firms are looking to AI to drive growth and productivity; however, they need the underlying infrastructure that is reliable and explainable, supported by real-time analytics spanning customer, product, finance, and risk and underpinned by trusted, shareable data across the organisation. Financial institutions are confronting data environments that were never designed for enterprise scale, yet the data foundations have become critical infrastructure, not technical hygiene, and without them, AI remains experimental and transformation slows.
What’s driving it?
- Exposed data gaps: AI is exposing gaps in data quality, lineage and provenance that directly impact model safety, quality and reliability.
- Customer expectations: Increasing focus on real-time engagement and personalised experiences require accurate, trusted and accessible data across the enterprise.
- Regulatory scrutiny on model explainability: Decision transparency and operational resilience is intensifying. Heightened regulatory attention on data sovereignty, including concerns around bias, model documentation and auditability, is accelerating the case for standardised controls
Where should firms focus?
To strengthen their data foundations and unlock scaled AI in a safe and reliable way, firms will need to develop capability across several areas:
- Modern data architecture: Utilise data platforms to build ‘data products’ that support consistent, well-organised, high-quality data at scale.
- Governance and quality: Define ownership, standardise definitions and automate controls to drive accuracy, completeness and compliance.
- Interoperability and access: Create a structured approach to data sharing across customer, product, finance, and risk processes, with support policies on data usage, privacy and permissions.
- Resilience and oversight: Ensure observable, reliable data flows for confident decision making.
Who’s doing it now?
Why does it matter?
Strong data foundations determine whether firms can scale AI technology investments. Reliable enterprise data improves transparency, reduces cost, accelerates compliance, and strengthens financial performance. Firms that act now will innovate faster and unlock more value; those that don’t face higher cost, higher risk, and slower delivery.
Trend 3: Continuous infrastructure investment
Continuous investment creates a modern, connected infrastructure and provides the resilient, scalable foundation needed to support always-on services and enterprise-wide transformation.
What’s changing?
Legacy infrastructure built for batch processing and standalone operations can’t meet real-time customer demands, scalable business growth, or AI. Financial institutions are investing not in one-off, multi-year projects, but in continuously building connected, resilient technology foundations that deliver reliable performance and seamless integration across applications, infrastructure, data, and services.
What’s driving it?
- Simplification pressure: Complex estates from years of incremental change, platform diversification and tactical integration need consolidation and standardisation to scale.
- Regulatory resilience: Rules on disruption response, dependency mapping and resilience testing are shaping network and failover design and infrastructure governance.
- Always-on expectations: Customers and regulators demand uninterrupted digital access, reducing tolerance for disruption, placing pressure on availability, failover and automated recovery.
- Real-time connectivity: Faster decision cycles, instant payments and data-intensive operations require low-latency and consistent performance across applications.
- Cloud maturity: Firms are moving beyond migration to modernise applications, optimise cost and resilience, and embed automation across cloud environments.
- AI acceleration: Data-heavy use cases require elastic compute, low latency connectivity and well-instrumented infrastructure to support analytics and real-time operations.
Where should firms focus?
To operate a connected, resilient, and adaptable tech foundation for future growth, firms must move beyond fragmented legacy systems and consider the following improvements:
- Modern architecture: Focus on streamlined, modular, interoperable platforms with repeatable design patterns.
- Cloud and compute engineering: Create elastic, automated compute environments supporting high-volume processing data-heavy workloads.
- Integration and connectivity: Replace fragmented point-to-point integrations with API-led, event-driven connectivity and data flows.
- Networks and observability: Focus on low-latency networks with real-time monitoring for rapid issue detection.
- Resilience engineering: Align engineered failover, automated recovery and defined resilience patterns to critical services and regulatory expectations.
- FinOps discipline: Build a robust financial operations team for continuous cost optimisation, efficiency and governance for cloud environments.
Who’s doing it now?
Why does it matter?
Continuous infrastructure investment enables secure, connected, and resilient services at scale. It accelerates delivery of new capabilities - especially enterprise-wide AI - while reducing risk and improving operational efficiency. Firms that modernise incrementally will move faster and innovate with confidence.
Trend 4: AI workforce culture
To unlock business value from AI, the workforce needs to trust the technology and the operating model needs to enable that. Governance will need to mature to ensure that AI can be a partner in employees’ day-to-day work.
What’s changing?
In 2026, financial institutions must embed AI into their workforce culture, redefining roles, operating models, and governance to ensure AI is used effectively and ethically. Many financial institutions are adopting native AI tools already part of their enterprise or hyperscaler suite and building internal models using public LLMs. Training programs are rolling out as part of adoption as building trust and clear guidelines for responsible AI will be critical.
What’s driving it?
- Competitive pressure: As AI adoption increases, laggards now risk losing market share.
- Productivity gains: Automating repetitive tasks to free time for value-added insights boost output.
- Rapid AI evolution: Successful pilots are accelerating enterprise-scale deployments, requiring new operating models to support embedding AI into the workforce.
Where should firms focus?
To successfully embed an AI workforce culture, several areas need to be considered:
- Cross-functional collaboration: Involve teams beyond digital and AI on AI design – employees in ops, compliance, risk, and tech all need to play a role to extract value from investments.
- Operating model transformation: Continuously adapt workflows and refine processes as employees discover new AI use cases.
- Governance revision: Simplify and mature governance to enable ethical, efficient and effective adoption and deployment by employees.
- Feedback and knowledge sharing: Continuously refine and train AI models and colleagues through workforce feedback on accuracy of outputs and by regular sharing of best practices.
Who’s doing it now?
Why does it matter?
The workforce remains a financial institutions greatest asset. If employees don’t see AI as a work companion, investments risk becoming redundant. Those that normalise AI will attract data-fluent talent and unlock greater value from technology.
Trend 5: Smarter insight = stronger resilience
Financial institutions are increasingly using digital technology to simulate the ‘cause and effect’ to support future design or performance considerations. This supports proactive planning, helping leaders anticipate risks, maintain continuity, enabling smarter decisions.
What’s changing?
In 2026, resilience in financial services will shift from reactive recovery to proactive anticipation. Financial institutions are building integrated capabilities that link strategy, technology, cyber, risk, and operations, creating real-time predictive interventions and continuous resilience technology ecosystems.
What’s driving it?
- Operational resilience and business continuity: Predictive scenario testing enables “war-gaming” cyberattacks, outages, and market shocks to identify vulnerabilities and optimise recovery plans without real-world risk.
- Technology and data advances: An ongoing move to cloud-native platforms and AI-driven process mining unlock new data sources and accelerate digitisation of infrastructure.
- Market volatility: Geopolitical and economic instability makes data-driven, scenario-tested decisions mission-critical before committing capital.
Where should firms focus?
To achieve stronger resilience, several areas need to be considered:
- Technology architecture: Build holistic enterprise views of applications and infrastructure for accurate modelling using IT configuration management database and AI tools.
- Data and analytics: Improve data ingestion and accuracy, whilst adopting advanced analytic techniques to assess holistic network effects, moving beyond linear models.
- Regulatory alignment: Ensure digital twin simulations meet stress-testing and privacy requirements within resilience frameworks.
Who is doing it now?
Why does it matter?
Resilience is now a performance driver, not just a safeguard. Embedding intelligent monitoring and predictive response reduces downtime, cuts recovery costs, and preserves customer trust. Advances in AI and simulation make predictive resilience achievable at scale - turning reliability into a competitive advantage.
Trend 6: Purposeful technology investment
IT budgets are constantly being scrutinised. Applying technology strategies around a build, buy and outsource model helps reduce redundancy, streamline systems, and deliver savings without sacrificing performance.
What’s changing?
Technology budgets keep rising, yet financial institutions struggle to turn spend into real business value. Estates have become complex and costly to maintain. Hyperscaler AI investments will soon push costs higher. Common themes across the industry include overlapping or redundant applications, duplicative vendors and underused licenses and platform features. Cloud workloads are expanding faster than governance frameworks and driving higher operating costs. Executives want modern, resilient, cost-effective foundations, but too much spend is locked in duplicated apps, fragmented platforms, and unmanaged cloud consumption.
What’s driving it?
- Escalating run-the-business (RTB)/ change-the business (CTB) costs: Costs are outpacing budgets and pressuring modernisation.
- Cloud and AI consumption: Increasing consumption is increasing operating bills without strong FinOps discipline.
- Weak ROI: Boards are demanding evidence-based decisions tied to strategic value, with clear return-on-investment
Where should firms focus?
TTo move from rising cost to smarter value-focused technology investment, firms should focus on:
- Single TCO view: Consolidate spend across RTB, CTB, resource, platforms, licenses, and cloud to surface allocated spend and duplication.
- Simplification and resilience: Prioritise investment to reduce complexity and increase resilience and adopt modular frameworks that focus on business wide benefits.
- FinOps and commercial discipline: Strengthen disciple by managing cloud economics, renegotiating vendor contracts, eliminating unused capacity and ensuring consumption aligns to value.
- Platform consolidation: Rationalise and consolidate by removing duplicated services, simplifying architecture and modernising more predictably across business lines.
- Long term advantage: Shift investment to reusable services, shared platforms, and modern engineering practices.
Who’s doing it now?
Why does it matter?
Smarter technology investment determines whether firms unlock real value. Without discipline, estates grow more complex, vendor costs rise, and cloud spend spirals. Those that simplify and optimise their technology decisions will free funds for accelerated transformation, improve stability, and deliver measurable outcomes.
Trend 7: Regulation by design
Financial institutions will be moving from fragmented pilots to enterprise-wide, auditable AI frameworks where governance, data integrity, and human oversight are built into the tech stack, making trust, transparency, and accountability the enablers of innovation.
What’s changing?
AI in financial services is moving from experimentation to regulated enterprise-level capability. The next wave of leaders will embed governance, data discipline, and ethical oversight into every model, creating AI that is explainable, compliant, and trusted. While AI promises efficiency, better customer experience, and stronger risk management, it also introduces systemic and ethical risks such as bias, data misuse, and opaque decision-making. The shift is happening now due to increasing expectations on AI governance by regulators worldwide
What’s driving it?
- Rapid AI adoption: AI now underpins credit decisions, underwriting, claims, fraud detection, trading, and customer service, risk modelling and an expanding range of generative AI applications - raising the stakes for errors or bias and increasing operational and customer-facing risk.
- Regulatory momentum: Regulatory frameworks across the globe are shifting from general guidance to enforcement, moving AI from innovation-first to accountability-first.
Where should firms focus?
Financial institutions need to move from fragmented pilots to standardised, auditable frameworks and get ready across four dimensions:
- Governance and controls: Define roles for model approval, monitoring, and escalation. The control framework should ensure AI is explainable and traceable and aligned to ethical standards and risk appetite.
- Data: Ensure high-quality, privacy-compliant data with full lineage and robust management.
- Technology: Ensure secure, resilient and scalable infrastructure. Hybrid cloud, model monitoring, audit trails, and robust vendor oversight is essential to maintaining operational integrity.
- People: Invest in a skilled, ethically aware workforce that understands AI outputs and applies human-in-the-loop judgment.
Who’s doing it now?
Why does it matter?
Embedding regulation into AI design is not just compliance – it’s a competitive edge. Firms that integrate governance early will avoid regulatory and reputational risk, build trust, and accelerate safe adoption. In a world where trust and resilience define success, “regulation by design” turns compliance from cost into advantage.
Trend 8: Digital asset readiness
As digital assets become integral to the global financial system, institutions must modernise architecture - transforming core systems, data, and infrastructure to support real-time, token-based operations, seamless CBDC and stablecoin integration, and the scalability, security, and resilience needed for a 24/7 digital economy.
What’s changing?
In 2026, digital assets will become core to financial services. Encouraged by regulatory clarity, technical maturity and institutional adoption, financial institutions are expanding their digital asset offerings. They’re utilising tokenisation to facilitate settlement of deposits, securities, and real-world assets (RWAs); introducing digital asset custody services to provide institutional level security; and using stablecoins for cross-border payments. Public institutions are also exploring central bank digital currencies (CBDCs), new financial market infrastructures (FMIs), and digitally native bond issuances, while insurers test tokenised contracts, parametric insurance, and digital asset custody solutions. The result? Faster, safer, more transparent and reliable finance, opportunities to enhance products, streamline processes, and unlock new revenue streams.
What’s driving it?
- Technology and network maturity: Blockchain and distributed ledger technology (DLT) is more scalable, secure, and interoperable with traditional banking systems. Growing liquidity on digital assets infrastructure also means they can be better utilised by participants.
- Operational efficiency, reliability and cost pressure: DLT has the potential to offer faster, cheaper and instant alternatives to legacy systems like SWIFT and CHAPS.
- Regulatory clarity: Frameworks such as UK FCA guidance, EU MiCA, and US GENIUS Act set clear rules for stablecoins and tokenised money.
Where should firm’s focus?
To best deploy the new capabilities digital assets, offer, financial institutions need to consider:
- Technology and infrastructure: Build scalable, low-latency systems resilient to market shocks and operational failures.
- Security architecture: Strengthen cryptographic key management, wallet protection, and transaction monitoring as this is a new avenue for cyber-attacks and architectural vulnerabilities.
- Treasury and liquidity: Adapt risk frameworks for faster settlement and volatility management. Do this through enhanced liquidity monitoring, stress testing and liquidity management capabilities.
- Data and analytics: Enable real-time processing of transaction data, wallet-to-account mappings, token flows, and compliance logs in near real-time, supporting auditing, regulatory reporting, fraud detection, anti-money laundering / combating the financing of terrorism monitoring, and operational risk.
Who’s doing it now?
Why does it matter?
Early adopters of stablecoins, tokenised deposits and RWAs will differentiate services, unlock new revenue streams, and maintain relevance in a rapidly evolving ecosystem. Digital assets enable faster, programmable payments and improved client experiences, bringing competitive advantage.
Trend 9: Re-engineering the core
Re-engineering legacy cores into modular, modern platforms will remove complexity, enable integration, and support faster, safer and more scalable change across financial services.
What’s changing?
Financial institutions are shifting away from fragmented, outdated core platforms and moving towards modular, interoperable systems. Rather than replacing everything at once, the focus is on redesigning core systems to remove non-essential functions and realign with true product platforms. This reduces friction, enables faster feature releases, simplifies integration with partners, and supports analytics and automation at scale.
What’s driving it?
- Customer expectations: Increasing demand for faster product and service changes across retail, insurance, wealth and corporate services.
- Regulatory pressure: Greater expectations around transparency and resilience expose weaknesses in legacy engines.
- External ecosystems: Legacy systems are unable to support the API-driven connectivity needed for external partners and marketplaces.
- Cost pressure: Simplification is needed to reduce overlapping systems and retirement of high cost platforms.
- Talent shortage: Declining expertise in legacy mainframes (as they retire) coupled with difficulty in attracting and retaining engineering talent
- Operational risk: Created when outdated workflows and product engines drive manual interventions, inconsistent decisions and control failures.
- Vendor maturity: Modern vendor platforms are reducing delivery risk and making component replacement more predictable.
Where should firm’s focus?
To start the journey, financial institutions need to assess their current architectural limitations against their product and customer growth strategies and create a modernisation blueprint. To define the blueprint, they need to focus on:
- Domain architecture simplification: Rationalise legacy systems into fewer, well-defined platforms with clear ownership.
- Build-buy-outsource strategies: Build for differentiation, buy for commodity, outsource for specialist services.
- Integration and orchestration: Adopt API-first and event-driven connectivity for modular interoperability across domains.
- Engineering discipline: Strengthen automation, refactoring patterns and coexistence strategies to minimise operational disruption.
Who’s doing it now?
Why does it matter?
Modernisation enables faster delivery, lower cost of ownership, improved transparency, and stronger resilience. It unlocks innovation, accelerates regulatory response, and maximises returns on data, analytics, and AI investments – and ultimately drives better outcomes for your customers and employees.
Trend 10: Agentic AI
It’s time to shift from isolated pilots to scaled adoption, using agentic AI, to transform how financial institutions operate, make decisions, and create value at an enterprise scale.
What’s changing?
Financial services AI adoption has grown rapidly, but progress remains in isolated pilots and narrow use cases. Fragmented data, inconsistent controls, and legacy tech have limited enterprise integration. Now, expectations are rising: leaders want agentic AI that can coordinate tasks, act, and optimise workflows - not just generate insights. We are seeing improvements in standardisation and interoperability, with the introduction of patterns like model context protocol (MCP). Processes are being redesigned so AI outputs drive downstream work, while regulators’ focus on explainability and governance, accelerating enterprise-wide deployment.
What’s driving it?
- Demand for business value: Executives want measurable operational efficiency, customer improvements, and risk gain - not isolated experiments. Return on investment is critical.
- Agentic AI maturity: Multi-step, action-oriented AI are rapidly evolving and will require orchestrated workflows, trusted data, and strong controls to deploy at scale safely.
- Operational pressure: Rising costs and talent constraints push automation across entire processes.
- Technology readiness: Modern data platforms, model registries, API-first architectures and event-driven patterns finally make enterprise-grade AI integration feasible
- Market competition: Early movers are scaling AI to differentiate on speed, service, and cost.
What should firms do?
To shift from isolated pilots to mature, enterprise-wide AI, firms need to establish:
- Commercial alignment: Prioritise use cases that deliver enterprise value, reduce cost to serve and scale horizontally over function-specific experiments.
- Shared AI platforms: Consolidate data pipelines, model registries, deployment tools, and observability into a single governance environment.
- Robust governance and security: Define ownership for model performance, monitoring, escalation paths and ensure robust security and understanding of technology supply chains and protocols.
- End-to-End integration: Enable API-driven patterns so AI outputs trigger downstream processes across operations, servicing, risk, payments and product decisions.
- Operational readiness: Clarify roles and train teams for oversight, challenge, and optimisation of AI-enabled processes safely.
Who’s doing it now?
Why does it matter?
Industrialised AI reduces manual workload, strengthens controls, accelerates cycle times, and enables smaller, more agile teams. Firms that scale AI safely and repeatedly across high-volume processes will achieve a step-change productivity and resilience. Those stuck in pilots will face rising costs, constrained capacity, and competitive disadvantage.
Want to dive deeper into the trends and discover how to stay ahead? Get in touch
Special thanks to Beth Jenkinson, Phoebe Choi, Ioana Topoliceanu, and Craig Renwick for their insights and contributions.
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