The key IT trends shaping financial services software in 2026 are the move from AI experimentation toward governed deployment, modernization of legacy systems and data, stronger cybersecurity and operational resilience, and continued investment in payments, fraud prevention, and digital customer experience. These priorities are connected: AI and customer-facing services depend on reliable data and integration, while security, governance, and resilience constrain how quickly institutions can change.
What the 2026 evidence says—and what it does not
The figures below come from different surveys and institutions, so they are snapshots of their stated respondents, not a single global forecast. Finastra’s 2026 State of the Nation survey was conducted in November 2025 among 1,509 managers and executives across 11 regions. KPMG’s global technology survey summary covers 760 financial-services technology leaders; its separate banking technology survey is US-oriented. The World Economic Forum and regulators provide policy and risk perspectives rather than comparable adoption surveys.
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That distinction matters: a respondent planning an investment is not proof that the investment happened, and an institution using, piloting, or planning AI is not necessarily running it in production. The evidence supports a picture of priorities and execution pressures, not a measured sector-wide outcome.
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AI is now a broad strategic priority, but adoption figures need careful reading. Finastra reports that 96% of its respondents were actively using, piloting, or planning to use AI in 2026, while 2% said they were not using it. The same survey says 61% had improved their AI capabilities over the prior year. The 96% combines current use with pilots and plans; it should not be read as a production-deployment rate.
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Finastra describes institutions’ focus as scaling AI responsibly, securely, and profitably across areas including payments, lending, compliance, and customer engagement. The practical shift is therefore not just acquiring models or applications. Institutions need to decide which workflows merit automation or assistance, how staff remain accountable, and how systems connect to the data and controls already in place.
Governance must cover the AI lifecycle
The Financial Stability Board’s 10 June 2026 consultation report proposes 12 sound practices for organisation-wide AI governance and management across the AI lifecycle. These are proposed practices in a consultation report, not binding rules. They nevertheless point to the breadth of the work: institutions need governance that reaches from use-case selection and data to deployment, monitoring, and change management.
The World Economic Forum’s AI Playbook for Financial Services, published on 24 June 2026, draws on more than 150 senior leaders across 100 institutions. It addresses strategy, governance, workforce transformation, data foundations, agentic systems, and case studies. Its scope reflects a central implementation reality: introducing generative or agentic AI is an operating-model decision as well as a software decision.
Integration and data readiness are deployment constraints
KPMG’s 2026 banking survey identifies integration with existing systems and processes as the top stated barrier to AI deployment, followed by regulatory, legal, and ethical uncertainty, then poor data quality or readiness. A model that performs well in isolation may still be difficult to use safely in a bank’s workflows if customer records, permissions, controls, and downstream systems do not line up.
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For technology leaders, this makes use-case selection and integration design inseparable. Before scaling a pilot, establish what data it can access, how outputs enter a business process, who can override or approve them, and how performance and incidents will be monitored.
2. Modernization and data foundations enable the rest
Modernization remains a strategic priority because it supports AI, resilience, and customer experience—not because every institution should replace its core systems at once. Finastra reports that 87% of its survey respondents planned to invest in modernization over the next 12 months, and 29% prioritized cloud adoption. Those are stated plans and priorities from its November 2025 survey, not evidence that the investments were completed.
KPMG’s global financial-services summary identifies data, cybersecurity, infrastructure, skills, and governance as foundations for durable technology value. It also points to technical debt and talent shortages as constraints on execution. More than half of respondents said foundational technologies such as data, cloud, and cybersecurity deliver the majority of digital value; KPMG’s summary does not give an exact percentage for that statement.
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For a financial institution, modernization can include data architecture, legacy transformation, cloud adoption, and the integration layers that connect new services to existing systems. The right scope depends on the institution’s current architecture, risk appetite, regulatory obligations, and capacity to operate change. The cited surveys do not prescribe one target architecture for every bank.
A useful investment test is whether a proposed change removes a specific constraint: for example, fragmented data that impedes analytics, brittle interfaces that make AI or payments changes difficult, or infrastructure that cannot meet resilience requirements. Buying a new platform without addressing those dependencies can move the constraint rather than remove it.
Partnership is one modernization route, not a universal answer
Finastra reports that 54% of respondents use partnerships with fintech providers as their default modernization approach. That finding describes respondents’ approach; it does not establish that partnerships are always faster, safer, or less costly than internal development. Any partner choice adds integration, oversight, and third-party risk questions alongside potential capability or delivery benefits.
Whether an institution modernizes in place, replaces a platform, or works with a fintech provider, leaders should assess the same essentials: integration burden, data quality, security and resilience, governance fit, support for AI and payments use cases, customer impact, and the skills and operating capacity required to deliver and maintain the change.
3. Cybersecurity, resilience, and third-party risk are design requirements
Financial-services software must be evaluated not only for functionality but also for how it behaves under attack, disruption, and supplier failure. The Federal Reserve’s Cybersecurity and Financial System Resilience Report describes its cyber-risk policies and procedures, related activities, and current and emerging threats including malware and supply-chain risks. The report index was last updated on 4 September 2026.
In the European Union, the European Banking Authority’s June 2026 Risk Assessment Report states that it had received more than 18,000 DORA-related incident reports through March 2026. This is an EU reporting figure for that period, not a global incident count. The EBA also discusses system failures, cyber incidents, third-party dependencies, and AI-associated risks.
These risks intersect with modernization choices. Cloud services, fintech connections, AI systems, and payments infrastructure can create dependencies beyond an institution’s direct control. Architecture and procurement reviews should therefore address access controls, data handling, incident response, supplier concentration and continuity, and how the institution can maintain critical services when a dependency fails.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.4. Payments, fraud prevention, and digital experience remain investment priorities
AI and modernization do not displace the everyday demands placed on financial-services software. KPMG’s 2026 Banking Technology Survey lists data-driven insights, cybersecurity, fraud prevention, digital customer experience, enterprise transformation, and data modernization among banking investment priorities. Its overview also identifies payments as an area of pressure and future capability development.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThese capabilities are linked in practice. Better data can support customer insight and fraud detection; payments changes can affect customer journeys and operational controls; and digital experiences depend on reliable services behind the interface. Treating these as disconnected projects can leave institutions with overlapping platforms or gaps between customer-facing applications and the systems that process transactions.
Finastra also identifies customer service and personalized experiences as prominent customer demands. Its survey finding helps explain why modernization is often framed around more than infrastructure: the intended outcome is a service that works reliably and can respond to customer needs, while still meeting security and compliance requirements.
5. Security investment is rising as a stated expectation
Finastra’s 2026 survey page projects a 40% increase in security investment in 2026. This is a projection reported by the survey, not a measured increase in realized spending. Read alongside the Federal Reserve and EBA materials, it signals that security and resilience are being treated as core requirements for technology change, rather than a later layer to add after deployment.
For investment planning, the question is not simply how much to spend but whether the spending addresses concrete exposures: cyber-risk management, malware, supply-chain threats, operational continuity, and third-party dependencies. The sources do not establish a single spending level or control set suitable for every institution.
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How to turn the trends into an investment sequence
Because these priorities depend on one another, leaders can use the following sequence to make proposals easier to evaluate. It is a decision framework, not a prescribed architecture or a ranking of vendors.
- Define the outcome. Specify the business or customer problem—such as a payments bottleneck, a fraud-control gap, or an AI-supported workflow—before choosing a platform or model.
- Map dependencies. Identify the relevant data, legacy systems, interfaces, cloud or supplier services, operational owners, and skills needed to deliver and support the change.
- Set controls before scale. Establish security, resilience, privacy, governance, human accountability, and monitoring requirements appropriate to the use case and applicable obligations.
- Test integration and readiness. Validate data quality and access, connections to existing processes, failure handling, and the path from software output to an authorized operational action.
- Stage delivery and review evidence. Use a bounded implementation to learn whether the change works in its actual environment, then reassess risks, operating capacity, customer impact, and measurable outcomes before expanding it.
The sequence helps avoid treating AI, modernization, security, payments, and customer experience as competing technology slogans. Each proposal should explain which constraint it resolves, what dependencies it introduces, and how the institution will know whether the change is working.
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