Operational resilience shifted from obligation to execution risk

Operational resilience in financial services entered a different phase once DORA came into force. Regulatory alignment stopped being a differentiator and became a baseline requirement that most large institutions were able to meet. By 2026, however, the most severe failures no longer originate from missing policies, incomplete registers, or gaps in formal reporting. They emerge from day-to-day execution inside operating environments that have become structurally complex, highly interdependent, and difficult to coordinate under pressure.

In practice, this means that formal compliance is no longer a reliable predictor of whether an institution can sustain critical services during disruption.

Banks and insurers operate on layered architectures built over decades, where core systems coexist with cloud platforms, external service providers, and AI-driven components embedded directly into critical business processes. Each layer is governed independently, often by different teams with separate objectives, funding models, and accountability structures. Under stable conditions this fragmentation remains largely invisible. During disruption, it determines how quickly an organisation recognises impact, assigns responsibility, and restores services that matter to customers, markets, and supervisors.

This dynamic explains why institutions that formally comply with DORA still experience service instability, prolonged incident resolution, and repeated supervisory scrutiny. The problem does not sit in regulatory interpretation. It sits in how work is organised, how ownership is distributed, and how decision authority is exercised when systems are under stress.

DORA operational resilience and the execution gap

DORA strengthened expectations around ICT risk management, incident reporting, and third-party oversight, which led most organisations to expand their governance frameworks. Documentation increased, registers multiplied, and control evidence improved across the board. These changes made institutions more auditable and more defensible from a regulatory standpoint.

They did not make them materially easier to operate during disruption.

Operational resilience depends on coordination rather than documentation. It depends on how quickly organisations recognise service impact, how responsibility is assigned across organisational boundaries, and how decisions are made when information is incomplete and time pressure is high. These behaviours cannot be enforced through controls alone. They depend on operating models that define how IT, risk, compliance, and business teams interact when incidents cut across formal structures, as well as escalation paths that remain effective outside normal operating conditions.

In many financial institutions, resilience responsibilities remain distributed across functions, with each area fulfilling its formal mandate while no one owns the full end-to-end service lifecycle during an incident. Compliance remains intact, but execution degrades precisely at the moment when resilience is being tested.

Third-party dependency reshaped the risk profile of financial institutions

Technology ecosystems in financial services expanded faster than operating models were able to adapt. Core banking platforms, cloud infrastructure, data providers, AI services, and niche fintech integrations now form tightly coupled dependency networks that underpin critical customer and market-facing services.

Regulatory visibility into third-party risk improved significantly. Practical operational control did not.

Most organisations continue to assess vendors individually, without modelling how dependencies accumulate across services or how failures propagate through shared components. Recovery assumptions therefore remain optimistic, substitution paths remain largely theoretical, and contractual safeguards rarely translate into real operational flexibility once disruption occurs.

By 2026, third-party dependency directly influences service continuity, supervisory outcomes, and capital planning decisions. Treating it primarily as a procurement or compliance topic understates its structural impact on the operating model and obscures the fact that resilience increasingly depends on ecosystems rather than individual systems.

AI adoption introduced new resilience failure modes

AI systems moved rapidly into production environments across fraud prevention, credit assessment, customer operations, and internal analytics. These systems rely on continuous data flows, external services, and governance mechanisms that traditional resilience frameworks were not designed to manage.

As a result, failure patterns shifted. Issues no longer follow clear system outage scenarios. They surface as degraded decision quality, delayed detection of anomalies, or cascading downstream effects that are difficult to classify within existing incident and continuity models. Ownership often remains unclear, particularly where AI components sit between business decision-making and underlying technical infrastructure.

Without explicit integration into service ownership and recovery planning, AI increases operational exposure. Risk materialises not through technology malfunction alone, but through organisational unpreparedness to manage AI as a production dependency with real continuity and recovery obligations.

Operational resilience challenges in banks and insurers in 2026

Across banking and insurance environments, resilience weaknesses tend to concentrate in the same structural areas. These weaknesses typically surface during real incidents, regulatory examinations, and post-incident reviews, rather than during planning exercises or control assessments, precisely because they sit between organisational boundaries rather than within clearly defined functions.

They persist because they fall outside traditional compliance scopes while remaining central to day-to-day execution.

Key operational resilience failure patterns observed under DORA include:

• fragmented ownership of end-to-end critical services, where responsibility is split across internal teams and external providers without a single point of operational accountability
• incident response models that exist procedurally but fail to translate into executable decision-making under time pressure
• third-party oversight that is disconnected from service criticality and realistic recovery feasibility
• architectural decisions optimised for availability or cost efficiency without being tested against continuity and recovery constraints
• AI systems governed as isolated initiatives rather than treated as core operational components with explicit recovery ownership

Resilience must be designed into the operating model

Operational resilience cannot be reinforced after the fact. It requires deliberate design choices embedded into how organisations operate on a daily basis, long before disruption occurs.

Institutions that improve resilience align service ownership with real dependency chains, define decision authority for disruption scenarios in advance, and connect architectural decisions to recovery feasibility rather than theoretical availability targets. They treat AI capabilities and external services as first-class operational assets, subject to the same accountability expectations as core systems.

When resilience becomes part of the operating model, regulatory alignment tends to follow naturally. When it remains a compliance exercise layered on top of existing structures, operational risk continues to accumulate silently until it surfaces under stress.

FAQ: Operational resilience in financial services

What does DORA require for operational resilience in financial institutions?

DORA defines expectations around ICT risk management, incident reporting, and third-party oversight. Operational resilience extends beyond compliance and refers to the ability to sustain critical services during disruption, including clear ownership, effective decision-making, and recoverability under pressure.

Why do DORA-compliant banks still experience operational outages?

Compliance validates controls and documentation. Outages typically stem from fragmented ownership, unclear decision authority, and unmanaged dependency chains embedded in operating models rather than from regulatory gaps.

What are the biggest operational resilience risks for banks in 2026?

The largest risks arise from cumulative third-party dependency, unclear end-to-end service ownership, AI systems without defined recovery accountability, and incident response models that do not scale under real operating stress.

How does third-party risk affect operational resilience?

External providers are embedded into critical services. Without modelling cumulative dependency and recovery feasibility across providers, institutions remain exposed even when individual vendors appear compliant.

Who should own operational resilience in financial institutions?

Resilience requires shared accountability across business, technology, and