Governance Challenges in Open Finance by June 2026

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Governance gaps hinder Open Finance development

  • AI is being deployed across Open Finance supply chains faster than “holistic governance” is being discussed by banks and their partners.
  • In Europe, Open Finance runs horizontally while regulators still tend to govern by verticals; with FiDA stalled, debate has turned to whether GDPR can cover the gap.
  • In Canada, FDATA proposed a tiered “Sponsored Fintech Model” to prevent accreditation costs from excluding smaller players—raising new questions about continuous oversight.
  • In the UK Smart Data debate, experts argued adoption is blocked less by “trust” and more by missing consumer-facing infrastructure: verification, off-ramps, and recourse.

AI Risks Across Open Finance

  • At a June 2026 summit, senior executives from ING, UBS, Barclays, and Standard Bank acknowledged that AI experimentation and deployment are moving faster than any “holistic governance” conversation across the ecosystem.
  • The risk-management implication raised in that discussion was supply-chain specific: when AI is embedded in intermediaries, fintechs, and technology service providers, incidents propagate across the Open Finance chain—not just within one institution.
  • A practical takeaway for risk owners: treat partner AI as part of your operational perimeter for governance, monitoring, and incident response (because the customer impact rarely respects organizational boundaries).

What are the key governance challenges in Open Finance as of June 2026?

By then, the defining governance challenge in Open Finance is simple: the ecosystem is scaling faster than the rules, roles, and controls that make it safe. At an industry summit with senior executives from ING, UBS, Barclays and Standard Bank, the admission was blunt—there is no holistic governance conversation happening at the pace AI is being deployed. That gap matters most not inside a single bank, but across the chain of intermediaries, fintechs, and technology service providers that power Open Finance.

The practical implication is that risk is now “network-shaped.” In practice, that means governance, monitoring, and incident response have to work across multiple connected participants (banks, fintechs, aggregators, and service providers), not just within a single institution. A bank may outsource data connectivity, analytics, or decisioning to third parties, but it cannot outsource accountability when something fails. As one industry framing put it: a partner’s AI risk becomes the bank’s risk; a partner’s incident becomes the bank’s incident. (This framing is drawn from Louise Beaumont’s June 2026 recap for Open Banking Expo.) This is governance pressure created by interdependence.

A second challenge is third-party and supply-chain oversight. Open Finance relies on APIs, cloud infrastructure, and specialized providers, which amplifies concentration and resilience risks. Traditional supervision and internal risk programs struggle when critical controls sit outside the institution’s perimeter.

Finally, governance is being forced to operationalize, not just document. The access question—how to get data flowing—is increasingly solvable. The harder question is what happens when something breaks inside the pipe: who detects it, who reports it, who compensates the consumer, and who is liable across multiple participants.

Governance Ownership and Risk Scan
Use this quick scan to categorize (and assign owners for) the June 2026 governance problem set:
1) Network-shaped risk (interdependence)

  • What changes when services are delivered by a chain (bank → aggregator → fintech → cloud/AI provider)?
  • Where do you need shared monitoring and shared incident playbooks (not just internal policies)?

2) Third-party oversight that works in production

  • Beyond onboarding: what is continuously monitored (security posture, model changes, outages, complaint spikes)?
  • What triggers intervention (rate limits, suspension, step-up authentication, forced re-consent)?

3) Operationalized governance (accountability you can execute)

  • Can you answer, quickly and consistently: who detects, who reports, who fixes, who compensates?
  • Are those answers the same across participants—or do they break at organizational boundaries?

How are regulators in Europe responding to the rapid deployment of Open Finance?

Europe’s regulatory response in mid-2026 is characterized by a mismatch between how oversight is structured and how Open Finance behaves. Regulators tend to govern in verticals—banking, payments, data protection—while Open Finance “runs horizontally,” cutting across sectors and business models through shared data, shared infrastructure, and shared dependencies.

That tension was visible in a European panel discussion featuring figures including Agnieszka Scott (UK Department for Business and Trade, Smart Data), Michael Salmony, and Todd Clyde (Token.io). With the EU’s Financial Data Access framework (FiDA) stalling, the debate has shifted to whether existing rules—especially GDPR—can fill the governance gap. The underlying point was less about legal theory and more about timing: firms pulling ahead are not waiting for regulation to catch up.

Instead, leading players are leaning on risk-management infrastructure now—controls that can accredit participants, monitor risk, and support incident handling across a network. This is a pragmatic response to regulatory uncertainty: build governance capabilities that work even when the rulebook is incomplete.

Europe is also operating in a wider context where operational resilience and incident reporting are becoming more formalized. The EU’s Digital Operational Resilience Act (DORA) is shaping expectations around ICT incident reporting and supply-chain visibility, reinforcing the idea that regulators increasingly care about disruptions that originate in third parties, not just within a bank’s own systems.

The net effect: Europe is moving, but unevenly—stronger on resilience mechanics, less settled on a unified Open Finance governance model.

Europe’s Open Finance Mismatch
A useful way to read the European “vertical vs horizontal” mismatch:

  • FiDA (stalled in mid-2026): intended to be a more explicit Open Finance-style framework for broader financial data access.
  • GDPR: strong on data protection principles, but it doesn’t, by itself, specify end-to-end Open Finance operating rules (like accreditation, shared incident handling, or cross-party liability).
  • DORA: pushes the ecosystem toward operational resilience (including third-party ICT risk and incident reporting), which is why many firms are building network-level controls even while Open Finance-specific rules evolve.

This aligns with a broader supervisory theme noted in the OECD’s January 2026 paper on AI in finance: as institutions rely more on external technology and AI providers, the institution’s risk profile becomes inseparable from its partners’ controls, making third-party oversight and cross-entity coordination central to supervision.

What proposal did FDATA present to support smaller fintechs in Canada?

On 8 June 2026, FDATA submitted a formal proposal to Canada’s Department of Finance and the Bank of Canada recommending a tiered participation structure under the Consumer-Driven Banking Act. The core idea was a “Sponsored Fintech Model,” designed to address a practical barrier: full accreditation costs could shut smaller fintechs out of Canada’s consumer-driven banking framework.

In other words, the proposal tries to prevent Open Finance from becoming a club only large incumbents and well-funded firms can afford to join. A tiered model would allow smaller players to participate under the umbrella of an accredited entity—often an aggregator—rather than bearing the full compliance and accreditation burden themselves.

But the proposal also sharpens a difficult governance question: what does it mean for an accredited sponsor to vouch, continuously, for every entity sponsored beneath it? Sponsorship is not a one-time endorsement; it implies ongoing oversight, monitoring, and the ability to intervene when risk rises. That creates a new layer of operational responsibility inside the ecosystem—one that must work in real time, not just at onboarding.

The model therefore trades one risk for another. It may broaden participation and competition, but it concentrates accountability in the sponsor, which must manage downstream conduct, security, and incident readiness. The governance challenge becomes designing sponsorship rules that are credible: clear obligations, continuous monitoring expectations, and a workable path for enforcement when a sponsored participant fails to meet standards.

What the Sponsored Fintech Model optimizes for Upside (why it helps) Cost / risk introduced (what must be governed) Practical “make it real” requirement
Lower barrier to entry for smaller fintechs More inclusion, more competition, less “accreditation as a moat” Uneven capability among sponsored firms can raise ecosystem risk Clear eligibility criteria for sponsorship (scope, data types, volumes)
Faster participation via an accredited sponsor Quicker time-to-market for niche products Accountability concentrates in the sponsor (continuous vouching) Continuous monitoring duties (not just onboarding checks)
Simplified compliance path for sponsored firms Reduces duplicated controls and audit burden Sponsor becomes a bottleneck and a single point of failure Intervention powers: suspend access, require remediation, force re-consent
Clearer network governance (if designed well) A visible “responsible party” for consumers and regulators Sponsor must manage downstream incidents and complaints Shared incident playbooks + defined handoffs across sponsor/sponsored parties

What barriers are hindering consumer adoption of Open Finance?

A UK Smart Data forum challenged one of the industry’s most repeated assumptions: that consumer “trust” is the main barrier to adoption. Four behavioural and consumer experts argued that consumers are not primarily asking for faith—they are asking for infrastructure.

What consumers want, according to the discussion, is verification, visible off-ramps, and somewhere to go when something goes wrong. That is a different diagnosis with different remedies. If the problem were trust alone, the fix might be branding, education, or reassurance. If the problem is infrastructure, the fix is product and governance design: make consent understandable, make participation reversible, and make recourse real.

“Verification” in this context means consumers need to be able to confirm who they are sharing data with and under what terms—before harm occurs. “Visible off-ramps” means the ability to stop sharing, revoke permissions, and exit a data-sharing relationship without friction or ambiguity. And “somewhere to go” means clear pathways for complaints, dispute resolution, and remediation when an error, fraud event, or service failure happens across multiple parties.

This framing also connects directly to the broader governance gap. Open Finance can make data flow; it is less mature at making accountability flow. When consumers cannot see how problems will be handled, adoption stalls—not because they distrust the concept, but because the system does not yet demonstrate reliable consumer outcomes.

Consumer Trust Essentials
If you’re designing (or assessing) an Open Finance consumer journey, these are the “infrastructure” items consumers implicitly look for:

  • Before sharing
  • Can the user verify who the recipient is (legal entity / brand / sponsor) and what data will be accessed?
  • Is consent specific (data type, purpose, duration) rather than a single blanket permission?
  • While sharing
  • Is there a simple “status view” showing active connections, last access time, and what’s being pulled?
  • Are there clear signals when something changes (new data category, new third party, new AI-driven feature)?
  • Stopping / exiting
  • Is revocation one or two steps, and does it take effect quickly?
  • Is there a visible off-ramp that doesn’t require contacting multiple parties to fully disconnect?
  • When something goes wrong
  • Is there one obvious place to report an issue—and does it route correctly across bank/fintech/aggregator?
  • Does the user get a reference number, timeline expectation, and a clear next update point?

How is liability being addressed in the context of Open Finance?

Liability is emerging as the hardest unresolved question in Open Finance because it sits at the intersection of technology, governance, and consumer protection. A UK roundtable chaired by Mike Hewitt of Breakwater Technology concluded that while the access question—getting data flowing—is being answered, the question of what happens when something breaks inside the pipe has barely been asked.

This is not an abstract concern. Open Finance ecosystems are built from multiple participants: banks, fintech apps, aggregators, and technology providers. When a failure occurs—an outage, a data error, a security incident, or an AI-driven decisioning problem—harm can be downstream and diffuse. Without clear liability mapping, each participant can argue the fault lies elsewhere, leaving consumers stuck in the middle.

The discussion also pointed to a looming cross-sector spillover: energy’s emerging consumer grid is about to inherit the same liability problem Open Banking has lived with for years. As Smart Data expands beyond financial accounts into other domains, the “who pays when it breaks” question becomes a shared infrastructure issue, not a niche banking dispute.

Industry messaging also emphasized that third-party incidents are not separable from bank risk. If a bank’s Open Finance chain is running AI experiments via partners, then incident response and accountability must extend across that chain. This is where infrastructure-layer approaches—accrediting network participants, monitoring risk in real time, and ensuring liability lands in the right place—are being positioned as practical tools to make liability enforceable rather than theoretical.

Incident Liability Response Flow
A practical way to “map liability” so it works during an incident (not just on paper):
1) Detect

  • Define who is responsible for detection at each layer (bank, aggregator, fintech, tech provider) and what signals count (outage, data mismatch, fraud spike, model drift).

2) Report (single front door, multiple handoffs)

  • Decide where the consumer reports first, and how that report is routed across parties.
  • Checkpoint: the consumer should not have to diagnose whether it’s a bank issue, an app issue, or an aggregator issue.

3) Triage (what broke, where, and who owns the fix)

  • Separate root cause owner (who fixes) from consumer outcome owner (who keeps the user whole).
  • Checkpoint: if triage takes longer than your promised update window, you need an interim status update mechanism.

4) Compensate / remediate (consumer outcome)

  • Pre-agree what “make it right” looks like for common failure modes (wrong balance, failed payment initiation, unauthorized access, bad AI decisioning).

5) Recover costs (between participants)

  • After the consumer outcome is resolved, execute the behind-the-scenes recovery path (chargeback-like flows, contractual indemnities, sponsor enforcement).
  • Checkpoint: if recovery is the only thing that’s clear, liability is not actually solved.

Open Finance Risk Management: Key Insights and Future Directions

The Importance of Governance in Open Finance

This period made the central lesson hard to ignore: the biggest Open Finance risk is not the API—it is the governance gap around the API. As AI experimentation accelerates across banks and their suppliers, “holistic governance” cannot remain a slow, internal policy exercise. In Open Finance, governance must be networked: shared standards, shared monitoring expectations, and shared incident playbooks that reflect how services are actually delivered.

Regulation is moving, but not always in a way that matches Open Finance’s horizontal reality. Europe’s FiDA uncertainty and the debate over whether GDPR can fill the gap illustrate a broader pattern: firms will not pause innovation while frameworks mature. That makes regulatory dialogue and practical risk infrastructure more important, not less—especially where operational resilience expectations are rising and third-party dependencies are deepening.

The Role of AI in Risk Management

AI is both a driver of new risk and a tool for managing it. The June discussions highlighted that AI deployment is outpacing governance conversations, particularly across intermediaries and service providers. At the same time, the broader risk-management direction points toward more real-time detection, monitoring, and auditability—capabilities that can be strengthened with AI, provided explainability and accountability are built in.

Building Resilience in Open Finance Ecosystems

Resilience is no longer just about preventing breaches; it is about handling failure well. Consumers want verification, off-ramps, and recourse—features that double as resilience mechanisms because they reduce harm when systems fail. Regulatory regimes focused on operational resilience and incident reporting reinforce this shift: resilience must include supply-chain visibility and coordinated response across participants.

The next phase of Open Finance governance will likely be defined by three pressures already visible: (1) tiered participation models that broaden access but concentrate oversight duties (as in Canada’s sponsored fintech proposal), (2) cross-sector expansion of Smart Data that imports unresolved liability questions into new domains, and (3) a race between AI deployment and the ability of institutions—and supervisors—to operationalize governance at the same speed.

Balancing Key Governance Tensions
What to watch next is less a single “risk” and more a set of tensions that governance has to resolve in practice:

  • Speed of AI deployment vs. speed of governance: experimentation scales quickly; accountability mechanisms usually don’t.
  • Openness (more participants) vs. enforceability: broader access increases innovation, but only works if standards and enforcement are credible.
  • Lower entry costs vs. concentrated accountability: tiered/sponsored models can include smaller firms, while shifting continuous oversight onto sponsors.
  • Data access solved vs. failure handling unsolved: getting data flowing is increasingly feasible; coordinated incident response and liability remain the harder infrastructure.

This perspective reflects a builder’s lens shaped by Martin Weidemann’s work across payments, fintech/insurtech, and regulated, multi-stakeholder digital transformation in Latin America—where third-party dependencies and incident handling are operational realities, not just policy topics.

This article reflects publicly available information and industry discussions as of June 2026. Regulatory frameworks and supervisory expectations may change quickly as Open Finance expands into broader Smart Data use cases. Obligations and terminology can vary by jurisdiction and by a firm’s role in the ecosystem, so some details may be uncertain and subject to update.

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