Open finance meets mortgages: the FCA sets out its vision – but the hard work starts now

Mortgage journeys often depend on consumers gathering documents, repeating information, explaining their circumstances and bridging the gaps between services that lack interoperability. Participants described the desired future state as "capture once, verify once, reuse safely across the ecosystem", a model where consumers provide information once, that information is verified at source or through a trusted infrastructure, and it can then be reused for appropriate purposes within the consumer's control.

Not all data is equal: for firms to rely on shared data in a mortgage journey, it needs to be accurate, current, standardised and capable of being recognised as evidence, a concept participants described as "decision-grade data". Participants repeatedly called for common data models, minimum requirements, data provenance, auditability and trust marks, identifying these as the key building blocks of change.

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Rental payment history was frequently cited by sprint teams who argued for the value of recognising verified rental data as affordability evidence. Participants argued that consumers who have demonstrated reliable payment of rent, but who may otherwise have irregular income, should not necessarily be at a disadvantage because firms interpret the same evidence in different ways, and that consistent recognition of such evidence across the market may require mandation. Equally, savings behaviour can demonstrate financial readiness for a mortgage, whilst investments, pensions, savings, property data and HMRC history are all important indicators of suitability for later-life financial planning.

Participants strongly argued that many of the identified benefits depend on broad participation by key organisations and would be difficult to realise if adoption remained uneven, with gaps in participation seen as undermining confidence in common approaches. Participants discussed mandatory and premium datasets as a range of possible requirements rather than a single policy choice, covering regulatory intervention, legislation or other mechanisms to require key organisations to participate, make specified data available, or follow common standards.

Open finance-enabled mortgage journeys can involve many different organisations, including data holders, wallet providers, aggregators, brokers, advisers, lenders, public bodies, technology providers and AI tools,  and participants warned that, without clear roles and responsibilities, consumers could be left navigating a fragmented redress landscape and be unsure who to approach when something goes wrong.  Participants repeatedly raised four accountability questions:

• who is responsible if the original data is wrong;
• who is responsible if that data is transformed incorrectly as it moves between systems;
• who is responsible if an automated tool produces an unsuitable output; and
• who is responsible if several providers each contribute to a decision that is later found to be incorrect.

Some open finance-enabled tools may help consumers understand their position or present relevant evidence, while others could influence financial decisions more directly, moving closer to regulated advice.  Participants felt that greater clarity on where this boundary sits would help firms understand their responsibilities whilst still leaving room for innovation and protecting consumers. The role of AI in the advice arena was recently addressed by the Mills Review (published on 6 July).

Consumers need to know what is being shared, with whom, why, for how long, whether onward sharing is allowed, whether data can be corrected, and what happens when the journey ends, with teams proposing consent dashboards, point-of-use consent, plain-English explanations, downloadable receipts and audit histories.  Participants were particularly concerned about consent scope creep, the risk that consent initially given for a specific mortgage-related purpose could gradually expand in practice so that data is reused in ways the consumer may not have expected or meaningfully agreed to, including for profiling, pricing or marketing.

Automated tools could reinforce existing barriers where data acts as a proxy for protected characteristics, or where consumers cannot confidently use a digital wallet or navigate an AI-led digital journey.  For later-life decisions in particular, participants suggested stronger protections, including simple and straightforward explanations, human support, cooling-off periods, independent review, checks for vulnerability, capacity and coercion, and clear accountability across advisers, providers and platforms.

Open finance could enable more personalised tools, assessments and support, particularly where current processes do not adequately recognise a consumer's circumstances, but greater personalisation is not an outcome in itself.  Participants stressed that the success of open finance-enabled mortgage services should be judged by consumer outcomes, whether consumers understand how their data is used, receive timely, relevant, comprehensible and accessible support, and can challenge negative outcomes.

Participants highlighted a challenge in which data holders may bear the costs of creating and maintaining data access, meeting common standards and managing liability, whilst some of the resulting value may be realised elsewhere in the ecosystem.  The proposals point towards a layered commercial model in which common standards and a minimum mortgage-relevant dataset would support consistent access, whilst firms compete through enhanced services and analytics, with commercial incentives, participation requirements and consumer outcomes needing to be considered together.

The insights generated through the policy sprint will contribute to a discussion paper on the key enablers for open finance.  The Smart Data Accelerator's next phase of work will look at the data-sharing architecture, roles and responsibilities, data journeys, the key components of infrastructure and trust such as identity and verification, authorisation and certification, and the interdependencies between agentic AI and open finance.