FIDA and Open Finance: the end of the era of passive data

What you should know:

• FIDA compliance does not create advantage. Sharing data is the cost of entry into Open Finance. Value begins only when an institution can interpret that data and use it to inform business decisions.
• Data without context does not drive results. The experience of markets such as Brazil and Australia shows that infrastructure alone is not enough. Success depends on shared data meaning and a clearly designed usage scenario.
• Those who win decide faster and at lower cost. Open Finance shifts competition away from products toward interpretation, decision automation, and advisory capabilities that materially reduce customer effort and operational costs.

FIDA: compliance is a cost, value comes later

In discussions about the FIDA regulation, the same logical mistake appears repeatedly. Regulatory compliance is equated with real business value. Yet data sharing alone, even when mandatory, creates no competitive advantage. It is merely a ticket to play. A cost borne by all market participants, but one that does not generate margin.

The real Open Finance business begins where compliance ends. Winners will be the organizations that treat open data not as an obligation, but as an asset. In practice, this means focusing on three areas: deliberate use of data from other institutions, the quality of one’s own data interpretation, and effective advisory delivered through a coherent, low-effort customer experience (CX).

Although FIDA applies to the entire financial sector, this article focuses primarily on the insurance perspective, where the way data is used translates most directly into tangible competitive advantage.

What determines Open Finance success? Global lessons from wins and failures

Before looking at local advantages, it is worth examining markets that have already gone through the data-opening phase. Their experience is the most valuable lesson today.

Brazil: success through consistency

Brazil provides a strong example. Its Open Insurance model was designed not as an infrastructure exercise, but as a tool for real market competition. The critical factor was not data exposure through APIs, but agreement on a shared standard of data meaning. As a result, information flowing from different institutions became comparable, and the quality of coverage measurable and demonstrable in real time. Competition began to shift away from price toward actual customer value, supported by high-quality operational data.

Australia: a warning about lack of purpose

A very different conclusion emerges from the Australian experience. Despite significant technology investment and a correctly implemented infrastructure, the model did not translate into clear value for the end customer. Data sharing became an end in itself rather than a means to simplify processes, improve advice, or enable better decisions. Adoption remained low, and the solution effectively turned into a costly technical layer with no meaningful impact on customer behavior or business outcomes.

These two cases show that Open Finance starts creating value only when consistent information is combined with a concrete usage scenario. Without that, even the best-designed technology remains a regulatory obligation rather than a source of competitive advantage. Below, we move from observation to practice, showing how insurers can deliberately use Open Finance to build market advantage.

Using data from other institutions: a real strategy

True transformation begins when an insurance organization stops viewing Open Finance solely through the lens of its own API and starts actively consuming data shared by other market participants. This shift, from data exposure to conscious data consumption, opens the door to an entirely new operating model.

Actively acquiring context

Access to account balances, transaction histories, and customer liabilities held at other institutions enables insurers to proactively identify coverage gaps. Instead of waiting for customer initiative, systems can detect a property purchase or a lifestyle change handled by a competitor and offer appropriate protection at the right moment, in real time.

A credible source of truth

External data enables a more complete and up-to-date customer view without forcing customers to complete additional questionnaires or forms. This changes the institution’s role from a passive policy issuer to an active partner whose recommendations are based on facts and context rather than declarations.

Offer quality: from static products to dynamic decisions

In a world of continuous data flows, static products no longer reflect a customer’s actual risk profile. Advantage accrues to organizations that can interpret data continuously and translate it into current offer decisions. In practice, this means shifting focus from product design to managing risk over time.

Risk assessment as a service (UaaS)

Access to up-to-date financial and asset data allows insurers to offer not only protection, but also ongoing assessment of a customer’s risk profile. This assessment can be used internally to automate underwriting decisions, or externally as part of purchasing processes in other industries. In both cases, the result is faster decisions, lower operating costs, and earlier entry into the customer decision moment.

Hyper-personalization

In a model based on continuous data interpretation, coverage scope can be adjusted to actual financial and life events rather than periodic declarations. This reduces excess coverage where it is not needed and strengthens protection in areas of real risk. The outcome is better cost-to-risk alignment and greater portfolio predictability.

Effective advisory and CX: where loyalty is really built

In the Open Finance world, loyalty is no longer driven by relationships or contact frequency, but by how advisory reaches the customer at critical moments. Excellent experience primarily means minimizing customer effort and consistently building trust in institutional decisions.

Reducing operational friction

Speed becomes the new service standard. Customers assume the system already has the necessary financial and asset context, without repeated data collection. Advisory shifts from reactive to proactive support, triggered precisely when a customer’s life situation changes. For financial institutions, this translates into shorter decision cycles, lower operational load, and higher acceptance rates for recommendations.

Trust dashboards

Consent and permission management panels formalize a relationship built on transparency and control. Customers can see what data is used and for what purpose, while the benefit of sharing data becomes immediate and measurable, for example through faster claims settlement or simplified decision processes. This model strengthens relationship durability and reduces susceptibility to competitive offers.

Foundation: operational consistency

Delivering this vision requires a solid foundation built in the 2026–2027 timeframe. Without it, organizations risk operational paralysis when FIDA becomes fully effective. A key element of this foundation is the so-called digital nervous system, a layer that ensures consistency and timeliness of information used in business decisions.

The consistency layer

Rather than modifying heavy legacy core systems, a more effective approach is implementing a flexible intermediary layer that organizes data from internal and external sources in real time. This layer ensures analytics, automated decisions, and advisory are based on complete and current information rather than fragmented or delayed data. In practice, it becomes the point where regulation, technology, and real business processes intersect.

The 2028 horizon

Current FIDA timelines indicate that Phase I may begin in the first half of 2028. This leaves approximately 24 months to build a consistency layer that not only meets regulatory requirements, but enables institutions to use external data and their own analytical intelligence as a source of value. Without it, data sharing remains a cost. With it, data becomes a monetization tool.

Conclusion: the active node strategy

FIDA marks the end of a model in which insurance operates as a set of isolated product factories. Experience from markets that have already gone through this transition is clear. Advantage belongs to organizations that can combine data from multiple sources with their own analytics and translate it into decisions that require no effort from the customer.

Data sharing itself remains the cost of entry into a new ecosystem. Real value emerges only when institutions can combine external data with their own interpretation, offer quality, and effective advisory. In this context, 2026 becomes a point of choice: remain a passive data provider, or build the position of an active node that genuinely participates in value creation within the Open Finance ecosystem.