Open Banking is no longer simply a regulatory initiative or a digital transformation program. It has become one of the most important drivers of profitability and innovation across the global banking industry.
Consider the United Kingdom, where more than 16 million people now use Open Banking services, while Account-to-Account payments have surpassed one billion transactions, continuing to grow at more than 50% annually. At the same time, estimates suggest that the economic value generated by Open Banking could reach as much as £43 billion annually once the market reaches maturity.
These figures are no longer measuring the success of a technology project.
They are measuring the emergence of an entirely new business model.
The Paradox: The Conversation Has Moved Beyond APIs
Yet many institutions are still discussing how many APIs they have built, while the global conversation has moved toward a very different question:
How does data translate into profitability?
The Open Banking journey began in the aftermath of the global financial crisis, as regulators sought to increase competition and innovation across the banking sector.
A major turning point came with the introduction of the European PSD2 framework in 2015, followed by the implementation of Open Banking in the United Kingdom in 2018. This established a model in which customers could share access to their financial data—with their consent—through secure APIs with authorized third-party providers.
At that stage, success was largely measured by:
- The number of APIs deployed,
- The speed of integration with fintech companies, and
- Improvements in customer experience.
But that was only the beginning.
Between 2020 and 2025, the global conversation increasingly shifted toward Open Finance—a natural evolution that extends beyond traditional bank accounts into:
- Investments,
- Insurance,
- Savings,
- Mortgages,
- Pensions, and
- Digital wallets.
As customers gained a more integrated view of their financial lives, banks gained the ability to design more precise products, increase cross-selling, improve Customer Lifetime Value, and transform data into a direct source of growth and revenue.
From Open Banking to Open Finance: Lessons from Brazil and Singapore
Brazil moved early in this direction. Rather than treating Open Finance as a standalone initiative, it integrated the model with its instant payment infrastructure, Pix, creating one of the world’s most dynamic financial ecosystems.
The result has been faster innovation, lower payment costs, and greater opportunities for collaboration between banks and fintech companies.
Singapore has taken a different approach, focusing on building a digital economy around an open API economy.
In this model, the bank is no longer simply a provider of financial services. It becomes a digital platform capable of integrating with a broader ecosystem of government, commercial, and financial services.
Australia and the Rise of Open Data
In Australia, the Consumer Data Right (CDR) has demonstrated that the future of data sharing will not be confined to financial services.
It can extend across the wider economy, laying the foundation for the next stage of digital evolution.
Today, the world is already entering that third phase: Open Data.
In this model, financial data alone is no longer sufficient.
With customer consent and within appropriate governance frameworks, banks can potentially combine financial information with data from:
- E-commerce,
- Supply chains,
- Energy,
- Government sources,
- Sustainability and ESG data, and
- Internet of Things (IoT) ecosystems.
The objective is to build a much more comprehensive picture of individuals and businesses.
This is where the bank moves from responding to customer needs to anticipating them.
But even Open Data will not be the final destination.
The Next Frontier: Autonomous Finance
The next stage beginning to take shape within global financial institutions is Autonomous Finance.
Here, artificial intelligence moves beyond analyzing data to taking and executing financial actions through Agentic AI, within predefined permissions established by the customer and supported by appropriate human oversight and governance.
This fundamentally changes the strategic question facing bank executives.
Instead of asking:
How do we implement Open Banking?
The more important question becomes:
Does our bank have the digital architecture, governance, data capabilities, and AI infrastructure required to compete in the era of Autonomous Finance?
From Data Sharing to Revenue Creation
If the first generation of Open Banking focused primarily on sharing data, the next generation will focus on turning data into revenue.
And that changes the banking model itself.
Historically, banks generated most of their revenue through lending, deposits, and banking fees.
Today, data itself has become an economic asset that—when used with customer consent and within appropriate regulatory frameworks—can enable banks to:
- Deliver more personalized services,
- Accelerate decision-making,
- Improve risk assessment, and
- Develop new sources of revenue.
This is why leading financial institutions are increasingly viewing Open Banking not simply as a regulatory compliance initiative, but as a Revenue Platform.
At Lloyds Banking Group, for example, the group has outlined an AI strategy aimed at generating significant operational efficiencies while redesigning customer journeys and improving productivity. Other banks are similarly expanding investments in automation and intelligent analytics as direct drivers of profitability—not simply as tools for improving service.
At JPMorgan Chase, artificial intelligence has become increasingly embedded in day-to-day activities ranging from data analysis and risk management to decision support, helping accelerate processes and improve operational efficiency at scale.
Meanwhile, BBVA invested early in open data capabilities to develop more sophisticated credit models, reduce the time required to assess applications, and improve the quality of its lending portfolios.
The Biggest Impact May Be in Corporate Credit
Perhaps nowhere is this transformation more significant than in corporate lending.
For decades, banks have relied on financial statements, paper documentation, and site visits to assess businesses.
In the era of Open Data, however, banks can—with the customer’s consent—potentially combine:
- Cash-flow data,
- E-invoices,
- ERP systems,
- Supply-chain information, and
- Selected indicators of commercial activity
to build an almost real-time view of a company’s financial performance.
The credit analyst does not disappear. The role evolves.
Instead of spending days collecting and consolidating information, the analyst can focus on reviewing AI-generated analysis and making the final credit decision within the bank’s established policies and risk framework.
The result?
- Shorter credit decision cycles,
- Lower operating costs,
- More accurate risk assessment,
- Lower probability of default, and
- Improved return on capital.
Retail Banking Will Undergo the Same Transformation
Retail banking will be no less affected.
The next phase will not necessarily be a competition between banking apps.
It will be a competition between AI-driven intelligence engines.
A bank could analyze spending, saving, and financial obligation patterns and then recommend the most appropriate financing option, investment product, or credit card at the right moment—rather than relying primarily on traditional marketing campaigns.
Open Data and the Future of Green Finance
The combination of Open Data and artificial intelligence could also open new possibilities for sustainable finance.
Instead of relying solely on annual sustainability reports, banks can potentially connect financing decisions to continuously updated operational data, including:
- Energy consumption,
- Emissions indicators, and
- Supply-chain performance.
This can enable banks to measure environmental impact more continuously and accurately.
Institutions such as DBS Singapore have been integrating data and analytics into sustainable finance decision-making and the development of products linked to sustainability objectives.
But all of these models depend on one fundamental capability:
Data alone does not create value.
The Four Layers of Data-Driven Banking
Real value emerges when data moves through four interconnected layers:
1. Data Layer
Collecting data through Open Banking and Open Data.
2. Intelligence Layer
Analyzing that data using artificial intelligence and advanced analytics.
3. Decision Layer
Converting intelligence into actionable recommendations and decisions.
4. Agent Layer
Using Agentic AI to execute actions automatically within customer-defined boundaries, under appropriate banking governance and oversight.
This is the point at which data transformation becomes business transformation.
Governance Becomes a Strategic Asset
The more extensively banks share data and deploy AI, the more important data governance, customer consent management, cybersecurity, and Model Risk Management become.
These are no longer simply regulatory requirements.
They are strategic mechanisms for protecting trust.
The winners of the next phase of banking will therefore not necessarily be the institutions that possess the largest amount of data.
They will be the institutions that have:
- The strongest data governance,
- The highest-quality data, and
- The ability to turn data into decisions that generate genuine economic value.
Where Are Banks Heading Over the Next Decade?
If the past decade was defined by the transition from branches to digital applications, the next decade may bring an even deeper transformation:
From selling financial products to delivering financial decisions.
The customer may not even need to open a banking application.
Instead, they could interact with an Agentic AI capable of understanding their financial objectives, comparing alternatives, recommending the most appropriate financing, investment, or insurance options, and executing approved actions automatically within predefined permissions.
Meanwhile, the bank would remain responsible for:
- Governance,
- Compliance,
- Risk management, and
- Data protection.
In this model, Open Banking will no longer be the competitive advantage. It will become foundational infrastructure.
And Open Finance will not be the final destination, but rather a transition toward a data-driven economy in which the ability to integrate financial and non-financial data becomes a critical source of competitive advantage.
Open Data will give banks a more real-time view of economic activity, enabling better decisions, lower operating costs, and more sophisticated risk management.
The Competitive Landscape Is About to Change
The biggest transformation, however, will come with Autonomous Finance.
Artificial intelligence will no longer function simply as a tool for analyzing data. It will increasingly become an active participant in executing operational and financial decisions—while humans remain accountable for governance, oversight, and ultimate decision-making.
And this is where I believe the competitive landscape will fundamentally change.
The question will no longer be:
Who has the largest branch network?
Nor even:
Who has the best banking application?
The questions will become:
Who has the strongest data platform?
Who has the highest-quality data?
And who can transform that data into faster decisions, better customer experiences, and higher profitability?
This is why the real investment opportunity is no longer technology alone.
It lies in digital infrastructure, data governance, cybersecurity, and responsible artificial intelligence.
These capabilities are no longer simply operating costs.
They are strategic assets that increasingly determine a bank’s market value and its ability to compete.
The Future Belongs to the Banks That Can Turn Data Into Value
Perhaps this is why the banks that lead the market over the next decade will not necessarily be the largest institutions.
They will be the ones most capable of transforming:
Data into intelligence.
Intelligence into decisions.
And decisions into sustainable economic value.
The future of banking will not simply be open. It will be intelligent, data-driven, and increasingly autonomous.
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