Using AI agents to maximize Open Banking value

The real opportunity in Open Banking

Open Banking is reshaping how financial institutions share data and connect with third-party apps. Most of the attention has been on setting up Open Banking APIs, but the real value comes from operationalizing, monitoring, monetizing, and gaining insights from these data flows.

This is where AI comes into play, helping financial institutions get the most out of their Open Banking investments.

Producers versus aggregators
In Open Banking, there are two main players:

• Producers: financial institutions that expose APIs
• Aggregators: Third parties that use those APIs

This article focuses on how the Producers can deploy AI agents/assistants to maximize the value of their open banking APIs.

Many financial institutions see Open Banking only as a compliance burden. But when you combine open banking with AI, it becomes a powerful business opportunity.

Open Banking business capabilities that can leverage AI

With the right setup, AI agents can deliver major benefits:

• Data quality management: Catch validation errors and data anomalies early.
• Lineage visualization: Track the journey of data across systems.
• Compliance monitoring: Ensure data sharing meets regulations.
• API performance optimization: Find and fix bottlenecks.
• Partner intelligence: Understand how partners are using your APIs.
• Monetization: Identify valuable data worth offering as premium services.
• Real-time issue response: Quickly find and fix problems.

To support these use cases for API producers, AI needs a semantic understanding of APIs and data. Doing this based on coded APIs is nearly impossible. A metadata-driven API infrastructure that provides LLMs with rich, structured, and contextual information about the APIs and the data they deliver, makes it all possible.

Solutions like Hasura Data Delivery Network (DDN) and the open source RouteForge plugin help enable this approach. It’s not about the specific tool you choose – it’s about adopting metadata-driven principles to enable flexible, standards-compliant data sharing (such as FDX schemas).

The most advanced solutions do more than transform data. They also support:

• Governance
• Lineage tracking
• Transparency across complex data flows

This semantic metadata lays the foundation for AI data agents like PromptQL to deliver powerful capabilities.

Imagine you're a data operations analyst facing API validation errors in your FDX endpoints just before an important meeting. Instead of spending hours digging through logs or consulting multiple teams, you ask your data agent (PromptQL):

"Find FDX API validation errors and show the API and error details."

The agent, using AI reasoning, scans the metadata in your semantic layer. It navigates complex relationships, using business metadata and inferred logic to find patterns.

It quickly identifies four validation errors, such as:

• Negative values where positive ones were expected
• Missing required fields in API responses

When asked for lineage information, the agent:

• Extracts key points ("leaf nodes") from data paths
• Pinpoints which transformations are working or failing
• Traces errors back to either source systems or transformation logic

For instance, the agent finds a transformation incorrectly flipping positive values to negative – an error with major impacts on financial calculations. This is a common issue in financial data ecosystems, often arising when one system expects a debit/credit attribute paired with an absolute value, while another system expects a signed value. Without visibility into these different conventions across systems, these discrepancies can propagate through the entire data pipeline and affect everything from balance calculations to regulatory reporting.

The best part: The agent uncovers all this without needing you to write complex queries or fully understand the data structure. Thanks to the metadata-driven design, the agent easily connects business rules, data flows, and transformations to surface clear, actionable insights.

With this understanding, you can walk into meetings ready with both the problems and solutions – saving hours of manual investigation.

The impact is huge: What once took days now takes minutes. Even better, the semantic layer creates a shared "language" across teams. This language – spanning business and technical domains – breaks down silos and unlocks faster collaboration, deeper insights, and new monetization opportunities.

You can see an example of this interaction, built with PromptQL, right here.

AI agents deliver value across multiple teams for API producers:

• Chief Data Officers: Gain real-time visibility into how data assets are used, spot quality issues early, and prioritize future investments beyond just compliance needs.
• Compliance teams: Easily monitor regulatory adherence across jurisdictions by tracking data lineage and transformation, ensuring sensitive data is properly handled.
• Product and Partnership teams: Analyze partner API usage patterns to guide product strategy, such as developing premium tiers for high-demand data elements.
• Revenue and business development: Identify new monetization opportunities by studying usage trends, partner behavior, and market gaps.
• Executive leadership: Access high-level metrics that showcase the broader business impact of Open Banking, supporting investment decisions and strategic planning.

• Build a metadata-driven infrastructure: Choose platforms that capture and publish rich metadata, including governance and lineage, to power your AI agents. Metadata is the fuel for your intelligence layer – the better your metadata, the more powerful your data agents become.
• Ensure 100% reliability: Select AI tools with a proven track record for accuracy, explainability, and auditability – especially critical for regulatory compliance in open banking.
• Define domain-specific business rules: Collaborate with experts to embed Open Banking standards and business logic into your agents for better, more relevant insights.
• Start with high-value use cases: Focus first on scenarios like error detection or compliance monitoring to build early wins and momentum.
• Integrate into existing workflows: Embed data agents within tools your teams already use to encourage adoption without adding complexity.
• Create continuous feedback loops: As users interact with your data agents, autonomously capture feedback and continuously refine them over time, making them smarter and more valuable with each interaction.

Institutions that treat Open Banking as more than just a compliance project will unlock significant value. AI agents represent the next step – combining smart governance, operational efficiency, and revenue generation.

The key isn't any one tool. It's about:

• Building a strong metadata-driven API architecture
• Creating AI agents that truly understand and act on your data

The institutions that embrace this will not just survive – they'll lead the future of Open Banking.

Ready to get started?

Start free