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Global financial institutions including SWIFT, UBS, Euroclear and more than 20 major market participants have tested Chainlink-based infrastructure to address one of the biggest barriers to AI adoption in finance: hallucinated or inaccurate data.
The initiative targets corporate actions, a notoriously fragmented and error-prone segment of capital markets that costs the industry over $58 billion annually due to reconciliation failures, incorrect data, and operational inefficiencies.
LLM hallucinations are a massive roadblock to enterprise adoption of AI.
Swift, UBS, Euroclear, & 20+ major organizations advanced a solution to the $58B+ annual corporate actions problem by leveraging Chainlink to reduce AI hallucination risk.
LINK everything. pic.twitter.com/I2OAobDadO
— Chainlink (@chainlink) April 8, 2026
The project marks one of the first large-scale efforts to combine AI, blockchain and oracle infrastructure to produce verifiable financial data usable across global institutions.
Corporate actions, including dividends, stock splits, mergers and bond redemptions, are still handled using unstructured PDFs, emails and fragmented vendor feeds across custodians, brokers and asset managers.
That fragmentation creates both manual reconciliation costs and data accuracy risks, which become significantly more dangerous when AI models are introduced.
Also, it remains one of the most costly operational challenges in capital markets. Data from the Depository Trust & Clearing Corporation shows the industry spends roughly $58 billion each year handling corporate-action workflows, reflecting the scale of reconciliation, verification and operational overhead involved.
The complexity of each event is also significant. Citi’s 2025 asset-servicing analysis found a typical corporate action can trigger over 100,000 interactions across financial institutions, from custodians and asset managers to brokers and infrastructure providers. Processing a single event can cost firms tens of millions of dollars, according to the report.
Industry research shows:
Large language models worsen the issue. Financial AI systems can generate confident but incorrect data, creating compliance, settlement and regulatory risks, particularly in high-value markets like securities servicing and asset management.
This makes hallucination risk one of the biggest barriers preventing banks from fully adopting generative AI.
The SWIFT-Chainlink initiative introduced a multi-layer verification model designed to reduce hallucination risk and create trusted financial data.
AI models first extract corporate-action information from unstructured documents. Multiple AI outputs are then compared to create consensus, rather than relying on a single model. Chainlink’s oracle infrastructure aggregates these results and produces a ‘golden record,’ a verified dataset that institutions can rely on.
This verified data is then distributed across banks, custodians and infrastructure providers using standardized financial messaging.
During testing, the system achieved nearly 100% data consensus across AI models, significantly reducing hallucination risk and improving reliability for institutional workflows.
The architecture effectively creates a trust layer:
This approach allows banks to adopt AI without sacrificing auditability or regulatory compliance.
SWIFT connects more than 11,500 financial institutions across over 200 countries, meaning infrastructure adopted at this layer could scale across global markets.
Corporate-action data flows through multiple intermediaries including custodians, brokers and asset managers. Errors at any stage can cascade across systems, making data integrity a critical concern.
By introducing verification at the data-generation stage, institutions aim to reduce operational risk while enabling automation of previously manual workflows.
The pilot matters for Chainlink adoption because it moves the network deeper into core financial infrastructure, not just crypto markets.
From a token-economics perspective, institutional usage is directly tied to LINK demand. Enterprise usage of Chainlink infrastructure generates fees converted into LINK tokens and stored in a network reserve, linking adoption to token fundamentals.
Chainlink already secures over $28 trillion in cumulative transaction value and processes $18 billion in monthly cross-chain volume, with partnerships including SWIFT, UBS, JPMorgan and central banks.
Thus, broader institutional integration, especially if Chainlink becomes embedded in global financial infrastructure could become a key long-term catalyst for LINK valuation, particularly as tokenized assets are projected to reach $11 trillion to $30 trillion by 2030.
The collaboration reflects a broader trend of combining AI with blockchain-based verification to improve financial data reliability. Banks are increasingly exploring hybrid infrastructure that integrates traditional systems with new technologies rather than replacing legacy networks.
The pilot also builds on earlier SWIFT-Chainlink experiments involving tokenized assets and cross-system interoperability, as financial institutions prepare for growing digital-asset adoption.
As banks move toward AI-driven operations, resolving data accuracy challenges may prove more important than model capability. The test suggests that trusted verification layers could become central to scaling AI across global finance.
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