• A new Chainlink (LINK) initiative aims to standardize the process of collecting and distributing information about key actions taken by corporations such as mergers, dividends and stock splits – vital data that is currently fragmented across countries.
  • Key participants included Euroclear (a major clearing and settlement firm in traditional finance), Swift (the messaging platform that connects banks around the world) and Franklin Templeton (the asset manager), while crypto projects Avalanche (AVAX), ZKsync (ZK) and Hyperledger Besu also contribute.
  • The process can “dramatically reduce the manual processes required, enabling significant potential operational efficiency and cost reduction,” Wellington Management’s digital asset and tokenization head said.

As a seasoned analyst with over two decades of experience navigating the complex world of traditional finance and emerging technologies, I am particularly intrigued by Chainlink’s latest initiative. Having witnessed firsthand the frustrations and inefficiencies stemming from the lack of standardization and real-time data for corporate actions, this move towards automation and unification of information could be a game-changer.


As a researcher, I’m thrilled to share that Chainlink (LINK), collaborating with heavyweights like Euroclear, Swift, and Franklin Templeton, has embarked on an initiative this week. The goal? To streamline and standardize corporate actions data using cutting-edge AI and blockchain technology. This project targets a persistent issue in the financial sector – the lack of real-time, standardized data for events like mergers, dividends, and stock splits, which are often fragmented, particularly in European markets, as per Chainlink’s report.

By automating and standardizing this data, businesses could potentially save a substantial amount of money each year that is now being lost to operational inefficiencies caused by human error and manual data processing, as suggested by the report. This data is frequently utilized by investors.

Sergey Nazarov, one of the founders of Chainlink, stated that transforming scattered corporate action data into consistent ‘golden records’ has a significant impact. This unified data source can be trusted by numerous market players as the sole authority, thereby accelerating financial markets, minimizing mistakes, and decreasing expenses.

The initial stage of this project centered around gathering and organizing corporate actions data for both equity and bond investments in six European nations. By integrating its decentralized oracles with advanced language models such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude, the initiative aimed to collect corporate actions data from multiple sources. This data was then reformatted into a standardized structure known as “Golden Records,” ensuring compliance with global financial norms like ISO 20022 and the Securities Market Practice Group (SMPG) recommendations. Finally, Chainlink’s Cross-Chain Interoperability Protocol (CCIP) was employed to distribute this data across various blockchain networks.

Chainlink Partners With Major Financial Players to Improve Corporate Actions Data Reporting Using AI and Blockchain

In later phases, we’ll look into methods to merge this system with current financial structures such as Swift messaging protocols, to foster wider acceptance across industries, according to the report.

The initiative involves entities such as Euroclear, SWIFT, UBS, Franklin Templeton, Wellington Management, CACEIS, Vontobel, and Sygnum Bank participating. Additionally, blockchain partners like Avalanche (AVAX), ZKsync (ZK), and the Hyperledger Besu networks have also lent their support to this endeavor.

Mark Garabedian, Wellington Management’s director of digital assets and tokenization strategy, stated that by using AI and Chainlink oracles to analyze, format, and send valuable but unstructured data, we can greatly minimize manual tasks. This leads to the possibility of increased operational efficiency and cost savings,” in simpler and more conversational terms.

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2024-10-21 22:10