Chainlink announced a major partnership with Fidelity International and Sygnum to bring Net Asset Value (NAV) data onchain. This collaboration will make the NAV of Fidelity’s $6.9 billion Institutional Liquidity Fund accessible onchain in real-time. Traditionally, NAV is calculated by determining the value of a fund’s assets minus its liabilities and dividing by outstanding shares, updated after each trading day. This onchain integration will enable real-time access, marking a big change in the financial industry.
Sygnum’s clients will directly benefit from this initiative. Recently, Sygnum tokenized $50 million from Matter Labs’ treasuries. This is part of Matter Labs’ larger strategy to move treasury reserves onchain while still investing in high-quality, short-term debt securities. Fidelity’s Institutional Liquidity Fund, which primarily targets institutional investors, offers various money market sub-funds.
Sergey Nazarov, Chainlink’s co-founder, emphasized the importance of this move. He described fund tokenization as a leading trend in the digital asset space. Nazarov said that tokenized funds offer much greater efficiency compared to traditional methods. He predicted that this approach would become standard practice across the asset management industry.
In related news, the Depository Trust and Clearing Corporation (DTCC) announced in May the results of its Smart NAV industry pilot, which used Chainlink’s Cross-Chain Interoperability Protocol (CCIP). The pilot aimed to extend the DTCC’s Mutual Fund Profile Service I (MFPS I). It showed that delivering structured data onchain and establishing standard roles and processes could embed foundational data into many onchain use cases, like tokenized funds and bulk consumer smart contracts.
The DTCC sees potential to expand the pilot’s scope. They plan to use the technology to support a broader range of use cases across multiple blockchains. This signals a transformative era for the financial industry, bringing more efficiency and accessibility to financial data and processes.
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