Central Bank Group Unveils Data Project Mapping Crypto Transfers

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A new decentralized finance data platform could underpin future regulation of crypto market actors, according to a study released by the Bank for International Settlements (BIS) Wednesday.

Project Atlas has initially been used to map out significant off-chain international flows between crypto exchanges, said a proof-of-concept report issued jointly with the Dutch and German central banks.

“We are developing a new and important public good for central banks globally,” Cecilia Skingsley, head of the BIS Innovation Hub, said in a statement. “Data on cross-border flows are relevant for areas like payments and macroeconomic analysis.”

“Though relatively small compared with total on-chain network traffic, identified flows between crypto exchanges are significant and substantial economically,” said the study, which initially looked at transactions on the Bitcoin network. “The output of Project Atlas could serve as a starting point for preliminary assessments and inform the drafting of data reporting requirements and regulation of crypto market actors.”

Central bankers are looking to gain better knowledge of risks in markets that are often hard to get to grips with. Though BIS first floated the idea of a “cryptocurrency market intelligence platform” in June 2022, more recent crises such as the collapse of the FTX exchange have highlighted the dangers of unregulated actors operating in opaque markets.

The report from BIS, which groups central banks from across the world, warns that crypto data can be “manipulated or distorted.” It cites figures that suggest as much as 70% of activity on some exchanges is wash trading, an outlawed activity in which the same traders buy and sell the same asset in order to mislead and manipulate markets.

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Major centralized exchanges such as BINANCE have sought to assuage investor concerns by publishing details of the wallets they control, a form of assurance known as proof of reserves that BIS says “could be used for novel approaches to data-driven supervision” in the future.

Edited by Sandali Handagama.

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