On June 4, 2025, at the Token Nation Conference in São Paulo, Henrique Videira, an executive of the Central Bank of Brazil, stated that the bank plans to utilize transaction data from its central bank digital currency (CBDC) infrastructure, known as DREX, as a direct input for setting the country’s interest rate benchmark. Here are the details:
Data Collection and Aggregation: Each payment and asset transfer recorded on DREX’s distributed ledger generates a time – stamped, structured entry. By aggregating these entries at the group level, the central bank aims to measure consumption shifts, liquidity pockets, and sector performance in near – real time.
Data Application in Policy – making: Staff economists will incorporate these metrics into existing output gap and credit supply models before each meeting of the Monetary Policy Committee. This enables the central bank to have a more timely understanding of economic activities than relying solely on tax receipts or bank statements. When output falls below potential or liquidity tightens, the bank can consider a rate cut earlier than usual. Conversely, when spending is robust, relevant data can support a quicker decision to raise rates.
Data Privacy Protection: DREX stores only hashed personal identifiers, preventing individual tracing. Anonymized DREX data passes through internal filters, merges with wholesale settlement flow on the same ledger, and is presented on policy dashboards that track spending by merchant category, collateral movements, and regional trade volumes.
Credit Access Channel: The central bank also plans to establish a credit access channel. Borrowers with limited banking history can authorize lenders to review their DREX cash flow records, providing auditable proof of income without the need for pay stubs. The central bank intends to publish a consultation paper on this model in 2025.
In addition, the central bank hopes that universities, startups, and public institutions will build analytics layers on DREX to further tap into the value of CBDC data. For example, the agricultural agency Embrapa and public health institutes can query anonymized datasets to improve crop yield forecasts or disease spread simulations.
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