解密加密货币：如何估算国际稳定币流量

Decrypting Crypto: Howto Estimate InternationalStablecoin Flows Marco Reuter WP/25/141 IMF Working Papersdescribe research inprogress by the author(s) and are published toelicit comments and to encourage debate.The views expressed in IMF Working Papers arethose of the author(s) and do not necessarilyrepresent the views of the IMF, its Executive Board,or IMF management. 2025JUL IMF Working PaperResearch Department Decrypting Crypto: How to Estimate International Stablecoin FlowsPrepared by Marco Reuter* Authorized for distribution by Maria Soledad Martinez PeriaJune 2025 IMF Working Papersdescribe research in progress by the author(s) and are published to elicitcomments and to encourage debate.The views expressed in IMF Working Papers are those of theauthor(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. ABSTRACT:This paper presents a novel methodology—leveraging a combination of AI and machine learningto estimate the geographic distribution of international stablecoin flows, overcoming the “anonymity” of cryptoassets. Analyzing 2024 stablecoin transactions totaling $2 trillion, our findings show: (i) stablecoin flows arehighest in North America ($633bn) and in Asia and Pacific ($519bn). (ii) Relative to GDP, they are most significantin Latin America and the Caribbean (7.7%), and in Africa and the Middle East (6.7%). (iii) North America exhibitsnet outflows of stablecoins, with evidence suggesting these flows meet global dollar demand, increasing duringperiods of dollar appreciation against other currencies. Further, we show that the 2023 banking crisis significantlyimpeded stablecoin flows originating from North America; and finally, offer a comprehensive comparison of ourdata to the Chainalysis dataset. Decrypting Crypto: How toEstimate International StablecoinFlows Prepared by Marco Reuter 1Introduction Policymakers are increasingly wary of the popularity of crypto assets and have called forbetter monitoring of crypto transactions and international crypto asset flows (BIS (2023),EU (2023), G7 (2023) , FATF (2023), FSB (2023), IMF (2023), US Treasury (2023)). At thesame time, recent research shows that crypto assets are increasingly used for internationaltransactions, particularly when capital flow measures make it difficult to use traditionalchannels (von Luckner et al. (2023), von Luckner et al. (2024)), and that they could poten-tially be sizable (Cardozo et al. (2024), Cerutti et al. (2024), Auer et al. (2025)). However,estimating international crypto asset flows remains challenging due to the opaque nature ofcrypto assets. The main contribution of this paper is the development of a novel method that enablesthe identification of the geographic regional origin of crypto wallets, facilitating the measure-ment of international stablecoin flows.1Before detailing our method, we address a commonmisconception—contrary to popular belief, the vast majority of crypto assets donotprovideanonymity. Every transaction is publicly recorded on a freely accessible ledger known as ablockchain. The perception of anonymity arises because blockchain data is pseudonymized;rather than recording personal information such as names or residences, blockchains log onlythe wallet addresses of senders and receivers. A wallet address, typically a long hexadecimalstring such as '0xdFDEe1155E1dd7c01774560C6E98C41B7da945dB', does not directly reveal personal in-formation about the user. The key challenge in mapping the geography of crypto asset flowsis supplementing blockchain data with useful information about senders and receivers. Ourmethodology addresses this challenge by enabling the estimation of the geographic region ofany arbitraryself-custodial wallet2in the Ethereum ecosystem. To estimate the geographic region of self-custodial wallets (we assign wallets to one ofthe following five regions: Africa and the Middle East, Asia and the Pacific, Europe, NorthAmerica, and Latin America and the Caribbean), our methodology involves obtaining ge-ographic information for a subset of wallets through two distinct approaches.First, weleverage domain names assigned to wallets through systems such as the Ethereum NameSystem (ENS).3We employ a large language model (LLM) to infer linguistic and cultural markers—such as language, script, or regional references—that suggest a wallet’s likely re-gion. Second, we identify wallets that frequently transact with centralized exchanges (CEXs)targeting specific regional markets, assuming that a wallet predominantly interacting with,for example, a Latin America focused exchange is likely from that region. These two methodsprovide an ad hoc regional classification for a subset of wallets, which we then use as labeledtraining data to train a machine learning model for classification of arbitrary wallets. The core of our approach lies in leveraging this training data to train a machine learningmodel to recognize patterns in on-chain activit