Report of FSA’s Joint Research on
Analyzing Decentralized Financial Systemusing On-Chain and Off-Chain Data

June 2023
QUNIE Corporation

Purpose and Background of the Research•

In a decentralized financial system, many issues have been pointed out from the viewpoints of userprotection, financial crime prevention, and financial stability, etc. In order to reap the fruits of technologicalinnovation, it is essential to reduce these risks. In considering how to respond to various risks, risk
assessment using objective and reliable data is considered important. However, as the FSB, FATF, and otherorganizations have pointed out in their reports, there is a lack of data necessary to understand the actual
state of decentralized financial systems, including DeFi and P2P.

•

Therefore, as part of the FSA's "International Joint Research on Blockchain" this research will conduct asurvey on the actual status of distributed financial systems, including DeFi and P2P, using on-chain/off-chain
data. In distributed financial systems, in addition to on-chain data such as transaction records on theblockchain, more in-depth data can be obtained by linking off-chain data such as IP addresses, web traffic,
and sanctions-related information with blockchain addresses. We will conduct this research in order tounderstand the actual situation of such on-chain/off-chain data, including whether or not the data can beobtained, and to provide a useful perspective for considering future policy measures.

* Note that the addresses and transactions that can be analyzed with the blockchain analytics tools, etc. usedin this research are only a small portion of the total, and not necessarily the data of the entire decentralized
financial system. (See below for details).

2

Acknowledgements and Disclaimer
Acknowledgements•

In preparing this report, we received useful advice and comments from Professor Naoyuki Iwashita of Kyoto
University, Professor Kazue Sako of Waseda University, and Research Professor Shin'ichiro Matsuo of Georgetown
University. We also received useful suggestions and advice from observers at the Digital Agency and the Bank ofJapan, as well as from officials at the Financial Services Agency.

•

However, any errors in the content regarding this report are attributed to the trustee, Qunie Corporation.

Disclaimer•

The contents of this report do not represent the official views of the JFSA.

•The contents in this report other than historical or current facts are forward-looking statements based on
information available at the time of writing, and actual trends may vary due to a variety of uncertainties.

3

Table of Contents
Glossary
Chapter 1: The Need for Data Analysis in Decentralized
Financial Systems
1-1 Data Gap Issues identified in FSB Reports
1-2 Data Gap Issues identified in FATF Reports
Chapter 2: On-Chain/Off-Chain Data Mapping
2-1 On-chain/off-chain Data Connections and Components
2-2 On-chain/Off-chain Data Mapping
Chapter 3: Survey and Examination of Data Necessary to
Understand the Actual State of Decentralized Financial
Systems
3-1 Various Data Sources and Scope of This Research
3-2 Survey Method
3-3 Overview of Blockchain Analytics Tools

Chapter 4: Expert Research Findings
4-1 Scope and Limitations of Data Analysis
4-2 Reliability Evaluation of Obtained Data

4-3 Results of Financial Stability-Related Data Analysis
4-3-1 Availability of Data as Indicated by the FSB Report
4-3-2 Research Survey Items
4-3-3 Major VASPs
4-3-4 Major Lending Platform
4-3-5 Stablecoins Related
4-3-6 DeFi Related
4-3-7 Main Findings
4-4 Results of AML/CFT-Related Data Analysis
4-4-1 Availability of Data as Indicated by the FATF Report
4-4-2 Research Survey Items
4-4-3 Major VASPs
4-4-4 Major Lending Platform
4-4-5 Unhosted Wallets
4-4-6 AML/CFT Related
4-4-7 Main Findings
Chapter 5 Conclusion

appendix
Data Analysis Methods Utilized in the Research Literature4

Glossary* Some of the terms in this report do not necessarily have fixed definitions, and there are also differences in definitions among
blockchain analytics tool companies. Note that these terms are specified based on the definitions of the blockchain analytics
tools employed in this report.
Terminology

Definition

AML/CFT

Anti Money Laundering and Combating the Financing of Terrorism

DeFi

Decentralized Finance: Financial services provided through the use of smart contracts

DEX

Decentralized Exchange (a type of DeFi)

EOA

Externally Owned Account: Accounts on the Ethereum blockchain that are managed with a private key and can send and receive
native tokens and other tokens and deploy and execute smart contracts

FATF

Financial Action Task Force on Money Laundering

FSB

Financial Stability Board

KYC

Know Your Customer: Customer Identification Program

P2P

Peer to Peer (Transaction): Transactions using unhosted wallets (for the purposes of this report, transactions between unhosted
wallets)

TVL

Total Value Locked: Total crypto assets deposited with DeFi (locked in smart contracts)

VASP

Virtual Asset Service Provider: Providers of crypto asset exchanges, custodial (i.e. hosted) wallets, etc.

5Glossary
Terminology

Definition

On-chain data

• Data that can be obtained on the blockchain (addresses, transaction history, balances, and other smart contract status)

Off-chain data

• Data other than on-chain data
*In this report, layer 2 of the Ethereum blockchain (e.g., Arbitrum) is considered off-chain data.

BC Explorer• For the purposes of this report, this refers to blockchain data analysis sites that are publicly available on websites (Etherscan,
Dune Analytics, etc.)

Crypto assetrelated databases• For the purposes of this report, refers to crypto asset market information sites published on websites (CoinGecko,
CoinMarketCap, etc.)

Blockchain
analytics tools• Analytics tools provided by blockchain analytics companies• On-chain data can be queried and searched, information such as category and account names for some addresses identified
by blockchain analytics companies, and transactions for high-risk addresses.

High Risk address• For the purposes of this report, certain blockchain analytics companies calculate risk values on a scale of 100, based on thefollowing information, with a score of 80 or higher being a "high-risk address"
 Addresses used for fraud and hacking
 Addresses posted on public sanctions lists
 Information from other blockchain analysis company
 Research information inside the blockchain company
 Information from Open Source Intel (OSINT)High Risk
Transaction• Transactions where the senders or recipients of the transaction, or both, are high-risk addresses

Smart Contract• A program that is written to the blockchain and defines rules that are automatically executed when a function is invokedthrough a transaction

6

Glossary
Terminology

Definition

Token Contract• Smart contracts that manage the amount of tokens (USDC/USDT, etc.) issued, owners, balances, etc. that comply with ERC20 standards, etc.
• Owner, balance, etc. are updated as tokens are transferred*1

Oracle Contracts• Smart contracts that feed data to obtain off-chain external data
• Used primarily as a price oracle to obtain external market prices and interest rates*2

Contract Account• Account for deployed smart contracts• Smart contracts are executed in response to messages received from EOA and other contract accounts*3

Transaction• Digitized and signed actions initiated by EOA• In Ethereum, transfer tokens, deploy smart contracts, or invoke smart contract functions*4

Block• Data containing the hash (cryptographic digest) and transaction of one previous block in the blockchain• Blocks are generated by a randomly selected validator on the Ethereum blockchain*5

Bridge• Also called a cross-chain bridge, a generic term for functions and services that provide a way to interconnect different
blockchains and transmit tokens and other items.
• When the tokens to be sent between different blockchains are different, the tokens of the source blockchain are generally
locked (frozen) in the bridge and exchanged for the tokens of the destination blockchain*6.

Hosted Wallet• Wallet provided by VASP and others• Users entrust the management of their private keys to VASPs, etc. (wallet administrators), and VASPs can intervene andexecute transfer transactions, etc. of users' cryptographic assets.

Unhosted Wallet• Wallets in which users directly manage their private keys without going through a VASP, etc. Users can generally execute
cryptographic asset transfer transactions, etc. directly.

*1: Ethereum.org ERC-20 TOKEN STANDARD https://ethereum.org/en/developers/docs/standards/tokens/erc-20/*2: Ethereum.org ORACLES https://ethereum.org/en/developers/docs/oracles/*3: Ethereum.org ETHEREUM ACCOUNTS https://ethereum.org/en/developers/docs/accounts/*4: Ethereum.org TRANSACTIONS https://ethereum.org/en/developers/docs/transactions/*5: Ethereum.org BLOCKS https://ethereum.org/en/developers/docs/blocks/*6: Ethereum.org BRIDGES https://ethereum.org/en/developers/docs/bridges/

7Chapter 1: The Need for Data Analysis in Distributed Financial Systems

8

Chapter 1: The Need for Data Analysis in Decentralized Financial Systems•

In this chapter, we will organize the types of data needed to understand the actual state of decentralized financial
systems from the perspective of financial authorities and the possibility of obtaining such data. In particular, the FSB
report and the FATF report on data gaps will be used as a starting point.

•

This chapter is organized as follows


Section 1-1, Explain the factors contributing to the data gaps identified in the FSB report and examples ofavailable/unavailable data and additional data to be obtained.

Section 1-2, Explain the results of the investigation of P2P transactions by blockchain analytics companies as pointed
out in the FATF report, and examples of red flags indicators in the identification of AML/CFT crypto asset related data.

9

1-1. Data Gap Issues Identified in the FSB Reports(1) FSB report issues•

In a report (published in February 2023) pointing out the financial stability risks of DeFi and other issues, the report points out the problemof lack of transparency and consistency of data in the crypto asset market, including DeFi. (see table below)•

In the future, they will work with Standard Setting Bodies (SSBs) and regulators to consider approaches to fill data gaps to measure andmonitor DeFi interconnections.
Table 1-1-1 Data Gap Factors in the FSB Report

Data gaps factor
Difficulty in aggregating andanalyzing the vast amount of data
available on a distributed ledger
Information on the public ledger isanonymous

A lot of off-chain data

No obligation to report consistent
data in accordance withregulations

Possibility of data manipulation bydata providers

Details•

While data from public blockchains is transparent and immutable in some respects, it is generally difficult to collect and analyze
due to its sheer volume.

•

While transaction data at the wallet level is accessible, the lack of data on the identity of the wallet owner makes vulnerability
assessment very difficult.
Various privacy-enhancing technologies (wallet mixers/tumblers/anonymity-enhancing crypto assets, etc.) exist that can obscure
the transparency of transactions by certain users.

••

The large amount of off-chain data in the crypto asset market, including DeFi, makes it difficult to get a picture of overall market
activity from on-chain data alone.

•

A portion of the crypto asset ecosystem is currently outside of, or not in compliance with, the regulatory framework and istherefore not obligated to produce and report consistent and reliable data, or is not in compliance with its obligations.

•

Some data providers may be incentivized to manipulate data to make their respective platforms appear more important andattract additional transaction volume or investment. Market incentives for trading and lending platforms, coupled withparticipants who deviate from or act without compliance with the existing regulatory framework, increase the risk of market
manipulation and data falsification.

Source: FSB, The Financial Stability Risks of Decentralized Finance, February 2023, https://www.fsb.org/2023/02/the-financial-stability-risks-of- decentralised-finance/101-1. Data Gap Issues Identified in the FSB Reports(2) FSB report issues (example data)•

Examples of available/unavailable data to be obtained, as noted by the FSB, are as follows•

Based on these points, this research identifies the data sets that are desirable to obtain, and verifies the availability and reliability of eachdata set using blockchain analytics tools.

Table 1-1-2-1 Data Gaps for Unbacked crypto-assets

Transmission
Channels
Wealth Effects

Confidence Effects

Financial Sector
Exposure

Use in Payments
and Settlements

Available Metrics

Data Gaps••••

Market capitalization of crypto-assets
Trading volumes
Realized volatility and gamma
Geographical adoption

•
•
•
•

Share of households invested in crypto-assets
Share of assets relative to household wealth
Demographic skew among household’s holdings
Owners of unbacked crypto-assets••

Share of retail ownership of crypto-assets
Number of clients in infrastructures that provide access to cryptoassets (e.g. trading platforms, wallet providers)•

Volume of crypto-asset fraud

•
•
•

•

•
•
•
•

Share of institutional ownership of crypto-assets
Share of assets invested in crypto-assets
Number of large financial service providers offering crypto-asset
services
Volume of crypto-asset derivatives market
Open interest of crypto-asset derivative contracts
Correlations of crypto-assets with other asset classes
Share of transaction volume by transaction size

AUM and share of holdings of funds that offer exposure to cryptoassets (by asset type e.g. spot, derivative, eco-system and investor
type)
Bank sector exposure (absolute vs hedged; change in open interest)
Reporting by financial institutions on crypto-assets held and serviced

•
•
•
•

Prices and delta (over one week, 1m, 3m, 6m, 1y)
Trading volumes (absolute vs. average)
Number of large payment service providers supporting crypto-assets
Market share of major crypto-asset exchanges

•
•

•
•
•

Number and value of transactions– Jurisdiction of the payers and payees– Type of transactions (e.g. remittances, ecommerce, trading)Types of crypto-assets employed
Acceptance as legal tender

Source: FSB, Assessment of Risks to Financial Stability from Crypto-assets, February 2022, https://www.fsb.org/2022/02/assessment-of-risks-to- financial-stability-from-crypto-assets/11

1-1. Data Gap Issues Identified in the FSB Reports(2) FSB report issues (example data)
Table 1-1-2-2 Data issues for stablecoins

Transmission
Channels
Wealth Effects

Confidence Effects

Available Metrics•••

Market capitalization of stablecoins
Trading volumes
Realized volatility•

Owners of stablecoins••

Share of retail ownership of stablecoins
Number of clients in infrastructures thatprovide access to stablecoins (e.g. trading
platforms, wallet providers)•

Volume of crypto-asset fraud•

Amounts and share of holdings of ETFs thatoffer exposure to stablecoins (by investor
type)
Profit and loss exposures
Reserve assets invested in regulated
markets
Liquidity of reserve assets
Granular and robust data on composition of
stablecoins reserve assets
Reporting by financial institutions on
crypto-assets held and serviced•

Financial Sector
Exposure•••

Share of institutional ownership ofstablecoins
Share of assets invested in stablecoins
Number of large financial service providers
offering stablecoin services
Size of stablecoin market relative to US
prime money market funds

•
•
•
•
•

Use in Payments
and Settlements•••

PricesTrading volumes
Number of large payment service providers
supporting stablecoins

Data to be added(as noted by experts)

Data Gaps

•
•
•
•
•

Number and value of transactions
Jurisdiction of the payers and payees
Type of transactions (e.g. remittances, ecommerce, trading)
Usage in crypto-asset trading platforms, bystablecoin
Breakdown of uses of stablecoins•

Business relationship between VASP andissuers of stablecoins••

Addresses to be frozen
Total amount subject to be frozen•

Actual crypto asset transactions byinstitutional investors and financial
institutions (e.g., types of crypto
assets/stablecoins preferred by large users)-

Source: FSB, Assessment of Risks to Financial Stability from Crypto-assets, February 2022, https://www.fsb.org/2022/02/assessment-of-risks-to- financial-stability-from-crypto-assets/12

1-1. Data Gap Issues Identified in the FSB Reports(2) FSB report issues (example data)
Table 1-1-2-3 Data issues for DeFi (1/2)

Transmission
ChannelsAvailable Metrics••

Wealth Effects

•
•
•
•
•
•

Confidence Effects•

Total value locked-in, gross, adjusted andnet; realized volatility
Transaction volume of DeFi’s Exchange(DEX)
Wallet growthMarket capitalization and transaction
volume of governance tokens;
Transaction volume in DeFi lending
Lending rate in DeFi Lending
Utilization rate of liquidity pool of DeFi
Lending and Exchange
DeFi yield and return

Number of clients in infrastructures thatprovide access to DeFi (e.g. trading
platforms, wallet providers)

Data to be added(as noted by experts)

Data Gaps

•
•
•

•
•

•
•

Share of retail vs institutional participation
Number of dApps on a blockchain
Liquidity pools, DeFi stablecoins, derivatives(entities within the DeFi space, including
types of financial institutions (specialized or
traditional financial institutions) tounderstand linkages of DeFi with the rest of
the financial system)
Metrics to measure leverage
Information on the governance tokens
holders could be obtained from to see towhat extent the governance isdecentralized (e.g. if the ownership ofgovernance tokens is concentrated, that
entity could be considered the actual
developer)

Volume of crypto-asset fraud
Share of transactions in unbacked cryptoassets vs. stablecoins

•
•
•

•
•
•
•

•

•
•
•

TVL (TVL-based market share of major
DeFi protocols)Market Capitalization of Stablecoin
Degree of linkage between major DeFi
(DEX-Lending, etc.)
Total tokens locked in the cross-chain
bridgeCross Chain Bridge's business relationshipwith VASP
Oracle's market share in TVL and othermeasures
Collateral ratios, leverage ratios, and actual
rehypothecation according to collateral typefor lending protocols
Major remittance address from Treasury
ProtocolGovernance Token Concentration
DeFi Protocol Concentration
Total amount and number of DeFi-related
hacking losses

Source: FSB, Assessment of Risks to Financial Stability from Crypto-assets, February 2022, https://www.fsb.org/2022/02/assessment-of-risks-to- financial-stability-from-crypto-assets/13

1-1. Data Gap Issues Identified in the FSB Reports(2) FSB report issues (example data)
Table 1-1-2-3 Data issues for DeFi (2/2)

Transmission
ChannelsAvailable Metrics•••

Financial Sector
Exposure••••

Use in Payments
and Settlements

Share of institutional ownership of cryptoassets
Share of assets invested in crypto-assets
Number of large financial service providers
offering crypto-asset services
Volume of crypto-asset derivatives market
Open interest of derivative contracts
Correlations of crypto-assets with otherasset classes
Share of transaction volume by transaction
size• Price of key players (DOT, UNI, LINK)
and delta over one week, one month,three months, six months, one year
and 7-day average volume;

Data to be added(as noted by experts)

Data Gaps•

Amounts and share of holdings of ETFs thatoffer exposure to crypto-assets by investor
type• Number and value of transactions• Breakdown of counterparties– Jurisdiction of the payers and payees– Type of transactions (e.g.
remittances, ecommerce, trading)•Amount invested in traditional financial
assets utilizing tokens locked to smart
contracts as collateral-

Source: FSB, Assessment of Risks to Financial Stability from Crypto-assets, February 2022, https://www.fsb.org/2022/02/assessment-of-risks-to- financial-stability-from-crypto-assets/14

1-2. Data Gap Issues Identified in FATF Reports(1) FATF report issues (P2P transactions)•

The July 2021 FATF report reported that the findings of several blockchain analytics company on the actual status of P2P transactions (e.g.,
the percentage of total P2P transactions) showed a wide variation in results.

•

Regarding the percentage of bitcoin transactions that occurred without VASP between 2016 and 2020, four companies analyzed 40% to
70% in terms of number of transactions, while two companies analyzed 10%~32%. In addition, for the value of transactions (in US dollars),a large variation exists in the company’s analysis, ranging from 2% to 91% (Graph 2).

•

A variation of 0.3% to 12.7% in terms of number of transactions and 0% to 15.4% in terms of transaction value (USD) exists for thepercentage of fraudulent bitcoin transactions identified between 2016 and 2020 (Graph 3).
Fig. 1-2-1 Example of FATF report's findings on P2P transactions and illicit transactions

Source: FATF, Second 12-Month Review of the Revised FATF Standards on Virtual Assets/VASPs, July 2021 https://www.fatf-gafi.org/en/publications/ Fatfrecommendations/Second-12-month-review-virtual-assets-vasps.html

15

1-2. Data Gap Issues Identified in FATF Reports(2) FATF report issues (AML/CFT related)•

The September 2020 FATF report noted that not only can money launderers, terrorist financiers, and other criminals obtain, move, andstore assets digitally outside of the regulated financial system, but also that it is difficult for reporting entities to identify suspicious activity ina timely manner, obfuscating the source and destination of funds The report also points out that it is difficult for reporting entities to identify
suspicious activity in a timely manner.

•

In light of the above points, we have published a report on ML/TF red flags related to crypto assets to assist reporting entities, including
VASPs, in identifying and reporting potential ML and TF activities related to crypto assets.
Table 1-2-2 Red Flag Indicators

Red flag Indicators
Size and frequency oftransactions
Transaction Patterns

Use Case Examples••••

Structuring VA transactions (e.g. exchange or transfer) in small amounts, or in amounts under record-keeping or reporting
thresholds, similar to structuring cash transactions.
Making multiple high-value transactions in short succession, such as within a 24-hour period, etc.
A new user attempts to trade the entire balance of VAs, or withdraws the VAs and attempts to send the entire balance off theplatform.
Making frequent transfers in a certain period of time (e.g. a day, a week, a month, etc.) to the same VA account, etc.

Anonymity•

VAs transferred to or from wallets that show previous patterns of activity associated with the use of VASPs that operate mixing or
tumbling services or P2P platforms.
Funds deposited or withdrawn from a VA address or wallet with direct and indirect exposure links to known suspicious sources,
including darknet marketplaces, mixing/tumbling services, questionable gambling sites, illegal activities (e.g. ransomware) and/or
theft reports, etc.

Senders or Recipients••

A customer’s VA address appears on public forums associated with illegal activity.
A customer is known via publicly available information to law enforcement due to previous criminal association, etc.

•

Transacting with VA addresses that are connected to known fraud, extortion, or ransomware schemes, sanctioned addresses,
darknet marketplaces, or other illicit websites.
VA transactions originating from or destined to online gambling services, etc.

•

Source of Funds or Wealth•

Geographical Risks••

Customer’s funds originate from, or are sent to, an exchange that is not registered in the jurisdiction where either the customer
or exchange is located.
Customer sends funds to VASPs operating in jurisdictions that have no VA regulation, or have not implemented AML/CFT controls.

Source: FATF, Virtual Assets Red Flag Indicators of Money Laundering and Terrorist Financing, September 2020 https://www.fatf-gafi.org/en/ publications/Methodsandtrends/Virtual-assets-red-flag-indicators.html

16Chapter 2: On-Chain/Off-Chain Data Mapping

17

Chapter 2: On-Chain/Off-Chain Data Mapping•

In order to analyze data in a decentralized financial system, there is a limit to using only a random list of numbers andletters, such as wallet addresses and transaction IDs, which can be obtained on-chain. It is necessary to link these onchain data with data outside the blockchain, such as wallet owners (off-chain data), to understand the actual status oftransaction relationships, etc. Therefore, this chapter attempts to provide an overview of on-chain/off-chain data,
organize its components, and map both types of data.

•

Specifically, we organized the elements and connections between on-chain data and other off-chain data that can beobtained on the Ethereum blockchain. Based on this, we mapped the overall picture of on-chain/off-chain data.

•

This chapter is organized as follows


Section 2-1, Describes the components of on-chain/off-chain data.

Section 2-2, Describes the mapping of on-chain/off-chain data.

18

2-1. Elements of On-chain/Off-chain Data
(1) On-chain/off-chain data connections•

Blockchain analytics company typically utilize open source and proprietary tools and know-how to collect both on-chain and off-chain data,group related addresses, label addresses (e.g., assigning a VASP's account name to a set of addresses managed by a particular VASP), andcalculate address risk scores. ), and calculate risk scores for addresses.

•

Below is an image showing some of the various data that blockchain analytics tool companies are collecting and the connections between
them.

[On-chain data (example)][Off-chain data (example)]

Data Retrievable on the Ethereum Blockchain

Data other than on-chain data

[Address group]
0xaf988a...
0x9c4fed... 0xe65cdb...
(Unhosted Wallet*)

0x28c6c0...
0x21a31e...
0xdfd529...
(VASP*)

0x7d655c... 0x32cefb...
0x165cd3...
(DeFi*)

Transaction
0x1ad5350c...

Transaction
0x49a673c0...

Transaction
0xa6277563...

[Block] (transaction history, etc.)

On-chain with BC Explorer and our own system / Collect asmuch off-chain data as possible to identify connections*Category names and account names are not identifiable on-chain.

Figure 2-1-1 Main connections between on-chain and off-chain data (image)

VASP Related Information(Address owned by VASP, Country where VASP is located, etc.)
Sanctions and Crime-Related Information(Sanctions lists, Past fraud/crime, Darknet, etc.)
Crypto Asset Trading Related Information(Type of crypto asset, price information, etc.)
User Information(e.g., Location of address owner identified from IP address, etc.)
Other OSINT information(Website, SNS information, etc.)
Internal information of business operators, etc.
(Customer KYC information, Internal transaction information, etc.)192-1. Elements of On-chain/Off-chain Data
(2) Elements of off-chain data•

Possible elements of off-chain data tied to on-chain data include the following. (On-chain data is defined in the glossary, so the explanation isomitted.)* This page does not discuss the availability of each piece of data, and the table is a sampling of the major on-chain data that are generally
believed to exist.

•

The relationship between the respective off-chain and on-chain data is shown in the mapping diagram in the next section.
Table 2-1-2 Major Components of Off-Chain Data (1/2)

Classification

Data Element
Token Transaction Price

Off-chain data

Data Contents• Transaction prices of various crypto assets, etc. offered by VASPs, etc.

Hosted Wallet• Users' IP addresses, KYC information (name, address, date of birth, credit card information, etc.),transaction history, wallet private keys, web traffic data, etc.

Unhosted Wallet• User information for wallets such as Metamask, including the user's IP address, wallet private key,
and web traffic data

DeFi User Interface• User information such as IP addresses of users via interfaces, web traffic data, etc. as user
information for DeFi, etc.

Transaction information outside theblockchain• Information on transactions outside the blockchain, such as transfers of crypto assets between
customers by rebooking within the VASP, etc.

Governance Voter Information• Information related to governance voting in DeFi, DAO, etc., including the identity of the voter(e.g., user name on Discord, etc.)

Infrastructure service user informationsuch as nodes and APIs

• Information on infrastructure service users who use Ethereum nodes and Ethereum blockchain API
services, including users' IP addresses, KYC information (name, address, date of birth, credit card
information, etc.), and number of API calls

Github source code, parameters, etc.

• Source code, parameters, etc. of smart contracts posted on Github (a software development
platform for storing and publishing programs, etc.), etc.

Information on assets backing stablecoin
issuances• Name of the financial institution that manages the bank deposits, government bonds, and otherassets backing stablecoin issuances, as well as the type and amount of assets.
20

2-1. Elements of On-chain/Off-chain Data
(2) Elements of off-chain data
Table 2-1-2 Major Components of Off-Chain Data (2/2)

Classification

Off-chain data

Data Element
Validator Information• Validator information, such as IP address of the validator node, secret key, etc.

Staking Information• Validator staking information, such as the investor and amount of money that will be used forstaking services

Layer 2 Transaction Information• Transaction information executed at Layer 2, such as the user's address, sending address, amounttransferred, etc.
(Layer 2 is defined as off-chain data in this research)

Bridge node information• Information on the node operated by the bridge administrator, such as IP address and secret key

Token information locked on the bridge• Information about the tokens locked on the bridge, including the type and amount of tokens
locked21

2-1. Elements of On-chain/Off-chain Data
(3) Connection points for on-chain/off-chain data•

Nodal points such as wallets and interfaces generally exist between on-chain and off-chain data. The table shows the major connection points.

•

The relationship between each connection point and on-chain/off-chain data is shown in the mapping diagram below.
Table 2-1-3 On-chain/off-chain connection points

Connection point

Description (specific examples of connections)• Oracle provider provides crypto asset prices, etc. provided by VASP to protocols in the blockchain(DeFi, etc.)

External Oracle
Hosted Wallet• VASP (hosted wallet provider) manages customer information and private keys, and customers instruct
the VASP to transfer tokens from the wallet.

Unhosted Wallet• Using a wallet provided by software companies (wallet provider), the user directly manages the private
key to transfer tokens, etc.

Wallet• DeFi and others operate user interfaces such as websites and smartphone applications, and users use
the services directly.

User Interface

Node

Smart contract developer node

• Smart contract developer deploys smart contract on blockchain based on source code on GitHub (mayalso be deployed from wallet)

Stablecoin Operator Node

• Stablecoin operator issues and burns stablecoins with administrative authority.

Validator Nodes• Validators participate in the blockchain consensus by depositing participation fees and registeringclients to generate and approve blocks

Layer 2 operator node

• Layer 2 operator executes transactions on the Layer 2 blockchain and forwards the results to the Layer
1 main chain

Bridge administrator node

• Bridge administrators send and verify tokens and other information that users exchange with eachother between different blockchains.

22

2-2. On-chain/Off-chain Data Mapping(1) On-chain/off-chain data mapping•

We extracted and mapped the main components of the on-chain/off-chain data of the Ethereum blockchain and visualized the following threepoints.


Main components and overall classification of on-chain data, connection points, and off-chain dataConnection between on-chain data, connection points, and off-chain dataManager of the off-chain data (organization or person who manages the off-chain data) *only in the detailed version23

2-2. On-chain/Off-chain Data Mapping[On-chain data]

Smart Contract

OracleEthereum Blockchain[On-chain and off-chain connection points]
External Oracle(Provided by Oracle provider)Figure 2-2-1 On-chain/off-chain data mapping (overview version)[Off-chain data]
Crypto asset prices provided by VASPs, etc.

Oracle Contract

Token Contract

WalletDeFi Smart Contract(Stablecoins, governanceTokens, etc.)

User
Interface

EOA(Address and balance)

Hosted Wallet(Wallet provided by VASP)

VASP User Information(IP address, KYC information, transaction information, etc.)

Unhosted Wallet

DeFi and other user information(IP address, transaction information, private key, etc.)

User Interface(DeFi website, etc.)

DeFi and other user information(IP addresses, web traffic data, etc.)
Governance Voter Information(IP address, number of votes, etc.)

Transaction(Transaction information)
Smart Contract Developer Node

Node

Block
(Transaction history)

Stablecoins Operator Node

Validator NodeGithub source code, parameters, etc.
Stablecoins Issuance Backing Reserve Information(Name of financial institution, type of asset, amount, etc.)

Validator information (IP address, etc.)

BridgeOther Blockchains(Note: This is not an exhaustive list of all on-chain/off-chain data and connection points and their relationships, especially for financial
regulators. Mapping focused on functions and data considered important.)24

2-2. On-chain/Off-chain Data Mapping[On-chain data].
DeFi Protocol

DEX

DeFi
Smart
Contract

Automated
MarketMaker

Smart Contract
Token
Contract

OracleContract

Stablecoin
Governance Tokens, etc.

Account
Contract
Account

Price feeds provided
by providers

Hosted Wallet(provided by VASP and others)

EOA

Block

Other blockchains (e.g. Avalanche)

BridgeBridgeValidators,
etc.

Consensus Layer

Node

Transaction[Off-chain data][Off-chain data manager]

Crypto asset prices provided by VASPs,
etc.

VASP and oracle providers(Chainlink, etc.)

VASP User Information(IP address, KYC information, etc.)
Transaction information outside the blockchain(inter-wallet transaction history within VASP)

VASPs and custodians

Wallet User Information(IP address, transaction information, etc.)

DeFi and other users

DeFi and other user information(IP addresses, web traffic data, etc.)

DeFi Protocol Development Company

Governance Voter Information(IP address, number of votes, etc.)

Governance token holders and community
operators

Infrastructure provider
node

Infrastructure service user information(IP address, KYC information, etc.)

Infrastructure provider

Smart contractdevelopers node

Github source code, parameters, etc.

Smart contract developer

Stablecoin Operator NodeStablecoin Issuance Backing Reserve Information(Name of financial institution, asset, etc.)

Stablecoin Operator

Validator Nodes

Validator information (IP address, etc.)
Staking information (staking amount, etc.)

Validator

Layer 2 operator node

Layer 2 Transaction Information(sender’s address, remittance amount, etc.)

Layer 2 service provider

Bridge administrator
node

Bridge node information(IP address and secret key)

Bridge administrator

Unhosted Wallet

User
Interphase

DeFi
Smart
Contract

WalletExecution Layer[On-chain and off-chain connection points]
OracleEthereum blockchain

Figure 2-2-2 On-chain/off-chain data mapping (detailed version)user interface(e.g., DeFi's Web site)

25

Chapter 3: Survey and Examination of Data Necessary to Understand theActual State of Decentralized Financial Systems

26

Chapter 3: Survey and Examination of Data Necessary to Understand the Actual State ofDecentralized Financial Systems•

Based on the organization of on-chain/off-chain data and connection points up to the previous chapter, this chapter
details the data sources considered useful as sources of these data, the range of data sources utilized in the data analysisof this research, and the data survey methodology.

•

This chapter is organized as follows
 Section 3-1, Summarize the various data sources that are considered useful for understanding the actual state ofdecentralized financial systems, and then indicate the range of data sources actually utilized in this research.
 Section 3-2, Provide an overview of the survey methodology used in the data analysis.

 Section 3-3, Provide an overview of the blockchain analytics tools that were heavily used in this data analysis.

27

•As shown in the table, various data sources exist, but in addition to on-chain/off-chain differences, data reliability, availability, and accessibility(i.e., free of charge) vary widely. While understanding the characteristics of each type of data, it is necessary to analyze data according toregulatory objectives.

•

In this research, we will utilize "BC (blockchain) Explorer," "cryptographic asset-related databases," and "blockchain analytics tool company(analytics tool researchers)," which may be able to obtain data of a different nature from the data available due to supervisory actions, etc., toconduct data analysis, etc.
Data Source

Businesses, etc.
(Inside Information)

Businesses, etc.
(Disclosure Information)

BC Explorer

Crypto Asset-Related
Database

Blockchain Analysis
Tool Company

Summary

Data obtained from
reporting requests

Information legallydisclosed by listedcompanies, etc.

Blockchain account andtransaction-related data

Crypto asset prices andother data

Data obtained fromblockchain analytics
tools/researchers

Public / Private

PrivatePublicPublicPublicPrivateOn-chain/Off-chain

Off-chain

Off-chain

On-chain(Many are) Off-chain

Combination of on-chain
and off-chain

Data Reliability

High

High

High

Medium(Many informations are on
VASPs declaration basis)

Medium to low?

〇 to △(Some VASPs are difficult
to obtain)

VASP

〇

〇

△
(Certain information isavailable regarding major
VASPs)(Unregistered)
VASP

×

×

△ to ×〇 to △(Some VASPs are difficult
to obtain)

DeFi△ to x(May be obtained for DeFi
regulated as VASP)

×(Some DeFi may
voluntarily disclose
information)△(Data on contract
addresses, etc. can beobtained)△(Data on DEX, etc. can beobtained)

Unhosted Wallet(including P2P)△
(VASP-Unhosted Walletrelated data may beavailable)×△ to x(Some unhosted wallets
may be obtained)×〇 to △(Depends on the capabilityof the blockchain analytics
tool companiy)〇 to △(Depends on the capabilityof the blockchain analytics
tool company)〇 to △(Depends on the capabilityof the blockchain analytics
tool company)△(Depends on the capabilityof the blockchain analytics
tool company, but is it
accurate or not)Scope of this research

Table 3-1 Various Data Sources and Scope of this Research

3-1. Various Data Sources and Scope of This Research*Note that blockchain analytics tools vary in functionality and characteristics, and the range of data that can be obtained and the level of confidence in the data varies.

28

3-2. Survey Method(1) Outline•

The data available from each of the data sources and the methodology of this research are as follows

Table 3-2-1 Data Sources and Survey Methods Utilized in this Research

Data Source

Data that can be obtained

BC (Blockchain)
explorer• Data such as blockchain addresses and transactions available
from public Web sites• Data such as category name (VASP, DeFi, etc.)/account nameassigned to the address that identified the target• Investigate data that can be obtained by referring to public
Web sites• Main survey targets
 Etherscan
 Dune Analytics, etc.

Crypto asset-related
databases• Data on crypto asset prices, market price charts, market
capitalization, and number of recent transactions available
from public Web sites• Transaction data, latest news, etc. on specific crypto assets,
crypto asset traders, etc.

• Investigate data that can be obtained by referring to public
Web sites• Main survey targets
 CoinGecko
 Coinmarketcap

Blockchain
Analytics
tools• Data that can be obtained from analytics tools provided byblockchain analytics company(address holder category/account name, risk score based on
sanctions and past criminal history, etc.)• Investigate data that can be obtained from multiple
blockchain analytics company tools(Avoid risk of relying on data from one company)

Research
by Experts• Data from research conducted by experts from blockchain
analytics company(e.g. data on complex conditions combining on-chain datawith in-house databases, etc.)• Outsource research to specific blockchain analytics company
to obtain necessary data

Blockchain
analytics
company

Survey Methodology for this research29

3-2. Survey Method(2) Candidate data for analysis•

As noted in the table, the data considered to be obtainable differed by each method. On the other hand, based on the fact that much of theinformation that can be obtained from (1) BC Explorer is also incorporated into (3) and (4), etc., data analysis was conducted primarily using (2),(3) and (4).

•By contracting with multiple companies, the blockchain analytics tool attempted to reduce the risk of relying on data from one company (whose
reliability is difficult for us to verify).
Table 3-2-2 Examples of data that can be obtained(2) Crypto-asset related
database(1) BC Explorer•

Main
Features•

Websites that allow users tosearch and query data in theblockchain
Some major VASPs and DeFi canget the address from the accountname

Block information (block number,number of transactions, etc.)• Account information (address,
balance by token, etc.)• Transaction information(transaction time, number, etc.)*Data can be obtained for eachaccount/transaction (overall
statistics cannot be obtained)••

Web site offering real-time
market prices for crypto assets••

Major DeFi TVL Token Issuance
VolumeTrading information on major
decentralized exchanges (last 24
hours trading volume, number oftoken pairs/trade volume by pair,
etc.)

•

List of token prices per VASPToken Price Trend Chart(From the 1st to the whole
period)Chart of market capitalization of
tokens
Latest Crypto Asset News
Overview of major tokens(founder, characteristics, etc.)•

On-chain
data

•

•
•

Off-chaindata

Data Availability••

Account names for someaddresses(e.g. some VASPs and DeFi thatpublish addresses)(3) Blockchain analytics
tools

•
•
•

•

•

•

•

Analytical tools provided byblockchain companies
Provides risk indication of
specific entities, addresses, andalert detection of high-risk
address transactions forinvestigative authorities, etc.

(4) Research by experts•

In-depth research conducted byexperts from blockchain analytics
company using information fromtheir own analytics tools anddatabases

Account information (address,
balance by token, etc.)
Transaction information (last 24hour trading volume, first/lasttrade date, trade date, numberof trades, volume, etc.)
Diagram display of account
counterparties, etc.

•

Aggregation of transactions
based on complex search criteria(small transactions, multiple
transfers to the same party in ashort period of time, fraudulent
address users, etc.)
Anonymous service address
Token freeze address,
transaction volume, etc.

Category name and accountname of the identified address
Risk value of an address
Risk values are self-determinedby the blockchain analytics
company
Account Information(country licenses held, level of
KYC enforcement, etc.)

•

•
•

•
•
•
•

Aggregation of transactions by
category and accountAggregate VASP information
Number of hacking victims andamount of damage
Aggregation of fraudulent
addresses (sanctioned lists,
fraud extortion, darknet, etc.)
Number of VASP users, etc.
30

3-3. Overview of Blockchain analytics tools•

Blockchain analytics tools are generally paid software provided by private companies that combine on-chain and off-chain data to identify category
and account names and determine the risk score of the corresponding addresses using clustering techniques, etc. Blockchain analytics tools aremainly used by VASPs, institutional investors, investigative authorities, etc. for the purpose of detecting transactions with high-risk addresses andidentifying account names of addresses under investigation (e.g., theft of cryptographic assets).

•

The following table summarizes the data and other information provided by the analytics tools used in this study (note that the information
obtained, its accuracy, and its scope vary depending on the tool).
Table 3-3 Overview of Blockchain analytics tools (1/2)

Item

Data and services

Specifics•

Entity Information•

Display andsearchblockchain
data

VASP: Holding address, account name, name of the country where the legal entity is located,current operating status, description of services, date of establishment, CEO name, brief
description, contact information, e-mail address, country where the office is located, etc.
DeFi/Wallet: address, account name, balance by token, last 24 hours trading volume, total
sent/received, first/last active date, etc.

Remarks•

Category names, accountnames, etc. labeled in theaddress can be obtained.

Entity Transaction
Information•

Transaction volume by VASP token, daily profit/loss amount, asset value, balance andtransaction amount by address managed by VASP, transaction history, etc.

•

Only VASPs are eligible.

Entity Risk Information•

Countries of license registration, AML/KYC implementation status, etc.

•

Only VASPs are eligible.

•

Risk level: 4 levels: Severe, High, Medium, and Low (severe: Risk score 80-100, High: risk
score 50-79, etc.)
Risk score: Risk value calculated on a scale of 100*Information on risk determination: addresses used for fraud or crime in the past, addresses
subject to sanctions, etc.

•

Risk level classification and risk
score values vary by analytics
tools

Address risk level/risk score••

Reasons for determining that the transaction is high risk (threats, malware, mixing, darknet,
phishing, ransomware, etc.)
Graphical display of number of transactions, amount (USD, ETH, etc.), and number oftransactions
Transaction history (transaction date and time, transaction hash number, sender's address,
recipient's address, amount, etc.)
The above can be displayed by blockchain, time period, etc.

•

Display addresses in order of risk score (link to view screen for each address)•
Description of high-risk
transactions related to theaddress

Listing of high-risk addresses

•
•

31

3-3. Overview of Blockchain analytics tools
Table 3-3 Overview of Blockchain analytics tools (2/2)(data)
item

Data and services
providedSpecifics•••

Display andsearchblockchain
data

Visualize the connection of transactions by drawing the transaction history by received/sent,
category name/account name of sent/received address, token, and amount on one screen.
Transactions associated with high-risk addresses are distinguished in red
For a given address, automatically draw multiple transactions before and after in relation toeach other

Remarks•

Clicking on the appropriate
icon (address) will display thesending/receiving transaction
recipient, token name, amount,etc., centered on that address.

•

When executed by specifying
an address and period, thetransaction status within thetarget period is continuously
displayed.

•

By specifying the address inadvance, the system
automatically detects when atransaction occurs.

Transaction Graph

Entity Related News

Trace
Function

Automatic Transaction
Tracing

Alert
Detection

Automatic detection ofdesignated addresstransactions

Sanctions
List

View the list of sanctions foreach country

Other

Record blockchain addresses•

Net news related to entities, etc. (excerpts of articles from various news sites)•

Automatically draws a series of multiple transactions executed for a specified address over a
specified period of time•

Automatic detection and alert notification of transactions at specified addresses, including
high-risk addresses•

Display information (names of targets, details of sanctions, etc.) published in sanctions listsof U.S. OFAC, UK, EU, etc.

•

A function that records a search address once and eliminates the need to enter the address
the next time the search is performed.

32

Chapter 4: Data Analysis Results

33

Chapter 4: Data Analysis Results•

In this chapter, we present the results of actual analysis of the data pointed out in the previous chapters, obtained as much as possible byusing analytical tools and researchers.

•

This chapter is organized as follows


Section 4-1, Discuss the scope of the data analysis conducted and the limitations of the data analysis (in that there are many addresses
and transactions that are difficult to analyze).

Section 4-2, Report the results of a quantitative comparison with other company’s tools to examine the reliability of the data obtained
from the researcher.

Section 4-3, Present the results of our data analysis focusing on the data availability and financial stability aspects of VASPs, lending
platform, stablecoins, and DeFi, also taking into account the points raised in the FSB report.

Section 4-4, Present the distribution of VASPs, lending platform, unhosted wallets (including P2P), AML/CFT-related data availability, andhigh-risk transactions and the results of our analysis, taking into account the findings of the FATF report.

34

4-1. Scope and Limitations of Data Analysis
The scope of this research is limited to transaction data from addresses identified by the blockchain analytics company used in this study, including
account names. As a result, the results of the data analysis are localized and limited to these data (the data of the entire decentralized financial
system was not analyzed).
Table 4-1-1-1 Scope of survey for on-chain/off-chain data

Item

Description

Scope of data research• Among the transaction data on the Ethereum blockchain in 2022, theblockchain analysis company have identified some addresses by categoryname (VASP, DeFi, etc.) or account name (VASP name, etc.), which wereused as survey targets. The transaction data sent and received from theseaddresses were aggregated.

Figure 4-1-1 Scope of transaction data survey
Transaction data on the Ethereum blockchain in 2022
Surveyed

Addresses identified
with account names, etc.
of surveyed entities

transaction within the surveyed entity
Addresses identified
with account names,
etc.

Supplement

Addresses identified
with account names, etc.
of surveyed entities

Addresses identified
with account names,
etc.

Not applicable to the survey• The number of transactions identified bycategory or account name is 4-33% of thetotal and is not indicative of the total• If the category names or otherclassifications identified by the blockchain
analytics company are incorrect, the data
will also be inaccurate.

Table 4-1-1-2 Number of transactions for entities surveyed
Number of transactions
Surveyed
EntityRatio to thetotal number of
Ethereumtransactions*(1) Identified
account name,etc.

(2) Unknown
account name,etc.

(1) + (2)*

VASP-A

3,381,239(13%)

22,397,649(87%)

25,778,888

6.3%

VASP-B

1,293,983(4%)

34,175,484(96%)

35,469,467

8.7%

Lending Platform

144,457(33%)

297,820(67%)

442,2770.1%

5,772,973(18%)

26,097,100(82%)

31,870,073

7.8%

Addresses withunknown accountnames, etc.

Addresses identified
with account names, etc.
of surveyed entities

Addresses withunknown accountnames, etc.

Unhosted Wallet

Addresses withunknown accountnames, etc.

Addresses with unknown
account names, etc.
of surveyed entities

Addresses withunknown accountnames, etc.

*”(1)+(2)” is the number of transactions identified address holders, even if it is an unhosted wallet transaction.
*The number of transactions for the entire Ethereum in 2022: approx. 409 million.

35

4-2. Reliability Evaluation of Obtained Data
(1) Comparison of research results and other company’s tools: A certain level of confidence in the number and value of VASP-related transactions•

There is a marked difference in the number of addresses held by the two companies, approximately 20~2,200 times. This may be due to the twocompanies different information collection resources and labeling methods.

•

Since the difference between the two companies in terms of the number of transactions and transaction value is about 50-150%, which is not alarge difference, a certain degree of reliability can be seen in the data.
→The above results suggest, for example, that (2) may contain surveyed entity-related addresses that are currently rarely used.
Table 4-2-1 Comparison of survey results and other company’s tool

Item

VASP-AClassification(1) Company A
Research Results(2) Company B
Tool Information(2) ÷ (1)Addresses

21,834

8,066,565

36945.0%

Incoming

9,310,372

14,369,476

154.3%

Outgoing

14,269,851

12,135,550

85.0%

Incoming

314,321344,649

109.6%

Outgoing

311,188

340,245109.3%

14,22031,755,943

223318.9%

Incoming

15,717,777

7,425,815

47.2%

Outgoing

18,752,632

13,616,799

72.6%

Incoming

250,914265,435

105.8%

Outgoing

252,892

255,632

101.1%

15,730306,442

1948.1%

Incoming

109,391

127,706

116.7%

Outgoing

205,940

202,160

98.2%

Incoming

13,78516,092116.7%

Outgoing

11,725

16,288138.9%

Total Transfers

Total Volume (M$)
Addresses

VASP-B

Total Transfers

Total Volume (M$)
Addresses
Lending
Platform*Using token price rates as of April 2023.

Total Transfers

Total Volume (M$)

Remarks
Significant difference (369x)

Significant difference (2,233x)

Significant difference (19 x)

36

4-2. Reliability Evaluation of Obtained Data
(2) Comparison of research results with other company’s tools: Reliability of data varies depending on trading partners.
•

Since there is no significant difference in the number of transactions between the two VASPs and other VASPs, a certain degree of reliability canbe seen in the data.

•

The number of transactions/addresses between VASP and DeFi differs by a factor of about 3 between the two companies. This is thought to be
due to differences in the way DeFi determines high-risk addresses between the blockchain analytics companies.
Table 4-2-2 Comparison of High-risk transactions survey results and other company’s tool

Item

Classification

Counterparty

Incoming /Outgoing

Addresses

In VASP-A(2) Company B Tool
Information(2) ÷ (1)-85 address

-

-

-

2,184,570(29 addresses)

2,184,443(17 addresses)

99.9%(58.6%)

VASP-A Incoming

88,988(18 addresses)

88,983(16 addresses)

100.0(88.9%)

VASP-A Outgoing

59,102(18 addresses)

59,102(18 addresses)

100.0%(100.0%)

VASP-A Incoming

173,577(115 addresses)

172,891(99 addresses)

99.6%(86.1%)

VASP-A Outgoing

133,483(398 addresses)

116,634(136 addresses)

87.4%(34.2%)

Difference (Number ofaddresses 3x)

VASP-A Incoming

30,158(262 addresses)

9,954(132 addresses)

33.0%(38.8%)

Difference (Trx. 3x, Addr.
3x)

VASP-A Outgoing

3,465(37 addresses)

3,383(21 addresses)

96.4%(82.3%)

VASP-A Incoming

11,565(55 addresses)

10,561(32 addresses)

91.3%(58.2%)

Difference (Number ofaddresses 2x)

VASP-A Outgoing

25,622(131 addresses)

19,561(58 addresses)

76.3%(44.3%)

Difference (Number ofaddresses 2x)

Other VASPs
Total Transfers

Remarks(1) Company A
Research Results

VASP-B

VASP-A

Total high-risk transfers (high-risk addresses)*

DeFi

Unhosted Wallet

Difference (Number ofaddresses 2x)*The number of addresses is the total of both sending and receiving addresses; (2) is the number of high-risk transactions and addresses in (1) that are high-risk using the tools of blockchain analytics company B.

37

4-2. Reliability Evaluation of Obtained Data
(3) Comparison of research results with other company’s tools: Reliability of data varies depending on trading partners.
•

There is a marked difference in the number of high-risk transactions/addresses for unhosted wallets between the two analytics companies. Thissuggests that there are significant differences in the identification rate of unhosted wallets and the method used to determine high-risk addresses
among the analytics companies.
Table 4-2-3 Comparison of High-risk transactions survey results and other company’s tool

Item

Classification

Counterparty

Incoming /Outgoing

Addresses--

In VASP-B-

VASP-A

Total Transfers

Unhosted
Wallet(2) Company B Tool
Information(2) ÷ (1)8 addresses--

694,838(8 addresses)

694,838(8 addresses)

100.0%(100.0%)Remarks

VASP-B Incoming

45,792(56 addresses)

45,744(55 addresses)

100.0%(98.2%)

VASP-B Outgoing

51,716(211 addresses)

47,144(57 addresseses)

91.2%(27.0%)

Difference (Number ofaddresses 4x)

VASP-B Incoming

4,039(102 addresses)

2,464(60 addresses)

61.0%(58.8%)

Difference (Trx. 2x, Addr.
2x)

VASP-B Outgoing361(14 addresses)

356(10 addresses)

98.6%(71.4%)

VASP-B Incoming

3,467(31 addresses)

3,264(21 addresses)

94.1%(67.7%)

Difference (Number ofaddresses 1.5x)

VASP-B Outgoing

22,259(105 addresses)

20,157(39 addresses)

90.6%(37.1%)

Difference (Number ofaddresses 3x)

DeFi

unhostedwallet(1) Company A
Research Results(Same as the number of transactions in VASP-A / VASP-B)

Other VASPs
VASP-B

Total high-risk transfers (high-risk addresses)*

Addresses--

291 addresses--

Total Transfers

P2P transactions-

425,882(291 addresses)

15,349(55 addresses)

3.6%(18.9%)

Significant difference(Trx. 28x, Addr. 5x)*The number of addresses is the total of both sending and receiving addresses; (2) is the number of high-risk transactions and addresses in (1) that are high-risk using the tools of blockchain analytics company B.

38

4-2. Reliability Evaluation of Obtained Data
(4) Key Findings
Table 4-2-4 Key Findings

Limitations of Blockchain Analytics Tools

Key Findings

Contents

Only in a small
percentage ofcases can the
counterparty be
identified.

• Of all the transactions conducted with related addresses of the surveyed (VASPs, etc.), only asmall portion of the transactions were able to identify the counterparties (i.e., categories ofsending/receiving addresses, account names, etc.).
• Reasons may include difficulty in obtaining sufficient information to identify categories andaccounts (e.g., addresses with only one transaction in the past), insufficient capacity ofanalytics tool companies (not enough off-chain data available to identify addresses, such as IP
addresses, web traffic, and sanctions-related information), etc.

Data exist thatshow marked
differencesamong analytics
tool companies.

• While there are data categories where the differences between the two companies, such as
the number and value of transactions between VASPs, are slight and can be given a certain
degree of credibility, the results show marked differences between the analytics toolcompanies, such as the number of addresses held by VASPs and DeFi/unhosted wallet-relatedtransactions.
• The reason for the relatively high confidence in VASP-related transactions may be that VASPs,
where transactions above a certain size are concentrated, may be easier to identify thanunhosted wallet-related transactions (including P2P), where transactions are held byindividuals and are considered sporadic.
• Other differences in labeling methods (heuristic-based: based on empirical rules, etc. or
proprietary algorithms, etc.)

Supplement• It is possible that morecounterparties wereidentified by othercompany’s analytics tools.
However, there is apossibility that theidentification is based on
insufficient data, and it isdifficult to judge thesuperiority of the analytics
company based solely on
the identification rate.

39

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report•

With regard to the points raised in the FSB report, the results of an investigation into the possibility of data acquisition using various tools andexpert research confirmed that some of the data made available in the report were difficult to obtain using the methods of this research, and that
it was possible to obtain some of the data that was said to be unavailable (including data with limited obtaining).

•

Note that the results of this survey are only localized based on the analytical tools used in this study and the results of expert research.

(1) Data availability for unbacked crypto-assets
Table 4-3-1-1 Data availability for unbacked crypto-assets (1/7)

Unbacked crypto-assets

Classif
icationTransmission
Channels

WealthEffects

Data
Classificat
ion

Available
Metrics〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Blockchain
Analytics Tools

Research byExperts

Remarks△
Major crypto assets canbe obtained×
Difficult to obtain data△
Major crypto assets canbe obtained

Targets are crypto
assets whose market
capitalization isdisclosed by crypto
asset-related databases,etc.

△
Can be obtained fromthe crypto asset issuer'stransaction data on a
per transaction basis.
(aggregation is difficult).

×
Difficult to obtain data△
Can be obtained fromthe crypto asset issuer'stransaction data on a
per transaction basis.
(aggregation is difficult).

〇
Can be obtained fromthe crypto asset issuer'stransaction data

Targets are addresses
identified by blockchain
analytics companies asunbacked crypto-assets

×
Difficult to obtain data▲
Price fluctuation data
can be obtained andmay be calculated fromsaid data.

×
Difficult to obtain data

(Not included in thissurvey)Market capitalization of
crypto-assets△
Major crypto assets canbe obtained

Trading volumes

Realized volatility andgamma

Crypto Asset
Related Database40

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(1) Data availability for unbacked crypto-assets
Table 4-3-1-1 Data availability for unbacked crypto-assets (2/7)
Classif
icationTransmission
Channels

Data
Classifica
tion

Unbacked crypto-assets

Available
Metrics

WealthEffects

Data Gaps〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts(Not included in thissurvey)Geographical adoption

×
Difficult to obtain data

×
Difficult to obtain data▲
Data on the countries ofregistration of the main
VASP licenses andavailable crypto assets
can be obtained

Share of households
invested in crypto
assets×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Share of assets relative
to household wealth×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Demographic skew
among household’s
holdings

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Owners of unbacked
crypto assets▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

Remarks
Some blockchain
analytics companies
that were not used inthis research havepublished reports onregional penetration

Certain types of users
are identified, such as
major exchanges,
prominent investors andsanctioned users.

41

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(1) Data availability for unbacked crypto-assets
Table 4-3-1-1 Data availability for unbacked crypto-assets (3/7)

Unbacked crypto-assets

Classif
icationTransmission
Channels

Data
Classifica
tion

Available
Metrics
Confidenc
e Effects

Data Gaps〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related Database

Remarks

Blockchain
Analytics Tools

Research byExperts

Targets are addresses
identified as accountnames, etc. byblockchain analytics
companies

Share of retailownership of crypto
assets▲
It is difficult to calculate
the share because it islimited to a part of theaddress account names,
etc. that are specified.

×
Difficult to obtain data

×
Difficult to obtain data▲
It is difficult to calculate
the share because it islimited to a part of theaddress account names,
etc. that are specified.

Number of clients ininfrastructures thatprovide access to crypto
assets (e.g. trading
platforms, wallet
providers)×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Platformers and wallet
providers may have it
as internal data.

×
Difficult to obtain data▲
Some fraud cases havebeen identified, but it isdifficult to calculate thetotal amount▲Some fraud cases havebeen identified, but it isdifficult to calculate thetotal amount

Targets are addresses
identified as fraudulent
transactions byblockchain analytics
companies

Volume of crypto asset
fraud×
Difficult to obtain data42

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(1) Data availability for unbacked crypto-assets
Table 4-3-1-1 Data availability for unbacked crypto-assets (4/7)

Unbacked crypto-assets

Classif
icationTransmission
Channels

Financial
SectorExposure

DataClassifica
tion

Available
Metrics

Results of availability of data
Surveyed Data〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts

Share of institutional
ownership of crypto
assets▲Institutional holdings
are limited to identified
addresses, making it
difficult to calculate
overall share×
Difficult to obtain data▲
Institutional holdings
are limited to identified
addresses, making it
difficult to calculate
overall share▲
Institutional holdings
are limited to identified
addresses, making it
difficult to calculate
overall share

Share of assets invested
in crypto assets

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)BC Explorer

Number of large
financial service
providers offering
crypto asset
services

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Volume of crypto asset
derivatives market

×
Difficult to obtain data△
Main crypto assets canbe obtained.

×
Difficult to obtain data△
Main crypto assets canbe obtained.

Open interest of crypto
asset derivative
contracts

×Difficult to obtain data△
Main crypto assets canbe obtained.

×
Difficult to obtain data△
Main crypto assets canbe obtained.

Correlations of crypto
assets with other asset
classes×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thesurvey)Share of transaction
volume by transaction
size▲
Limited to someaddresses such as
identified by accountname, etc., and difficult
to ascertain the share oftransactions.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc., and difficult
to ascertain the share oftransactions.

▲
Limited to someaddresses such as
identified by accountname, etc., and difficult
to ascertain the share oftransactions.

Remarks

It is considered possible
to ascertain a certain
level of information
through information onregistered operators ineach country.

Targets are addresses
identified as financial
institutions byblockchain analytics
companies43

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(1) Data availability for unbacked crypto-assets
Table 4-3-1-1 Data availability for unbacked crypto-assets (5/7)

Unbacked crypto-assets

Classif
icationTransmission
Channels

Financial
SectorExposure

DataClassifica
tion

Data Gaps〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts

AUM and share ofholdings of funds thatoffer exposure to crypto
assets (by asset typee.g. spot, derivative,
eco system and investor
type)

×Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Bank sector exposure(absolute vs hedged;change in open interest)

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Reporting by financial
institutions on crypto
assets held and serviced×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Remarks44

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(1) Data availability for unbacked crypto-assets
Table 4-3-1-1 Data availability for unbacked crypto-assets (6/7)

Unbacked crypto-assets

Classif
icationTransmission
Channels

Use inPayments
andSettlements

Data
Classifica
tion

Available
Metrics〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed Data

Remarks

BC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts

Prices and delta (overone week, 1m, 3m, 6m,
1y)×
Difficult to obtain data△
Main token prices andtrends can be obtained.

×
Difficult to obtain data△
Major Token Prices andTransition Available

Trading volumes(absolute vs. average)▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as payment
services by blockchain
analytics companies

Number of large
payment service
providers supporting
crypto assets

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Certain public
information about
registered operators
exists.

×
Difficult to obtain data〇
Possibility to calculatemarket share fromtrading volume bycrypto asset pair for
major VASPs

×Difficult to obtain data〇
Possibility to calculatemarket share fromtrading volume bycrypto asset pair for
major VASPs

Crypto asset related
databases to obtain thelast 24 hours trading
volume, etc.

Market share of major
crypto-asset exchanges

45

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(1) Data availability for unbacked crypto-assets
Table 4-3-1-1 Data availability for unbacked crypto-assets (7/7)
Classif
icationTransmission
Channels

Data
Classifica
tion

Results of availability of data
Surveyed Data

Unbacked crypto-assets

Number and value oftransactions

Use inPayments
andSettlements〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

BC Explorer▲
Limited to someaddresses such as
identified by accountname, etc.

Crypto Asset
Related Database

Remarks

Blockchain
Analytics Tools

Research byExperts×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as payment
services by blockchain
analytics companies▲
Limited to someaddresses such as
identified by accountname, etc. andjurisdiction (e.g., VASP)

Targets are addresses
identified with accountname and jurisdiction,etc. by blockchain
analytics companies

Jurisdiction of thepayers and payees×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc. andjurisdiction (e.g., VASP)Type of transactions(e.g. remittances,ecommerce, trading)×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Types of crypto assets
employed×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Acceptance as legal
tender×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Data Gaps46

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(2) Data availability for stablecoins
Table 4-3-1-2 Data availability for stablecoins (1/6)
Classif
icationTransmission
Channels

Data
Classifica
tion

Stablecoins

Available
Metrics

WealthEffects

Data Gaps

Data to be
added〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related Database

Remarks

Blockchain
Analytics Tools

Research byExperts△
Major stablecoins canbe ontained×
Difficult to obtain data〇
(Assumption ofequivalence to pegged
legal tender)

Targets are stablecoins
whose market
capitalization isdisclosed by crypto
asset-related databases,etc.

〇
Can be obtained fromstablecoin issuer
transaction data

Targets are addresses
identified as stablecoinsby blockchain analytics
companies

Market capitalization of
stablecoins〇(Assumption ofequivalence to pegged
legal tender)

Trading volumes△
Can be obtained fromstablecoin issuer
transaction data on a
per transaction basis(difficult to aggregate)×
Difficult to obtain data△
Can be obtained fromstablecoin issuer
transaction data on a
per transaction basis(difficult to aggregate)

Realized volatility

×
Difficult to obtain data▲
Price fluctuation data
can be obtained andmay be calculated

×
Difficult to obtain data

(Not included in thissurvey)Owners of stablecoins▲
Limited to a portion of
the address accountname identified.

×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are toaddresses identified as
account names, etc. byblockchain analytics
companies

Business relationshipbetween VASP andissuers of stablecoins△
Limited to VASPs
identified on the BC
Explorer and obtainedon a per transaction
basis (difficult toaggregate)×
Difficult to obtain data△
Limited to VASPs
identified by the
analytics tool and canbe obtained on a pertransaction basis(difficult to aggregate)△
A certain number canbe obtained only for the
VASPs identified by the
analytics tool

Targets are addresses
identified as VASP byblockchain analytics
companies47

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(2) Data availability for stablecoins
Table 4-3-1-2 Data availability for stablecoins (2/6)
Classif
icationTransmission
Channels

Data
Classifica
tion

Stablecoins

Available
Metrics

Results of availability of data
Surveyed Data

Data to be
added

BC Explorer

Crypto Asset
Related Database

Remarks

Blockchain
Analytics Tools

Research byExperts

Targets are addresses
identified by blockchain
analytics companies asaccount names, etc.

Share of retailownership of stablecoins▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Number of clients ininfrastructures thatprovide access tostablecoins (e.g. trading
platforms, wallet
providers)×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Platformers and wallet
providers may have it
as internal data.

Volume of crypto asset
fraud△
Can be obtained fromtransaction data ofaddresses with pastfraudulent transactions
on a per transaction
basis (difficult toaggregate)×
Difficult to obtain data△
Can be obtained fromtransaction data ofaddresses with pastfraudulent transactions
on a per transaction
basis (difficult toaggregate)〇
Can be obtained fromtransaction data ofaddresses with pastfraudulent transactions

Targets are addresses
identified as fraudulent
transactions byblockchain analytics
companies

Addresses to be frozen /total amount subject tobe frozen△
Can be obtained fromstablecoin issuer
transaction data on a
per transaction basis(difficult to aggregate)×
Difficult to obtain data〇
Can be obtained fromstablecoin issuer
transaction data

Aggregate addresses
and volume oftransactions with
"Blocked" transaction
results for stablecoinissuers

Confidenc
e Effects
Data Gaps〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

×
Difficult to obtain data48

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(2) Data availability for stablecoins
Table 4-3-1-2 Data availability for stablecoins (3/6)
Classif
icationTransmission
Channels

Data
Classifica
tion

Stablecoins

Available
Metrics

Financial
SectorExposure

Data Gaps〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed Data

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts

Share of institutional
ownership ofstablecoins▲
Institutional holdings
are limited to identified
addresses, making it
difficult to calculate
overall share×
Difficult to obtain data▲
Institutional holdings
are limited to identified
addresses, making it
difficult to calculate
overall share▲
Institutional holdings
are limited to identified
addresses, making it
difficult to calculate
overall share

Share of assets invested
in stablecoins×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thesurvey)Number of large
financial service
providersoffering stablecoin
services

BC Explorer

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Size of stablecoinmarket relative to US
prime money market
funds

×Difficult to obtain data△
Stablecoin market size
is obtainable
Market size of US MMFs
is difficult to obtain×
Difficult to obtain data〇
The size of the market
for stablecoin and US
MMFs can be obtained.

Amounts and share ofholdings of ETFs thatoffer exposure tostablecoins (by investor
type)

×Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Profit and loss
exposures×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Remarks

It is considered possible
to ascertain a certain
level of information
through information onregistered operators ineach country.

49

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(2) Data availability for stablecoins
Table 4-3-1-2 Data availability for stablecoins (4/6)
Classif
icationTransmission
Channels

Data
Classifica
tion

Stablecoins

Data Gaps

Financial
SectorExposure

Data to be
added〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts

Reserve assets invested
in regulatedMarkets / Liquidity ofreserve assets /granular and robust
data on composition of
stablecoins reserve
assets×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Reporting by financial
institutions on
crypto assets held andserviced×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Actual crypto asset
transactions byinstitutional investors
and financial institutions(e.g., types of crypto
assets/ stablecoinspreferred by large
users)▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data

Remarks

Targets are addresses
identified as institutional
investors or financial
institutions byblockchain analytics
companies50

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(2) Data availability for stablecoins
Table 4-3-1-2 Data availability for stablecoins (5/6)

Stablecoins

Classif
icationTransmission
Channels

Use inPayments
andSettlements

Data
Classifica
tion

Available
Metrics〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related Database×
Difficult to obtain data

Remarks

Blockchain
Analytics Tools

Research byExperts×
Difficult to obtain data〇
Token price to be used
for payment can beobtained from payment
service websites, etc.

Investigate thepossibility of obtaining atoken price when usedin payments.
Targets are addresses
identified as payment
services by blockchain
analytics companies

Prices×
Difficult to obtain data

Number of transactions▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Number of large
payment service
providers supporting
stablecoins×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)51

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(2) Data availability for stablecoins
Table 4-3-1-2 Data availability for stablecoins (6/6)
Classif
icationTransmission
Channels

Data
Classifica
tion

Results of availability of data
Surveyed Data

Stablecoins

Number and value oftransactions

Use inPayments
andSettlements〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

BC Explorer▲
Limited to someaddresses such as
identified by accountname, etc.

Crypto Asset
Related Database

Remarks

Blockchain
Analytics Tools

Research byExperts×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as payment
services by blockchain
analytics companies

Limited to someaddresses such as
identified by accountname, etc. andjurisdiction (e.g., VASP)

Targets are addresses
identified with accountname and jurisdiction,etc. by blockchain
analytics companies

Jurisdiction of thepayers and payees×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc. andjurisdiction (e.g., VASP)Type of transactions(e.g. remittances, ecommerce, trading)×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Usage in crypto asset
trading platforms, bystablecoin

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Breakdown of uses ofstablecoins×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Data Gaps52

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (1/11)
Classif
icationDeFi

Transmission
Channels

WealthEffects

Data
Classifica
tion

Available
Metrics

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related Database

Total value locked in,gross, adjusted andnet; realized volatility

×
Difficult to obtain data

Transaction volume of
DeFi’s Exchange(DEX)▲
Transactions for eachaddress identified as
DEX can be obtained foreach transaction, but it
is difficult to aggregate
the number oftransactions.

Wallet growth×
Difficult to obtain data〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Remarks

Blockchain Analytics
Tools

Research byExperts△
Main DeFi can beobtained.

×
Difficult to obtain data△
Main DeFi can beobtained.

Targets are DeFi forwhich crypto assetrelated databases andother data are publicly
available

×
Difficult to obtain data▲
Transactions for eachaddress identified as
DEX can be obtained foreach transaction, but it
is difficult to aggregate
the number oftransactions.

△
A certain number oftransaction data for
addresses identified as
DEX can be obtained.

Targets are addresses
identified as DEX byblockchain analytics
companies

×
Difficult to obtain data▲
Transactions for someaddresses identified as
unhosted wallets can beobtained on a pertransaction basis(historical transaction
data can be obtained).

▲
Historical trend data canbe obtained fortransactions at someaddresses identified as
unhosted wallets

Investigate thefeasibility of obtaining
data on the number oftransactions andamount of money inunhosted wallets using
DeFi.

53

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (2/11)
Classif
icationDeFi

Transmission
Channels

WealthEffects

Data
Classifica
tion

Available
Metrics

Results of availability of data
Surveyed Data〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

BC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research by
Rxperts

Remarks

Market capitalizationand transaction
volume of governancetokens△Market capitalization ofmajor tokens can beobtained
Number of transactions
can be obtained pertransaction (difficult toaggregate)△
Market capitalization ofmajor tokens can beobtained
Number of transactions
is difficult to obtain△Market capitalization ofmajor tokens is difficult
to obtain
Number of transactions
can be obtained pertransaction (difficult toaggregate)〇
Market capitalizationand number oftransactions for major
tokens can be obtained.

Major governancetokens for which cryptoasset related databases,
etc., publish market
capitalization

Transaction volume in
DeFi Lending / Lending
rate in DeFi Lending×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data▲
Some DeFi can beobtained.

May be obtained fromthe website of therelevant DeFi lending
serviceUtilization rate ofliquidity pool of DeFi
Lending and Exchange×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)DeFi yield and return×
Difficult to obtain data△
DeFi yields can beobtained, but return isdifficult to obtain×
Difficult to obtain data

(Not included in thissurvey)54

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (3/11)
Classif
icationTransmission
Channels

Data
Classifica
tion

Results of availability of data
Surveyed Data

Share of retail vsinstitutional
participation

Number of dApps on a
blockchain

DeFi

WealthEffects〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

BC Explorer▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data

Blockchain
Analytics Tools

Research byExperts

Remarks×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as accountnames, etc. byblockchain analytics
companies

×
Difficult to obtain data

×
Difficult to obtain data▲
Major DApps have been
identified and it may bepossible to aggregate acertain number ofDApps, but researchcosts are highData can be obtained bysome BC Explorers(DappRadar, etc.)

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as financial
institutions byblockchain analytics
companies

Crypto Asset
Related Database×
Difficult to obtain data

Data Gaps
Liquidity pools, DeFi
stablecoins, derivatives(entities within the DeFi
space, including typesof financial institutions(specialized or
traditional financial
institutions) tounderstand linkages of
DeFi with the rest of thefinancial system)

×
Difficult to obtain data55

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (4/11)
Classif
icationTransmission
Channels

Data
Classifica
tion

Results of availability of data
Surveyed Data

Metrics to measureleverage

DeFi

WealthEffects

Data Gaps〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Information on thegovernance tokens
holders could beobtained from to see towhat extent the
governance isdecentralized (e.g. if theownership ofgovernance tokens isconcentrated, that
entity could beconsidered the actual
developer)

Remarks

BC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts▲
Some DeFi can beobtained.

×
Difficult to obtain data

×
Difficult to obtain data▲
Some DeFi can beobtained.

Investigate thefeasibility of obtaining
data on overall DeFi
liabilities/assets, etc.

×
Difficult to obtain data▲
Limited such as
identified by accountname, etc. of the holder
address.
Dispersion of token
holdings by address canbe obtained.

Targets are addresses
identified as accountnames, etc. byblockchain analytics
companies▲
Limited such as
identified by accountname, etc. of the holder
address.
Dispersion of token
holdings by address canbe obtained.

×
Difficult to obtain data56

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (5/11)
Classif
icationTransmission
Channels

Data
Classifica
tion

Results of availability of data
Surveyed Data

TVL (TVL-based market
share of major DeFi
protocols)

DeFi

WealthEffects

Data to be
added〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Remarks

BC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts×
Difficult to obtain data△
Main DeFi can beobtained.

×
Difficult to obtain data△
Main DeFi can beobtained.

DeFi for which crypto
asset-related databases
and other data are
publicly available.
Targets are stablecoins
whose market
capitalization isdisclosed by crypto
asset-related databases,
etc.

Market Capitalization of
Stablecoins△
Major stablecoins canbe obtained△
Major stablecoins canbe obtained×
Difficult to obtain data△
Major stablecoins canbe obtained

Degree of linkage
between major DeFi
(DEX-Lending, etc.)△
Transaction data
between major DeFi's
can be obtained on a
per transaction basis(difficult to aggregate)×
Difficult to obtain data△
Transaction data
between major DeFi's
can be obtained on a
per transaction basis(difficult to aggregate)〇
Transaction data
between major DeFi's
can be obtained.

Total tokens locked incross-chain bridge△
The major bridges canbe obtained×
Difficult to obtain data

×
Difficult to obtain data△
The major bridges canbe obtained

Targets are addresses
identified as bridges byblockchain analytics
companies

Cross Chain Bridge's
business relationshipwith VASP△
Transaction data
between main DeFi andbridge can be obtainedon a per transaction
basis (difficult toaggregate)×
Difficult to obtain data△
A certain number oftransaction data for
addresses identified as
bridges and VASPs canbe obtained.

Targets are addresses
identified as VASP byblockchain analytics
companies

×
Difficult to obtain data57

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (6/11)
Classif
icationDeFi

Transmission
Channels

WealthEffects

Data
Classifica
tion

Data to be
added〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts

Oracle's market share in
TVL and othermeasures×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Collateral ratios,
leverage ratios, andactual rehypothecationaccording to collateraltype for lending
protocols▲Some DeFi can beobtained.

×
Difficult to obtain data

×
Difficult to obtain data▲
Some DeFi can beobtained.

Major remittanceaddress from Treasury
Protocol▲Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

Remarks

Targets are addresses
identified as Treasury
Protocol by blockchain
analytics companies58

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (7/11)
Classif
icationDeFi

Transmission
Channels

Results of availability of data

Data
Classifica
tion

Surveyed Data

Available
Metrics

Number of clients ininfrastructures thatprovide access to DeFi
(e.g. trading platforms,
wallet providers)Confidenc
e Effects〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

Information fromstablecoin operators is
considered necessary.

×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as fraudulent
transactions byblockchain analytics
companies

×
Difficult to obtain data△
Transaction data for
major crypto assets andstablecoins can beobtained on a pertransaction basis.
(difficult to aggregate)△
A certain number ofmajor crypto assets andstablecoin transaction
data can be obtained.

Targets are addresses
identified as unbacked
crypto-assets or
stablecoins byblockchain analytics
companies

Volume of crypto asset
fraud×
Difficult to obtain data

Share of transactions inunbacked crypto
assets vs. stablecoins△
Transaction data for
major crypto assets andstablecoins can beobtained on a pertransaction basis.
(difficult to aggregate)

Data Gaps

Remarks

BC Explorer

594-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (8/11)
Classif
icationDeFi

Transmission
Channels

Confidenc
e Effects

DataClassifica
tion

Data to be
added〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Crypto Asset
Related database

Blockchain
Analytics Tools

Research byExperts

Remarks

Governance Token
Concentration△
Transaction data foreach of the major
governance tokens canbe obtained (difficult toaggregate)×
Difficult to obtain data

×
Difficult to obtain data△
Transaction data for
major governancetokens can be obtained.

Targets are Addresses
identified as governor's
tokens by blockchain
analytics companies

DeFi Protocol
Concentration×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data△
Number and value oftransactions by main
DeFi can be obtained.

Targets are addresses
identified as DeFi
protocols by blockchain
analytics companies

×
Difficult to obtain data△
Total damage andnumber of major
hacking incidents can beobtained.

Total amount andnumber of DeFi-related
hacking losses

×
Difficult to obtain data

×
Difficult to obtain data60

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (9/11)
Classif
icationDeFi

Transmission
Channels

Financial
SectorExposure

DataClassifica
tion

Available
Metrics〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed DataBC Explorer

Remarks

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as institutionalby blockchain analytics
companies
Difficulty in acquiring aratio of crypto assets
held by financial
institutions

Share of institutional
ownership of crypto
assets▲Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Share of assets invested
in crypto assets

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Number of large
financial service
providers offering
crypto asset services×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Volume of crypto asset
derivatives market

×
Difficult to obtain data△
Main DeFi can beobtained.

×
Difficult to obtain data

(Not included in thissurvey)Open interest ofderivative contracts

×
Difficult to obtain data△
Main DeFi can beobtained.

×
Difficult to obtain data

(Not included in thissurvey)Correlations of crypto
assets with otherasset classes

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Difficult to obtain dataon crypto assets infinancial institutions'assetsShare of transaction
volume by transaction
size▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as financial
institutions byblockchain analytics
companies61

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (10/11)
Classif
icationDeFi

Transmission
Channels

Financial
SectorExposure〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data

Data
Classifica
tion

Surveyed DataBC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts

Data Gaps

Amounts and share ofholdings of ETFs thatoffer exposure to crypto
assets by investor type×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Data to be
added

Amount invested in
traditional financial
assets utilizing tokens
locked to smart
contracts as collateral

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Remarks62

4-3. Results of Financial Stability-Related Data Analysis
4-3-1. Availability of Data as Indicated by the FSB Report(3) Data availability for DeFi
Table 4-3-1-3 Data availability for DeFi (11/11)
Classif
icationTransmission
Channels

Data
Classifica
tion

Available
Metrics

DeFi

Use inPayments
andSettlementsData Gaps〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed Data

Remarks

BC Explorer

Crypto Asset
Related Database

Blockchain
Analytics Tools

Research byExperts

Price of key players(DOT, UNI, LINK)×
Difficult to obtain data△
Main token prices canbe obtained.

×
Difficult to obtain data△
Main token prices canbe obtained.

Delta over one week,
one month, threemonths, six months,
one year and 7 day
average volume

×
Difficult to obtain data△
Main token priceincrease/decrease canbe obtained.

×
Difficult to obtain data△
Main token priceincrease/decrease canbe obtained.

Number and value oftransactions▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

Targets are addresses
identified as payment
services by blockchain
analytics companies
Targets are addresses
identified as accountnames, etc. byblockchain analytics
companies
Data on transaction
type is difficult to obtain×
Difficult to obtain data

Jurisdiction of thepayers and payees×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc. andjurisdiction (e.g., VASP)Type of transactions(e.g. remittances,ecommerce, trading)×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

(Not included in thissurvey)Investigate thepossibility of obtaining
the main token prices
used in payments63

4-3. Results of Financial Stability-Related Data Analysis
4-3-2. Research Survey Items•

In this chapter, specific survey items such as the number of address holders and the number and value of transactions were set for each category
of VASPs and lending platform for the following four survey items, which were researched by experts from a blockchain analysis company.

•

The research results were organized into tables and graphs after organizing the data, and the trends and characteristics seen in the results werediscussed.

Table 4-3-2 Research survey items

Survey items

Survey Contents•

Trends in the number oftransactions of major VASPs

Trends in the number oftransactions by major lending
platform
Stablecoin related data

•
•
•
•
•

DeFi related data•

supplement

The number and value of transactions for the two major VASPs were surveyed in three
categories: incoming, within their own company, and outgoing.
Three categories were investigated: (1) by category, (2) by DeFi, and (3) by token
breakdown among the categories.
The number of transactions and the amount of transactions were surveyed for one major
lending platform in three categories: incoming, within their own company, and outgoing.
Three categories were investigated: (1) by category, (2) by DeFi, and (3) by token
breakdown among the categories.
The actual remittance status and frozen transaction data were surveyed for three major
stablecoins (USDC, USDC, and DAI).
For the major DeFi projects (Uniswap, Maker, and Aave), we surveyed the size of the DeFi,
the number of governor's token holders, collateral ratios, and rehypothecation (collateraldiversion).
As part of a survey of the overall DeFi situation, we investigated the status of cross-chain
bridge usage, hacking damage, cooperation with financial institutions, and the degree ofconcentration on specific oracle services.

•••

Account category names and accountnames used classifications defined by theblockchain analytics company.
DeFi service types were defined by us
based on public information on the Web.
The transaction amount used the token
price and other rates as of April 2023.

644-3. Results of Financial Stability-Related Data Analysis
4-3-3. Major VASPs(1) Main VASPs: Summary of the number of transactions by VASP-A/VASP-B•

Among the transactions that could be identified, many were between VASPs, suggesting a close linkage of exchanges, etc. In addition,transactions between VASPs and DeFi, and between VASPs and stablecoin issuers/investment companies, etc., were also identified (included inunhosted wallets by definition), suggesting the possibility of a close business relationship among them.

•

It should be noted that the results of this analysis do not necessarily represent the overall trend related to VASPs, as the number of transactions
with addresses where the account name or other information is unknown accounted for more than 95% of the total number of transactions.
Figure 4-3-3-1 Summary of major VASP transactions65

4-3. Results of Financial Stability-Related Data Analysis
4-3-3. Major VASPs (VASP-A)(2) Major VASPs: VASP-A by Category
Figure 4-3-3-2 VASP-A number of transactions / amount by category

Transaction Category

Table 4-3-3-2 VASP-A transactions / amount data by category
Transaction
Category

VASP-A(2)

Other VASP, DeFi,
Unhosted Wallet,
etc.

(1)

VASP-A(3)

Other VASP, DeFi,
Unhosted Wallet,
etc.

(1)
Incoming

Number of transactions/amount
Outside: Number of transactions
Inside: Transaction amount (M$)

(2) Within
VASP-A(3)
Outgoing
Incoming

Within VASP-A

Outgoing

Incoming
Unhosted Wallet

Outgoing

Amount of transaction
Amount M$
Amount Ratio
51,239
77.8%
528
0.8%
13,791
20.9%
27
0.0％
148
0.2%
123
0.2%
65,856100.0％
625,406
100.0％
625,406
100.0％
27,079
62.1%
46
0.1%
15,873
36.4%
217
0.5%
398
0.9%
0
0.0％
43,613100.0％•“(2) Within VASP-A” is the largest number of transactions within transaction categories.
→ Are most of them due to internal fund transfers of wallets.

•“(1) Incoming” is for other VASPs, and “(3) Outgoing” are for token contracts and VASPs, which
account for the majority of the number of transactions.
A certain number of DeFi and unhosted wallet transactions are also recognized.
→ Token contracts are transfers of tokens compliant with ERC-20 standards (e.g., 62% of alltransfers are USDT transfers, but the transaction amounts are small and may be partially doublecounted with actual transfers (counted in a different category such as VASP)),DeFi is intended for DEX use, while unhosted wallets may be intended for subsequent DeFi use,
etc.?

VASP
Token
Contract
DeFi

VASP

VASP
DeFi
Unhosted Wallet
Token Contract
Bridge
Other
Total
VASP
Total
VASP
DeFi
Unhosted Wallet
Token Contract
Bridge
Other
Total

Number of transactions
Transactions
Transaction ratio
334,182
74.3%
77,711
17.3%
34,776
7.7%
2,320
0.5%
566
0.1%
133
0.0%
449,688
100.0%
2,198,665
100.0%
2,198,665
100.0%
195,055
26.6%
4,211
0.6%
26,576
3.6%
496,837
67.8%
3,451
0.5%
6,756
0.9%
732,886
100.0％[Discussion]

Outside: Number of transactions
Inside: Transaction amount (M$)

DeFi

Account Category

Unhosted
Wallet66

4-3. Results of Financial Stability-Related Data Analysis
4-3-3. Major VASPs (VASP-A)(3) Major VASPs: VASP-A DeFi breakdown among Category
Figure 4-3-3-3 VASP-A number of transactions / amount by DeFi

Table 4-3-3-3 VASP-A transactions / amount data by DeFi

Transaction Category(1) Incoming
DeFi
(1)

DeFi

(3)
DeFi

VASP-A

Number of incoming transactions / amount
Maker
Sushiswap

ServiceCurve FinanceOutside: Number of transactions
Inside: Transaction amount (M$)(3) OutgoingTransacti
onsTransacti
on ratioAmountM$

Transacti
onsTransacti
on ratioAmountM$

Uniswap

Ddecentralized
Exchange

66,954

86.2%

335

179

4.3%

6

Sushiswap

Decentralized
Exchange

9,955

12.8%

49

9

0.2%

0

Maker

Stablecoins
Issuance

215

0.3%

18

0

0.0%

0

Curve
Finance

Decentralized
Exchange

65

0.1%

72

3,875

92.0%

34

ZapparFinance

DeFi Dashboard

1

0.0%

0

106

2.5%

0

Other

-

521

0.7%

54

42

1.0%

5

77,711100.0%

528

4,211

100.0%

46

Total
amountUniswap

Number of outgoing transactions / amount
Zappar Finance

Uniswap[Discussion]•

DeFi has large number of transactions and amount on decentralized exchanges (over 95% of
total).
→ Decentralized exchanges are thought to have more crypto asset exchanges and staking
remittances.

•

Among decentralized exchanges, Uniswap has the highest number of transactions for “(1)Incoming” and Curve Finance for “(3) Outgoing”.
→ The reasons are not necessarily clear, but Uniswap exchanges many types of tokens (about
800 types), Curve Finance is used for exchanging stablecoins, etc.?

Sushiswap
Outside: Number of transactions
Inside: Transaction amount (M$)

Curve Finance67

4-3. Results of Financial Stability-Related Data Analysis
4-3-3. Major VASPs (VASP-A)(4) Major VASPs: VASP-A by Token
Figure 4-3-3-4 VASP-A number of transactions / amount by token

Transaction Category

Table 4-3-3-4 VASP-A transactions / amount data by token(1) Incoming

VASP-A

Token(2)

Other VASP, DeFi,
Unhosted Wallet,
etc.

(1)

VASP-A

Other VASP, DeFi,
Unhosted Wallet,
etc.

(3)

Number of incoming transactions / amount
Outside: Number of transactions
Inside: Transaction amount (M$)

ETH

LINK

(3) Outgoing

Classification Transacti Transacti Amount Transacti Transacti Amount Transacti Transacti Amountons
on ratioM$
ons
on ratioM$
ons
on ratioM$

ETH

Native Token

72,09516.0%

18,416 1,581,015

71.9% 196,040 557,875

76.1%

9,845

USDT

Stablecoins

32,8497.3%

16,50984,191

3.8% 164,185

15,463

2.1%

16,135LINK

For External
Oracles

12,7442.8%

513

15,3170.7%

84,536

4,769

0.7%

9,736

USDC

Stablecoins

12,3192.7%

14,47018,349

0.8%

4,396

10,120

1.4%

336

GALA

For use inGames

10,3372.3%

208

11,500

0.5%

2,300

3,924

0.5%

89

309,34468.8%

15,740 488,293

22.2% 173,949 140,735

19.2%

7,473

449,688

100.0%

65,856 2,198,665

100.0% 625,406 732,886

100.0%

43,613Other
USDT(2) Within VASP-A

Total

-

*1) "Other" in incoming refers to the number of transactions for approximately 1,100 types of tokens.

USDC
Other

GALA

Number of outgoing transactions / amount
Other

Outside: Number of transactions
Inside: Transaction amount (M$)[Discussion]•

ETH, USDT, and USDC have the large number and amount of transactions.
→ This may be due to the fact that these tokens are often used as major tokens and exchanged
for other tokens, etc.

•

ETH has the largest number and amount of transactions in “(1) Incoming” and ”(2) Within VASPA”. In “(3) Outgoing”, ETH has the highest number of transactions, USDT has the largest
transaction amount. USDT has a higher amount per transaction than the others.
→ ETH and USDT are considered to be used more for fund transfers with other VASPs.

•

LINK (external oracle service Chainlink use token) has a large number of transactions.
→ This is considered to be due to the large number of DeFi that use Chainlink, an external oracle
service.

•

GALA (Gala Games‘ game usage token) has a next large number o transactions.
→ Possibly due to the use of this game (Gala Games: total supply of 39 billion tokens and
230,000 addresses of token holders as of May 2011).

GALA
USDC
LINK

USDT

ETH

68

4-3. Results of Financial Stability-Related Data Analysis
4-3-3. Major VASPs (VASP-B)(5) Major VASPs: VASP-B by Category
Figure 4-3-3-5 VASP-B number of transactions / amount by category

Transaction Category

Table 4-3-3-5 VASP-B transactions / amount data by category
Transaction
Category

VASP-B(2)(1)

Other VASP, DeFi,
Unhosted Wallet, etc.

VASP-B(3)

Other VASP, DeFi,
Unhosted Wallet, etc.

(1)
Incoming

Number of transactions/amount
Outside: Number of transactions
Inside: Transaction amount (M$)

(2) Within
VASP-B(3)
Outgoing
Incoming

Within VASP-B

Outgoing

Incoming
Unhosted
WalletUnhosted Wallet

DeFi
DeFi

VASP

VASP

Number of transactions
Transactions

Amount of transaction

Transaction ratio

Amount M$

Amount Ratio

VASP

115,177

92.1%

DeFi

4,673

3.7%

123

0.1%

Unhosted Wallet

4,635

3.7%

65,944

79.2%

Token Contract
Bridge
Other

306
192
6

0.2%
0.2%
0.0%

0
1
0

0.0%
0.0%
0.0%

Total

124,989

100.0%

83,259100.0%

VASP

999,058

100.0%

97,707100.0%

Total

999,058

100.0%

97,707100.0%

VASP

140,704

82.8%

13,655

64.9%

DeFi

482

0.3%

2,583

12.3%

Unhosted Wallet

22,307

13.1%

4,514

21.5%

Token Contract
Bridge
Other

5,320
1,072
51

3.1%
0.6%
0.0%

1
279
0

0.0%
1.3%
0.0%

169,936

100.0%

21,032100.0%

Total

Outgoing

Outside: Number of transactions
Inside: Transaction amount (M$)

Account Category

17,19020.6%[Discussion]• “(2) Within VASP-B” is the largest number of transactions within transaction categories.
→ Most of them are due to internal fund transfers of wallets within VASP.
• Other than within-VASP-B transactions, the majority of transactions with other VASPs accounted
for both “(1) Incoming” and “(3) Outgoing” transactions.
→ Most of the funds are considered to be transferred to other major VASPs.
• The next large number of transactions are DeFi and Unhosted Wallets for “(1) Incoming” andUnhosted Wallets for “(3) Outgoing”.
→ DeFi is the token exchange for decentralized exchanges, while Unhosted Wallets are
considered for DeFi use, etc.?
• Unhosted Wallets is the largest amount of transactions within “(1) Incoming”.
→ Could this mean that funds from the VASP wallet were used for DeFi services, etc. through a
Unhosted Wallet?
69

4-3. Results of Financial Stability-Related Data Analysis
4-3-3. Major VASPs (VASP-B)(6) Major VASPs: VASP-B DeFi breakdown among Category
Figure 4-3-3-6 VASP-B number of transactions / amount by DeFi

Table 4-3-3-6 VASP-B transactions / amount data by DeFi

Transaction Category(1) Incoming
DeFi
(1)

DeFi

VASP-B(3)

DeFi

Number of incoming transactions / amount
Curve FinanceSushiswap

Service

Transacti
onsTransacti
on ratioAmountM$

Transacti
onsTransacti
on ratioAmountM$

Uniswap

Decentralized
Exchange

4,457

95.4%

19

5

1.0%

0

Sushiswap

Decentralized
Exchange

121

2.6%

3

4

0.8%

0

BlockFi

Lending
Platform

76

1.6%

30

326

67.6%

2,583

Curve Finance

Decentralized
Exchange

0

0.0%

0

128

26.6%

0

Zappar Finance

DeFi Dashboard

0

0.0%

0

14

2.9%

0

-

19

0.4%

0

5

1.0%

0

4,673

100.0%

52482

100.0%

2,583

BlockFi
Outside: Number of transactions
Inside: Transaction amount (M$)(3) Outgoing

Other
total amount
Uniswap

Number of outgoing transactions / amount
Zappar Finance

Curve Finance

Uniswap
Sushiswap[Discussion]•

The large number of transactions are decentralized exchanges for "(1) Incoming" and lending
platform for "(3) Outgoing".
→ Decentralized exchanges are considered to be due to the exchange of crypto assets andremittance of staking, while lending platform is considered to be due to the use of crypto asset
lending services.

•

Among decentralized exchanges, Uniswap for “(1) Incoming” and Curve Finance for “(3)Outgoing” have large number of transactions.
→ Uniswap is used for exchanging many types of tokens (about 800 types), and Curve Financeis used for exchanging stablecoins?

Outside: Number of transactions
Inside: Transaction amount (M$)

BlockFi70

4-3. Results of Financial Stability-Related Data Analysis
4-3-3. Major VASPs (VASP-B)(7) Major VASPs: VASP-B by Token
Figure 4-3-3-7 VASP-B number of transactions / amount by token

Transaction Category(1) Incoming

VASP-B

Token(2)
Other VASP, DeFi,
Unhosted Wallet, etc.

(1)

Other VASP, DeFi,
Unhosted Wallet,
etc.

(3)

VASP-B

Number of incoming transactions / amount
Outside: Number of transactions
Inside: Transaction amount (M$)

Table 4-3-3-7 VASP-B transactions / amount data by Token

ETH
USDC

Classification

Other

VGX

(3) OutgoingTransacti Transacti Amount Transacti Transacti Amount Transacti Transacti Amountons
on ratioM$
ons
on ratioM$
ons
on ratioM$

ETH

Native token

9,804

7.8

4,723 848,157

84.9%

9,345

46,376

27.3%

5,155

USDC

Stablecoins

20,433

16.3%

74,86645,016

4.5%

82,798

14,612

8.6%

9,249

USDT

Stablecoins

5,957

4.8%

1,636

16,371

1.6%

3,031

14,333

8.4%

3,541

VGX

VASP-issued
tokens

3,922

3.1%

47

3,411

0.3%

54

2,201

1.3%

14

LINK

For External
Oracles

3,563

2.9%

203

5,038

0.5%

391

6,424

3.8%

233

81,310

65.1%

1,713

81,065

8.1%

2,100

85,990

50.6%

2,845

124,989

100.0%

83,188 999,058

100.0%

97,719 169,936

100.0

21,038Other
USDT(2) Within VASP-B

Total

-

*1) "Other" in incoming refers to the number of transactions for approximately 600 types of tokens.

LINK

Number of outgoing transactions / amount
Outside: Number of transactions
Inside: Transaction amount (M$)

ETH

[Discussion]•

ETH, USDT, and USDC have a large number of transactions in all transaction categories.
→ This may be due to the fact that these tokens are often used as major tokens and exchanged
for other tokens.

•

VGX (tokens issued by VASP Voyager) has a large number of transactions.
→ Probably due to fund transfers between major VASPs.
(As of May 2011: total supply of 290 million tokens, 6,500 addresses of token holders)•

LINK (external oracle service Chainlink use token) has a next large number of transactions.
→ This is considered to be due to the large number of DeFi that use Chainlink, an external oracle
service.

Other
USDC
USDT
LINK

VGX

71

4-3. Results of Financial Stability-Related Data Analysis
4-3-4. Major Lender(1) Major Lending Platform: By Category
Figure 4-3-4-1 Lending Platform number of transactions / amount by category

Transaction Category

Table 4-3-4-1 Lending Platform transactions / amount data by category
Transaction
Category

Lending
Platform(2)

Other VASP, DeFi,
Unhosted Wallet,
etc.

(1)

Lending
Platform(3)

Other VASP, DeFi,
Unhosted Wallet, etc.

(1)
Incoming

Number of transactions/amount
Outside: Number of transactions
Inside: Transaction amount (M$)

(2) Within
theLending
Platform(3)
Outgoing

Incoming

Within The Lending Platform

Outgoing

Account Category
VASP
DeFi
Unhosted Wallet
Token Contract
BridgeOther
Total
Lending Platform
Total
VASP
DeFi
Unhosted Wallet
Token Contract
Bridge
Other
Total

Number of transactions
Transactions
Transaction ratio
12,841
81.2%
2,727
17.2%
174
1.1%
50
0.3%
12
0.1%
10
0.1%
15,814100.0%
126,946
100.0%

Amount of transaction
Amount M$
Amount Ratio
6,807
99.7%
6
0.1%
15
0.2%
0
0.0%
0
0.0%
0
0.0%
6,828
100.0%
8,015
100.0%

126,946

100.0%

8,015

100.0%

782
0
256
224
332
103
1,697

46.1%
0.0%
15.1%
13.2%
19.6%
6.1%
100.0%

177
0
141
0
21
73
412

43.0%
0.0%
34.2%
0.0%
5.1%
17.6%
100.0%[Discussion]
Unhosted
WalletIncoming

Outgoing•“(2) Within The Lending Platform” is the largest number of transactions within transaction
categories.
→ Likely due to internal fund transfers of wallets within their own services.

•

Among the transaction categories, the number of transactions in “(1) Incoming” is larger thanthat in “(3) Outgoing”.
→ The number of transactions in “(1) Incoming” is considered to be higher due to the use of
crypto asset lending platform, while the number of transactions in “(3) Outgoing” is considered to
be lower due to the withdrawal of funds.

•

By category, the number of transactions with other VASPs is large for both “(1) Incoming” and“(3) Outgoing”. The next large number of transactions were DeFi for “(1) Incoming” andUnhosted Wallets such as custodians, etc. for “(3) Outgoing”.
→ Although the background is not necessarily clear, it is possible that the use of staking services
for crypto assets, for example, is a possible purpose of utilization.

Outside: Number of transactions Other
Inside: Transaction amount (M$)

DeFi
BridgeVASP

TokenContract
VASP

Unhosted
Wallet72

4-3. Results of Financial Stability-Related Data Analysis
4-3-4. Major Lending Platform(2) Major Lending Platform: DeFi Breakdown among Category
Figure 4-3-4-2 Lending Platform number of transactions / amount by DeFi

Table 4-3-4-2 Lending Platform transactions / amount data by DeFi

Transaction Category(1) Incoming(1)

Lending
Platform

DeFi

Service

Uniswap

Decentralized
Exchange

Maker
Compoundtransactions

Transaction
ratio

amount M$

2,721

99.8%

6

Stablecoins Issuance

5

0.2%

0

Lending

1

0.0%

0

2,727

100.0%

6

(3)
DeFi

Number of incoming transactions / amount
Maker

DeFi

Outside: Number of transactions
Inside: Transaction amount (M$)

total amount*”(3) Outgoing” is notapplicable transaction.

[Discussion]•“(1) Incoming” are mostly traded on decentralized exchanges.
→ This is considered to be due to multi token transfers (approx. 300 types).

•“(1) Incoming” and “(3) Outgoing” are different.
→ This is considered to be due to the large number of transactions on decentralized exchanges.

Uniswap73

4-3. Results of Financial Stability-Related Data Analysis
4-3-4. Major Lending Platform(3) Major Lending Platform: By Token
Figure 4-3-4-3 Lending Platform number of transactions / amount by DeFi

Transaction Category

Table 4-3-4-3 Lending Platform transactions / amount data by Token
Token

(2)
(1)

Other VASP, DeFi,
Unhosted Wallet, etc.

Lending
Platform

Other VASP, DeFi,
Unhosted Wallet,
etc.

(3)

Number of incoming transactions / amount
ETH(2) Within The Lending(3) Outgoing
Platform
Classificationtransactio Transacti amount transactio Transacti amount transactio Transacti amountns
on ratioM$ns
on ratioM$ns
on ratioM$(1) Incoming

Lending
Platform

Outside: Number of transactions
Inside: Transaction amount (M$)

Other

ETH

Native token

215

1.4%

3,836

77,958

61.4%

2,346

995

58.6%

65

USDC

Stablecoins

7,941

50.2%

2,057

29,412

23.2%

3,784

392

23.1%

298

USDT

Stablecoins

1,478

9.3%

36

4,932

3.9%

994

174

10.3%

39

LINK

For External
Oracles

1,057

6.7%

78

2,614

2.1%

117

36

2.1%

2

GUSD

Stablecoins

750

4.7%

162

5,474

4.3%

476

12

0.7%

0

4,373

27.7%

659

6,556

5.2%

298

88

5.2%

8

15,814100.0%

6,828 126,946

100.0%

8,015

1,697

100.0%

412

Other
USDC

GUSD

Total

-

*1) "Other" in incoming is the number of transactions for about 300 types of tokens.

LINK
USDT

Number of outgoing transactions / amount
LINK
USDT

USDC

GUSD

Other

[Discussion]•

ETH, USDT, and USDC have a large number of transactions in all transaction categories.
→ This may be due to the fact that these tokens are often used as major tokens and exchanged
for other tokens.

•

LINK (external oracle service Chainlink use token) has a large number of transactions.
→ This is considered to be due to the large number of DeFi that use Chainlink, an external oracle
service.

•

GUSD (token issued by VASP Gemini) has a next large number of transactions.
→ This is considered to be due to fund transfers within VASPs.
(GUSD: total supply 540 million tokens, token holders 10,000,000 addresses as of May 23)

Outside: Number of transactions
Inside: Transaction amount (M$)

ETH

74

4-3. Results of Financial Stability-Related Data Analysis
4-3-5. Stablecoins Related•

While there were some trends among the three types of stave coins surveyed, in general, the overwhelming majority of stave coin use wasconfirmed to be on DeFi.

•

No actual cases of address freezing could be confirmed for DAI, an algorithmic stave coin (possibility that the freezing function does not exist).

•

Unhosted wallets were observed to have more destination addresses but fewer transactions in number and amount than DeFi and VASP. This may
be due to the fact that DeFi and VASP transactions are concentrated in specific remittance destinations (e.g., DEX major VASPs).
Table 4-3-5 Stablecoins Data Survey Results

Category

Surveyed Items

Actual status of stablecoin
remittance (main use cases,
remittance scale, etc.)

Stablecoins
RelatedData related to stave coinsfrozen by the issuing entity(addresses subject tofreezing, total amountfrozen, etc.)

Surveyed Data

On-chain data survey results

Remarks

USDC Stablecoins

USDT Stablecoins

DAI Stablecoins

Remittance Addresses

2,057 Addresses【Breakdown】
Unhosted Wallet 1,020,
Token Contract 698,
VASP 128, etc.

1, 657 Addresses【Breakdown】
Unhosted Wallet 689,
Token Contract 630,
VASP 151, etc.

899 Addresses【Breakdown】
Unhosted Ｗallet 321,
Token Contract 311,
VASP 105, etc.

Number of transactions andamount of transactions at
the above addresses

7,966,000 TRX/
1,402.1 billion USD【Breakdown】
DeFi
4.059 million TRX/
751.4 billion USD,
VASP
1.95 million TRX/
1.1 billion USD,
Unhosted Wallet
615,000 TRX/
138.1 billion USD, etc.

8,093,000 TRX/
236.2 billion USD【Breakdown】
VASP
5.109 million TRX/
2.6 billion USD,
DeFi
1.865 million TRX/
93.4 billion USD,
Unhosted Wallet
188,000 TRX/
21.5 billion USD, etc.

1,272,000TRX /509.5 billion USD【Breakdown】
DeFi
648,000 TRX/
428.2 billion USD,
VASP
193,000 TRX/
0.08 billion USD,
Unhosted Wallet
87,000 TRX/
19.3 billion USD, etc.

Addresses subject to freeze

159 addresses

858 addresses-

As of April 2023

Total frozen
amount/average amount
per address

7,859,000 USD/
21 addresses
Average 374,000 USD

440 million USD/
777 addresses
Average 567,000 USD-

As of April 202375

4-3. Results of Financial Stability-Related Data Analysis
4-3-6. DeFi Related(1) DeFi related
Table 4-3-6-1 Results of DeFi data survey (1/2)
On-chain data survey results
Category

Surveyed Items

Surveyed Data

Maker Stablecoin
Issuance

Aave
Lending Protocol

4.09 billion USD (UNI)

7.23 billion USD (DAI)USD 5.18 billion (AAVE)

As of May 2023

(Listed in the GovernanceToken column)

507,000 addresses (DAI)(Listed in the GovernanceToken column)

As of May 2023

Market capitalization of
tokens/stablecoins

2.94 billion USD (UNI)

4.98 billion USD (DAI)

940 million USD (AAVE)

As of May 2023

Number of Governancetoken holder addresses

370,000 addresses (UNI)

95,000 addresses (MKR)

161,000 addresses (AAVE)

As of May 2023

Number of transactions bygovernor token holder
address

Incoming: 2,137,000 TRX
Outgoing: 2,105,000 TRX

Incoming: 1,139,000 TRX
Outgoing: 1,121,000 TRX

Incoming: 966,000 TRX
Outgoing: 1,020,000 TRX

Oracle Service usage trends
by DeFi

Provide oracle functionality
within the project (TWAP:
Time Weighted Average
Price)

Provide oracle functionality
within the project (oracle
price feed)

Use of external oracle
services (Chainlink)

As of April 2023

Collateral ratios based oncollateral type-19 types
102% to 5,000%

10 types
125% to 200%

As of April 2023

Leverage ratio(Leverage ratio = total
debt/total assets)-

6 types of tokens
96.6% to 99.9%

14 types of tokens
0.4% to 77.9%

As of April 2023

TVL per DeFi
Overall size of DeFi (TVL,
number of users, market
capitalization of stabledcoins, etc.)

DeFi
RelatedDeFi vulnerabilities (e.g.,
governance tokens and
DeFi protocol
concentration)

Degree of concentration on
specific Oracle services

Lending Protocol related
data

Remarks

Uniswap Decentralized
Exchange

Number of token/stablecoinholders76

4-3. Results of Financial Stability-Related Data Analysis
4-3-6. DeFi Related(1) DeFi related
Table 4-3-6-1 Results of DeFi data survey (2/2)
Category

Surveyed Items

Surveyed Data
Cross-chain bridge address

Data Survey Results(on-chain/off-chain)
18 addresses

Remarks[Main Cross-Chain Bridges] As of 2023/4
Cross-chain bridge

Actual usage of cross-chain bridges (total
amount of tokens locked, transactional
relationships with VASPs, etc.)

Total amount of tokens locked
Number of transactions with VASPs at theabove address

DeFi
RelatedActual damage from DeFi-related hacking(total amount of damage, number of cases,
etc.)
Actual linkages between the traditional
financial sector and DeFi (e.g., the amountinvested in traditional financial assets using
tokens locked to smart contracts as
collateral)
Degree of concentration on specific oracle
services
DEX-related data (e.g., liquidity of major
token pairs, entities with close trading
relationships with the DEX)

Identification of hacked DeFi

9.50 billion USD

10,270,000 addresses

Polygon Bridges
Arbitrum Bridges
Avalanche Bridge
Optimism Bridges
Ronin Bridge

Total amount oftokens locked
3.30 billion USD
2.29 billion USD
1.53 billion USD
1.28 billion USD
700 million USD

10 hacking incidents occurred

For accruals in 2022

Total amount of hacking

2.49 billion USD

For accruals in 2022

Addresses held by financial institutions

155 addresses

Information held by blockchain analytics
company

Number of transactions linked to financial
institutions and DeFi

13,252 transactions

External oracle services and number of
DeFi used

Chainlink: 263 projects
TWAP (provided by Uniswap): 78 projects
Chronicle: 2 projects

Liquidity of major token pairs

Major token pairs: 31 pairs(Uniswap: WETH, USDC, USDT, DAI,
MATIC, etc.)
Total TVL: 10.13 billion USD

77

4-3. Results of Financial Stability-Related Data Analysis
4-3-6. DeFi Related(2) Rehypothecation (collateral diversion)
Table 4-3-6-2 DeFi (Rehypothecation) Data Survey Results
Blockchain analytics company research results
Category

DeFi
RelatedSurveyed Items

Lending Protocol
Related Data

Surveyed Data

The reality ofrehypothecation(collateral diversion)

Data Source

Data Acquisition
MethodEtherscan
Dune Analytics
In-house database

Obtain loan balanceswhere collateral fromthe surveyed lending
protocols ware reusedby other lending
protocols(as of 2022/12)*Only tokens issued byown project are eligible(DAI/AAVE)

Ratio of collateral / rehypothecation amount(2022/12 cross section)(1) Funds Locked(1) Funds locked 9.85 billion USD*.

(2) DAI collateralCollateral Reuse(3) DAI
rehypothecation
Other Lending Protocols
CompoundAave v2, etc.

Aave

(Not applicable)

Total: 347 million USD【Breakdown】
Compound
257 million USD
Aave v2
78 million USD
Aave v1
4.7 million USD, etc.

Total: 163,081 USD【Breakdown】
Maker 147,935 USD
Euler Finance
14,611 USD, etc.

Maker (DAI token)

Maker

Investors

Maker*Rehypothecation amount is the value of the 2022/12 cross section[Maker (DAI) Rehypothecation]

Collateral Deposit/
Token Lending

Uniswap(2) DAI collateral
5.78 billion USD (58.6%)(3) DAI rehypothecation
347 million USD (3.5%)*The amount of funds locked was calculated from
(2) the amount of collateral and the collateral ratio(170%).

Aave (AAVE token)

Rehypothecati
on Lender

Amount USD

Rehypothecati
on Lender

Compound

256,862,012

Maker

147,935

Aave v2

77,652,885

Euler Finance

14,611

Aave v1

4,748,882

Idle Cash

Euler Finance

3,807,531

Total amount

DyDx

1,613,278

Cream Finance

1,401,051

Yearn Finance

662,674

Idle Cash

70,541

Total amountAmount USD

535
163,081

346,818,854

Source: Dai Stats https://daistats.com/#/784-3. Results of Financial Stability-Related Data Analysis
4-3-7. Main Findings
Table 4-3-7 Main Findings

Main Findings

Contents

A certain amount of data isavailable that may be useful inassessing financial stability
impacts.

• It is believed that a certain amount of data that could serve as a starting point for further risk analysis and evaluation could bepresented in this initial data analysis, including the transaction relationships among entities such as VASPs and the actual
situation of rehypothecation in DeFi.
• Regarding the availability of data as indicated in the FSB report, it was confirmed that in some cases data that the FSB indicatedwere available were difficult to obtain for this study, while in other cases data that the FSB indicated were not available wereconsidered to be available for this study (but some data were limited).

The need for analysis using
multiple data sources and theeffectiveness of expert
research• The type of data that can be obtained and its reliability varies depending on the data source. For example, BC Explorer canobtain data on transactions and balances on the blockchain, but token prices are difficult to obtain. On the other hand, crypto
asset-related databases can obtain market data such as crypto asset prices, but transactions on the blockchain are difficult toobtain. Blockchain analysis tools are mainly designed to collect information on high-risk VASPs and trace high-risk transactions,making it difficult to obtain some data related to stablecoin and DeFi (other analysis tools may be able to obtain data related tostablecoin and DeFi).
• It was confirmed that many survey items for which it is difficult to obtain data with analytical tools can be obtained with expert
research (but only some data are limited).
• Where there are data sources other than those utilized in this study, such as data obtained through supervisory responses, it
may be necessary for the financial authorities to ensure accessibility to multiple data sources and to monitor implications forfinancial stability.

Data contributing to theanalysis of interconnectedness
with the existing financial
system is difficult to obtain• In terms of the exposure of financial institutions and the actual status of payment use, not much data was available from thesurvey results. As a background, it is considered that off-chain data (e.g., information on custodians to whom financial
institutions entrust digital assets, transaction and price data for payment use in e-commerce, etc.), which is difficult to obtainfrom public information, is necessary to grasp the actual status of these transactions. It is possible that many of these data are
not currently held by blockchain analytics companies.
• On the other hand, there is a possibility that data can be obtained through supervisory measures (such as requesting reports
from financial institutions) (which were not the subject of this survey), and it would be desirable for the authorities to explore
various methods to strengthen monitoring capabilities.
794-4. Results of AML/CFT-Related Data Analysis
4-4-1. Availability of Data as Indicated by the FATF Report•

With regard to the points raised in the FATF report, the results of an investigation into the possibility of data obtaining using various tools andexpert research confirmed that, among the data examined in this research, some data, such as conversion to legal tender and crypto asset
ATMs/kiosks, are difficult to obtain, but a certain amount of data that could potentially be obtained.

•

Note that the results of this survey are only localized based on the analytical tools used in this research and the results of expert research.

(1) VASP data availability
Table 4-4-1-1 Data availability for VASP (1/3)

VASP

Categ
ory

〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed Items
Investigation oftransaction trends of
relevant walletaddresses of major
VASPs (including
understanding of actual
management conditions
such ascentralized/decentralize
d management andintra-grouptransactions)
Major VASPs andrelated entities(institutional investors,
financial institutions,etc.) and their actual
transactions

Surveyed DataBC Explorer

Crypto Asset
Related database

Blockchain
analytics tools

Research by experts

Intra-group addresses
held by major VASPs△
A certain number ofaddresses identified as
VASPs can be obtained.

×
Difficult to obtain data△
A certain number ofaddresses identified as
VASPs can be obtained.

△
A certain number ofaddresses identified as
VASPs can be obtained.

Number of transactionsand value oftransactions at theabove addresses△
Transaction data foreach VASP and
identified address canbe obtained for eachtransaction(aggregation is difficult)×
Difficult to obtain data△
Transaction data foreach VASP and
identified address canbe obtained for eachtransaction(aggregation is difficult)△
A certain number oftransaction data for
VASP and identified
addresses can beobtained.

Addresses of
institutional investors
and financial institutions▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

Remarks

Targets are addresses
identified as VASP byblockchain analytics
companies

Targets are addresses
identified as institutional
investors, etc. byblockchain analytics
companies80

4-4. Results of AML/CFT-Related Data Analysis
4-4-1. Availability of Data as Indicated by the FATF Report(1) VASP data availability
Table 4-4-1-1 Data availability for VASP (2/3)

VASP

Categ
ory

〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed Items

Actual transactions
between VASPDeFi/unhosted wallets

Surveyed DataBC Explorer

DeFi Address△
A certain number ofaddresses identified
with DeFi can beobtained.

Unhosted Wallet
Address▲
Limited to someaddresses such as
identified by accountname, etc.

Number of transactionsand value oftransactions at theabove addresses△
Transaction data for
DeFi and unhosted
wallets and identified
addresses can beobtained around one
transaction (difficult toaggregate).

Crypto Asset
Related database

Blockchain
analytics tools

Research by experts

×
Difficult to obtain data△
A certain number ofaddresses identified
with DeFi can beobtained.

△
A certain number ofaddresses identified
with DeFi can beobtained.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data△
Transaction data for
DeFi and unhosted
wallets and identified
addresses can beobtained around one
transaction (difficult toaggregate).

△
A certain number oftransaction data for
DeFi and unhosted
wallets and identified
addresses can beobtained.

Remarks

Targets are addresses
identified as DeFi or
unhosted wallets byblockchain analytics
companies81

4-4. Results of AML/CFT-Related Data Analysis
4-4-1. Availability of Data as Indicated by the FATF Report(1) VASP data availability
Table 4-4-1-1 Data availability for VASP (3/3)
Categ
ory

〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed Items

VASP

Identification of VASPrelated walletaddresses/transactions
considered high risk

Trends bylocation/region of VASPs(located in jurisdictions
with no registration or
inadequate regulatory
requirements)

Surveyed Data
Addresses determined
to be high risk (e.g.,
addresses used for
criminal activity in the
past)

Number of transactionsand value oftransactions at theabove addresses

BC Explorer

×
Difficult to obtain data

×
Difficult to obtain data

Crypto Asset
Related database

Blockchain
analytics tools

Research by experts

×
Difficult to obtain data△
A certain number ofhigh risk addresses
identified as VASPs canbe obtained.

△
A certain number ofhigh risk addresses
identified as VASPs canbe obtained.

×
Difficult to obtain data△
Transaction data foreach VASP and
identified address canbe obtained for eachtransaction(aggregation is difficult)△
A certain number oftransaction data for
VASP and identified
addresses can beobtained.

△
A certain number ofaddresses identified as
VASPs can be obtained.
△
A certain number ofidentified VASP locations
can be obtained.

Address of unregistered
VASP

×Difficult to obtain data

×
Difficult to obtain data▲
A certain number ofaddresses identified as
VASPs can be obtained.
Difficult to search for
unregistered VASPs

Trends in unregistered
VASPs by location andregion×
Difficult to obtain data

×
Difficult to obtain data△
A certain number ofidentified VASP locations
can be obtained.

Remarks

Targets are addresses
deemed high risk byblockchain analytics
companies

Rely on VASP
registration information
held by blockchain
analytics companies intheir own databases

82

4-4. Results of AML/CFT-Related Data Analysis
4-4-1. Availability of Data as Indicated by the FATF Report(2) Possibility of obtaining data from unhosted wallets
Table 4-4-1-2 Data availability for Unhosted Wallets
Categ
ory

Results of availability of data
Surveyed Items

Surveyed Data

Unhosted Wallet
Address

Unhosted Wallet (incl. P2P)〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

P2P Transaction Facts

Actual use of PET (e.g.,
mixing services)

Number of transactionsand value of total P2P
transactions

BC Explorer

×
Difficult to obtain data

×
Difficult to obtain data

Percentage ofFraudulent Transactions

×
Difficult to obtain data

Number of transactionsand value oftransactions for mixing
services▲
Limited to someaddresses such as
identified by accountname, etc.

Crypto Asset
Related database

Blockchain
analytics tools

Research by experts

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data▲
Limited to someaddresses such as
identified by accountname, etc.

▲
Limited to someaddresses such as
identified by accountname, etc.

Remarks

Targets are addresses
identified as unhosted
wallets by blockchain
analytics companies

Targets are addresses
identified as mixing
services by blockchain
analytics companies834-4. Results of AML/CFT-Related Data Analysis
4-4-1. Availability of Data as Indicated by the FATF Report(3) AML/CFT related data availability
Table 4-4-1-3 Data availability for AML/CFT (1/2)

AML/CFT related

Categ
ory

〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed Items

Surveyed Data

When converting large
amounts of legal tender
into large amounts ofcrypto assets

Addresses of users(accounts) thatexchange crypto assets
and legal tender×
Difficult to obtain data

Addresses that are
being misused▲
Limited to someaddresses such as
identified by accountname, etc.

crypto assets identified
as holding stolen funds
or receive funds thatare suspected to havebeen in the event

Transferring funds froma VASP with no or
inadequate customer
relationshipmanagement (CDD) or
identity verification(KYC) processes

Address of thecounterparty to theabove address

Identification of VASPs
that appear to have no
CDD/KYC process at thetime of joining the VASPBC Explorer▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data

Crypto Asset
Related database

Blockchain
analytics tools

Research by experts

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data△
A certain number oftarget addresses can beobtained (each address
can be obtained, but
aggregation is difficult)△
A certain number oftarget addresses can beobtained.

×
Difficult to obtain data△
A certain number oftarget addresses can beobtained (each address
can be obtained, but
aggregation is difficult)△
A certain number oftarget addresses can beobtained.

×
Difficult to obtain data△
A certain number of
VASPs can be obtained,
but it is difficult tosearch△The VASP for the
relevant condition isa certain number can beobtained

Remarks
Legal tender exchange
is mainly done by VASPs,
with the possibility ofdata acquisition by
supervisory authorities

Targets are addresses
identified as abusive byblockchain analytics
companies

Rely on VASP
registration information
held by blockchain
analytics companies intheir own databases

844-4. Results of AML/CFT-Related Data Analysis
4-4-1. Availability of Data as Indicated by the FATF Report(3) AML/CFT related data availability
Table 4-4-1-3 Data availability for AML/CFT (1/2)

AML/CFT related

Categ
ory

〇: All data can be obtained△: Data generally obtainable, but some difficult to obtain▲: Some data can be obtained, but only to a limited extent
×: No data can be obtained at all-: Not included in this survey

Results of availability of data
Surveyed Items

Surveyed Data

Use of crypto asset
ATMs/kiosks in locations
with a high risk ofincreased criminal
activity
Transactions at crypto
asset addresses
associated with fraud
and extortion,
ransomware schemes,
sanctioned addresses,
darknet marketplaces,or other illegal websites

Remarks

BC Explorer

Crypto Asset
Related database

Blockchain
analytics tools

Research by experts

Addresses using crypto
asset ATM/kiosk

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

×
Difficult to obtain data

Difficult to obtain dataon crypto assets
ATM/kiosk

Cryptographic asset
addresses associatedwith illegal websites▲
Limited to someaddresses such as
identified by accountname, etc.

×
Difficult to obtain data△
A certain number oftarget addresses can beobtained (each address
can be obtained, but
aggregation is difficult)△
A certain number oftarget addresses can beobtained.

Targets are addresses
identified as fraudulent,
extortion, etc. byblockchain analytics
companies85

4-4. Results of AML/CFT-Related Data Analysis
4-4-2. Research Survey Items•

In this chapter, four survey items were researched by experts from a blockchain analytics company, setting specific survey items such as thenumber and amount of transactions and the actual use of suspicious transactions by category, such as VASPs and lending platform.

•

The research results were organized into tables and graphs after organizing the data, and the trends and characteristics seen in the results werediscussed.

Table 4-4-2 Research Survey Items

Survey Items
Trends in high-risk transaction
ratios for major VASPs
Trends in high-risk transaction
ratios of major lending platform
Trends in high-risk Transaction
Ratios for Unhosted Wallets

Survey Contents

Supplement•

The two main crypto asset traders were surveyed by category for the number oftransactions, transaction value, and number of high-risk transactions in three categories:
incoming, within their own company, and outgoing.

•

The number of transactions, value of transactions, and number of high-risk transactions by
category were examined for one major lending platform in three categories: incoming,
within their own company, and outgoing.

••

The number of transactions, transaction value, and high-risk transactions for unhosted
wallets were examined by category, by breakdown of DeFi among categories, and by token,
for three categories: incoming, P2P (in unhosted wallets), and outgoing.

••

The survey of suspicious transactions included small-value transactions, consecutive highvalue transactions, mixing services, fraudulent/extortion and sanctioned addresses, online
gambling services, and other high-risk addresses.
The location and regional trends of unregistered VASPs that are not registered in thejurisdiction in which they are located were investigated.

AML/CFT related data•

Account category names and accountnames used classifications defined by theblockchain analytics companies.
The transaction amount was calculatedusing the token price and other rates asof April 2023.

864-4. Results of AML/CFT-Related Data Analysis
4-4-3. Major VASPs(1) Major VASPs: Summary of high-risk transactions for VASP-A/VASP-B•

High-risk transactions are mainly remittance from VASP-A/VASP-B and other VASPs (token transfers from high-risk addresses managed by
VASPs), all of which account for more than 70% of the total.
•However, under the definition of high-risk addresses and transactions, many VASP-related transactions are considered to be classified as "high-risk" (i.e., alarge number of transactions are sent and received at a small number of addresses managed by the VASP).
The actual situation of the addresses (which are likely to be classified as high-risk) and whether they reflect the actual situation requires close examination(details are provided on the next page and beyond).

A significant number of high-risk transactions with DeFi and unhosted wallets (including some large operators and funds) were also identified.
Figure 4-4-3-1 Summary of major VASPs high-risk transactions87

4-4. Results of AML/CFT-Related Data Analysis
4-4-3. Major VASPs (VASP-A)(2) Major VASPs: VASP-A By Category
Figure 4-4-3-2 VASP-A number of transactions / amount by category

Transaction Category
(1)

(3)

VASP-A

Other VASP, DeFi,
Unhosted Wallet,
etc.

Number of transactions & high-risk by category(1)
Incoming

Outgoing: Unhosted Wallet
Incoming: VASP
Incoming: DeFi

Outgoing: VASP

(2) Within
VASP-A

Incoming: Unhosted
Outgoing: Token Contract

HighRisk

WalletHighRisk(3)
Outgoing

HighRisk

Number of high-risk
Amount of transaction
transactions
Transactions High-riskHigh Risk
AmountTransactions
Amount M$
Ratiotransactions
Ratio
Ratio
334,182
74.3%
262,565
78.6%
51,23977.8%
77,71117.3%
30,158
38.8%
528
0.8%
34,776
7.7%
11,565
33.3%
13,791
20.9%
2,320
0.5%
1,864
80.3%
27
0.0%
566
0.1%
146
25.8%
148
0.2%
133
0.0%
42
31.6%
123
0.2%
449,688
100.0%
306,340
68.1%
65,856100.0％
2,198,665
100.0% 2,184,570
99.4%
625,406
100.0％
2,198,665
100.0% 2,184,570
99.4%
625,406
100.0％
195,055
26.6%
192,585
98.7%
27,079
62.1%
4,211
0.6%
3,465
82.3%
46
0.1%
26,576
3.6%
25,622
96.4%
15,87336.4%
496,83767.8%
481,607
96.9%
217
0.5%
3,451
0.5%
3,117
90.3%
398
0.9%
6,756
0.9%
6,157
91.1%
0
0.0％
732,886
100.0%
712,553
97.2%
43,613100.0％
Number of transactions

Transaction
Category

VASP-A(2)

Other VASP, DeFi,
Unhosted Wallet, etc.

Table 4-4-3-2 VASP-A transactions / amount data by category
Account Category
VASP
DeFi
Unhosted Wallet
Token Contract
Bridge
Other
Total
VASP
Total
VASP
DeFi
Unhosted Wallet
Token Contract
Bridge
Other
Total[Discussion]•“(2) Within VASP-A” is the highest ratio within High-risk transactions.
→ Transactions within the VASP are concentrated in transfers to specific high-risk addresses (5addresses).
Some addresses with high transaction volume are determined to be "high risk" based on theirtrack record of being used for fraud and crimes, and all transactions associated with suchaddresses may be classified as "high risk“.
All transactions related to the address may be classified as "high risk" (in reality, the majority oftransactions may not be problematic).

•

Next higher ratio are “(3) Outgoing”, followed by “(1) Incoming”.
→ As “(2) Within VASP-A”, it is possible that the number of transactions to a particular high-risk
address in VASP is high.

HighRisk

Outside: Number of transactions
Inside: High-Risk / Low-Risk transactions

Within VASP-A

88

4-4. Results of AML/CFT-Related Data Analysis
4-4-3. Major VASPs (VASP-B)(3) Major VASPs: VASP-B By Category
Figure 4-4-3-3 VASP-B number of transactions / amount by category

Transaction Category

VASP-B(1)

VASP-B(3)

Other VASP, DeFi,
Unhosted Wallet,
etc.

Number of transactions & high-risk by category
Outgoing: Token Contract

Outgoing: Incoming:
Other

VASP

Outgoing: Unhosted Wallet

Incoming: DeFi

(1)
Incoming

Incoming: Unhosted
WalletOutgoing: VASP

(2) Within
VASP-B

Incoming:
HighRisk

Other

HighRisk(3)
Outgoing

LowRiskAccount Category
VASP
DeFi
Unhosted Wallet
Token Contract
Bridge
Other
Total
VASP
Total
VASP
DeFi
Unhosted Wallet
Token Contract
Bridge
Other
Total[Discussion]•

High-risk transactions have the highest ratio of “(3) outgoing” within transaction categories.
→ “(3) Outgoing” may be due to the high number of transactions from certain high-risk
addresses (8 addresses) in the VASP.
It is possible that some addresses with high transaction volume are determined to be “high risk”
based on their track record of being used for fraud and crime, and that all transactions associatedwith such addresses and all transactions related to the address may be classified as "high risk".

•

Next, the ratios are high for “(1) Incoming” and “(2) Within VASP-B”.
→ As with “(3) Outgoing”, the number of transactions for certain high-risk addresses in VASP
may be high.

HighRisk

Outside: Number of transactions
Inside: High-Risk / Low-Risk transactions

Number of high-risk
Amount of transaction
transactions
Transaction High-riskHigh Risk
AmountTransactions
Amount M$
ratiotransactions
Ratio
Ratio
115,17792.1%
104,894
91.1%
17,190
20.6%
4,673
3.7%
4,039
86.4%
123
0.1%
4,635
3.7%
3,467
74.8%
65,944
79.2%
306
0.2%
304
99.3%
0
0.0%
192
0.2%
30
15.6%
1
0.0%
6
0.0%
6
100.0%
00.0%
124,989
100.0%
112,740
90.2%
83,259100.0%
999,058
100.0%
694,83869.5%
97,707100.0%
999,058
100.0%
694,83869.5%
97,707100.0%
140,704
82.8%
140,704
100.0%
13,655
64.9%
482
0.3%
361
74.9%
2,583
12.3%
22,30713.1%
22,259
99.8%
4,514
21.5%
5,320
3.1%
3,179
59.8%
1
0.0%
1,072
0.6%
1,072
100.0%
279
1.3%
51
0.0
34
66.7%
0
0.0%
169,936
100.0
167,609
98.6%
21,032100.0%
Number of transactions

Transaction
Category(2)

Other VASP, DeFi,
Unhosted Wallet, etc.

Table 4-4-3-3 VASP-B transactions / amount data by category

Within VASP-B

89

4-4. Results of AML/CFT-Related Data Analysis
4-4-4. Major Lending Platform(1) Major Lending Platform: By Category
Figure 4-4-1 Lending Platform number of transactions / amount by category

Transaction Category

Lending
Platform(1)

Lending
Platform(3)

Other VASP, DeFi,
Unhosted Wallet,
etc.

Number of transactions & high-risk by category
Outgoing: Bridge
Outgoing: Unhosted…

Outgoing:
Other

Incoming: VASP
Incoming: DeFi
Incoming: Unhosted

Outgoing: VASP

WalletHighRisk(1)
Incoming(2) Within
The
Lending
Platform

Incoming:
Other(3)
Outgoing

Number of high-risk
Amount of transaction
transactions
Transaction High-riskHigh Risk
AmountTransactions
Amount M$
ratiotransactions
Ratio
Ratio
12,841
81.2%
9,733
75.8%
6,807
99.7%
2,727
17.2%
501
18.4%
6
0.1%
174
1.1%
146
83.9%
15
0.2%
50
0.3%
40
80.0%
0
0.0%
12
0.1%
4
33.3%
0
0.0%
10
0.1%
9
90.0%
0
0.0%
15,814100.0%
10,43366.0%
6,828
100.0%
Number of transactions

Transaction
Category(2)
Other VASP, DeFi,
Unhosted Wallet, etc.

Table 4-4-1 Lending Platform transactions / amount data by category
Account Category
VASP
DeFi
Unhosted Wallet
Token Contract
BridgeOther
Total
Lending Platform

126,946

100.0

0

0.0%

8,015

100.0%

Total

126,946

100.0

0

0.0%

8,015

100.0%

782
0
256
224
332
103
1,697

46.1%
0.0
15.1%
13.2%
19.6%
6.1%
100.0

40
0
62
125
0
4
231

5.1%
0.0%
24.2%
55.8%
0.0%
3.9%
13.6%

177
0
141
0
21
73
412

43.0%
0.0%
34.2%
0.0%
5.1%
17.6%
100.0%VASP
DeFi
Unhosted Wallet
Token Contract
Bridge
Other
Total[Discussion]•

High risk transactions have the highest ratio of “(1) Incoming”. By category, other VASPs,
unhosted wallets, and token contracts have the high ratios.
→ For VASPs and token contracts, addresses with a high number of transactions may be more
likely to be used for fraud and crime. For unhosted wallets, is it possible that many transfers are
made from specific high-risk addresses?

•

There is no high-risk transactions of “(2) Within The Lending Platform”.
→ The number of transactions at the Lending Platform's address is smaller than that of VASPs,
etc., which may make it less likely to be used for fraud or crime.

LowRisk

Within The Lending Platform
Outside: Number of transactions
Inside: High-Risk / Low-Risk transactions90

4-4. Results of AML/CFT-Related Data Analysis
4-4-5. Unhosted Wallets(1) Unhosted Wallets: By Category
Figure 4-4-5-1 Unhosted Wallets number of transactions / amount by category

Transaction Category

Unhosted
Wallet(2)

DeFi, etc.
(excluding VASP)

(1)

(3)

Unhosted
WalletDeFi, etc. (excluding
VASP)

Number of transactions & high-risk by category
Outgoing: Other

Outside: Number of transactions
Inside: High-Risk / Low-Risk transactions

Outgoing: Bridge

Incoming: NFT

HighRisk

Outgoing: DeFi

Marketplace

HighRisk
HighRisk
HighRisk*MEV (Maximum Extractable Value) BOT:.
Automated execution software that analyzes the waitingtransaction pool (mempool) of the Ethereum blockchain andobtains value by front running, etc.

Table 4-4-5-1 Unhosted Wallets transactions / amount data by category
Number of high-risk
Number of transactions
Amount of transaction
Transacti
transactionson
Account Category Addresses
Transacti
High-risk
High Risk
AmountCategory
Transactions
Amount M$
on ratio transactions
Ratio
Ratio
NFT Marketplace
190
1,468,421 49.5%
1,433,197
97.6%
152
0.2%
DeFi
8,752
672,335 22.7%
653,168
97.1%
30,537
44.9%
Token Contract
1,753
373,899 12.6%
259,006
69.3%
22,37832.9%(1)Bridge895
271,5899.1%
265,74497.8%
10,17915.0%
Incoming
MEV BOT*
103
152,869
5.2%
11,550
7.6%
989
1.5%
Other
549
29,174
1.0%
11,861
40.7%
3,818
5.6%
Total
12,242
2,968,287 100.0%
2,634,526
88.8%
68,053 100.0%(2) P2P P2P Transactions
5,989
484,394 100.0%
425,882
87.9%
28,967 100.0%
Transacti
Total
5,989
484,394 100.0%
425,882
87.9%
28,967 100.0%
ons
NFT Marketplace77
1,088,944 46.9%
1,088,322
99.9%
1,083
2.1%
DeFi
4,799
674,524 29.1%
656,787
97.4%
22,87644.0%
Token Contract
1,361
248,009 10.7%
160,91764.9%
16,07730.9%(3)Bridge500
295,673 12.7%
275,22293.1%
10,14919.5%
Outgoing
MEV BOT*
27
3,963
0.2%
2,834
71.5%
344
0.7%
Other
329
9,179
0.4%
7,087
77.2%
1,498
2.9%
Total
7,093
2,320,292 100.0%
2,191,169
94.4%
52,027 100.0%[Discussion]

HighRisk

HighRisk

Outgoing: NFT
Marketplace

HighRisk

HighRisk

High risk transactions have the highest ratio of “(3) Outgoing”. By category, other VASPs,
unhosted wallets, and bridges have high ratios.
→ VASPs and bridges may be more likely to be used for fraud and crime, as addresses with ahigh number of transactions are more likely to be used for fraud and crime.
VASPs and bridges are likely to be used for fraud and crime.
Unhosted wallets may have a high number of transactions of certain high-risk addresses?

•

Next, the ratios are high for “(1) Incoming” and “(2) P2P transactions”.
→ This is considered that the ratio of transactions of high-risk addresses is high, as is the casewith “(3) Outgoing”?

Incoming: DeFi

HighRisk

Incoming: Token Contract
P2P Transactions•

Incoming: Other91

4-4. Results of AML/CFT-Related Data Analysis
4-4-5. Unhosted Wallets(2) Unhosted Wallets: DeFi breakdown among Category
Figure 4-4-5-2 Unhosted Wallets number of transactions / amount by DeFi

Table 4-4-5-2 Unhosted Wallets transactions / amount data by DeFi

Transaction Category(1) Incoming
DeFi

DeFi

(1)

Unhosted
Wallet(3)

DeFi

Uniswap

Number of incoming transactions / amount
Curve Finance

Balancer
Outside: Number of transactions
Inside: Transaction amount (M$)

Sushiswap

Service

Sushiswap
Curve Finance
BalancermStable
Other
Total

Decentralized
Exchange
Decentralized
Exchange
Decentralized
Exchange
Decentralized
Exchange
Stablecoins
Issuance-transactio
nsTransacti
on ratio(3) OutgoingamountM$transactio
nsTransacti
on ratioamountM$

532,196

79.2%

19,400532,203

78.9%

14,393113,024

16.8%

1,144

113,155

16.8%

869

23,941

3.6%

8,874

23,170

3.4%

7,348

1,279

0.2%

3

1,288

0.2%

2

897

0.1%

114

837

0.1%

81

998

0.1%

1,002

3,871

0.6%

183

672,335

100.0%

30,537

674,524100.0

22,876

Uniswap

Number of outgoing transactions / amount
Balancer
Curve FinanceSushiswap

Outside: Number of transactions
Inside: Transaction amount (M$)[Discussion]•

Both “(1) Incoming” and “(3) Outgoing” have a large number of transactions on decentralizedexchanges.
→ Likely to exchange of crypto assets, liquidity provision, and remittance of staking.

•

Of the decentralized exchanges, Uniswap has a large number of transactions for both “(1)Incoming” and “(3) Outgoing”.
→ Uniswap is considered a multi-type token exchange (approx. 700 types)•

The number of transactions “(1) Incoming” and “(3) Outgoing” is almost the same for each DeFi.
→ Details need to be scrutinized, but for example, is there a possible movement to transfer
tokens exchanged on decentralized exchanges to unhosted wallets?

Uniswap92

4-4. Results of AML/CFT-Related Data Analysis
4-4-5. Unhosted Wallets(3) Unhosted Wallets: By Token
Figure 4-4-5-3 Unhosted Wallets number of transactions / amount by Token

Transaction Category

UnhostedwalletTable 4-4-5-3 Unhosted Wallets transactions / amount data by Token
Token(1) Incoming(2) P2P Transactions(3) Outgoing
Classification transactio Transacti amount transactio Transacti amount transactio Transacti amountns
on ratioM$ns
on ratioM$ns
on ratioM$

ETH

Native token

2,076,058

69.9%

12,420 391,975

80.9%

843 1,386,001

59.7%

9,853

WETH

Native token

424,295

14.3%

9,219

28,025

5.8%

20,524 362,035

15.6%

7,439

USDC

Stablecoins

164,494

5.5%

16,29926,9085.6%

1,952 212,593

9.2% 14,020

USDT

Stablecoins

61,740

2.1%

8,053

5,655

1.2%

1,642

58,577

2.5%

6,769

DAI

Stablecoins

30,959

1.0%

3,503

4,241

0.9%

1,209

37,793

1.6%

3,255

210,741

7.1%

18,55927,5905.7%

2,797 263,293

11.3% 10,691

2,968,287

100.0%

68,053 484,394

100.0%

28,967 2,320,292

100.0% 52,027(2)
DeFi and others(excluding crypto
asset exchangers)(1)

UnhostedwalletDeFi and others(excluding crypto
asset exchangers)(3)

P2P transactions/amount
DAI
USDT

Other

Outside: Number of transactions
Inside: Transaction amount (M$)

USDC
WETH

Other
Total

-

*The number of transactions for about 800 types of tokens in "Other" for “(1) Incoming” and about 700 typesof tokens in "Other" for “(3) Outgoing.

ETH

Incoming
DAI
USDT
USDC

Other

Outside: Numberof transactions
Inside: Transaction
amount (M$)[Discussion]

Outgoing
Other•

ETH and WETH (tokens pegged 1:1 to ETH) has large number of transactions in all transaction
categories.
Next, there are many transactions of stablecoins such as USDC, USDT, DAI, etc.
→ This is considered to be due to their use in exchanges with other tokens (e.g. to fix theamount of money)•

WETH is not among the major VASPs or lending platform in terms of number and amount oftransactions, but has a high number and amount of transactions in the unhosted wallet.
→ WETH is an ERC-20 compliant token to facilitate the use of ETH for DeFi, NFT services, etc.,
and this is thought to be due to its high use by DeFi, etc.

DAI
USDT
USDC

WETH
ETH
ETH

WETH

93

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(1) Number of addresses associated with holders of stolen funds
Figure 4-4-6-1 Stolen funds number of addresses by category

Surveyed

Table 4-4-6-1 Stolen Funds addresses data by category
Category

Stolen funds.
DeFi, Wallet, etc.

Associated with the holder ofthe stolen funds
Identified
Account Name

Number of addresses by category
Large Investor(Whale)Token Contract

Other

Addresses

Ratio

VASP

1,567

37.4%

Unhosted Wallet

1,285

30.7%

DeFi

662

15.8%

Token Contract

246

5.9%

Large Investor(Whale)

216

5.2%

Other

215

5.1%

Total

4,191

100.0

Identification ofaddress holder

Addresses

Ratio

Identified address
holders

4,191

2.6%

Unknown address
holders

158,918

97.4%

Total

163,109100.0%

VASP[Discussion]
DeFi•

Of the addresses associated with the holders of the stolen funds, only 3% of all addresses wereable to identify the account names.
→ Even if the addresses could be identified by the databases of blockchain analytics company,
most of the account names could not be identified, suggesting that it is extremely difficult to
identify accounts from off-chain information and other sources.

•

Regarding the addresses that were identified, VASP addresses are the highest ratio. Unhosted
wallets were next higher ratio.
→ It is possible that VASP and Unhosted Wallet have many addresses that were used for crimes
and frauds in the past. However, since VASPs are considered to be relatively easy to identify, thismay not necessarily indicate an overall trend.

Unhosted Wallet*"Addresses associated with holders of stolen funds" used data on known attackers held by a blockchain analytics company.

94

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(2) Number of addresses using mixing services
Figure 4-4-6-2 Mixing services number of addresses by category

Surveyed

Table 4-4-6-2 Mixing services addresses data by category
Category

Mixing Services.
Address used
Identified
Account Name

Mixing Services

Number of addresses by category
Large Investor(Whale)bridgeOther

Addresses

Ratio

VASP

22,257

67.0%

Unhosted Wallet

6,304

19.0%

Token Contract

1,866

5.6%

Large investor(Whale)727

2.2%

Bridge

692

2.1%

Other

1,390

4.2%

Total

33,236100.0%

Identification ofaddress holder

Addresses

Ratio

Identified address
holders

33,236

0.4%

Unknown address
holders.

9,001,940

99.6%

Total

9,035,176

100.0%

Token Contract

Unhosted
Wallet

VASP[Discussion]•

The majority of mixing services related addresses identified were related to Tornado Cash.

•

Of the addresses that used mixing services, account names could be identified for 0.4% of thetotal.
→ Even if the addresses that used the mixing service could be identified through theinvestigation of on-chain transaction information, most of the account names could not be
identified, suggesting that it is extremely difficult to identify accounts from off-chain informationand other sources.

•

By category, VASP addresses are the highest ratio. Unhosted wallets are next higher ratio.
→ Possibility that VASP users or unhosted wallet owners are utilizing mixing services for some
purpose (including ML).

95

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(3) Number of transactions and amount of addresses associated with fraud and extortion, ransomware, sanctioned addresses
Figure 4-4-6-3 Fraud, etc. number of transactions / amount by category

Transaction Category

Other VASP, DeFi,
Unhosted Wallet, etc.

(1)

Addresses
associated withillegal websites(2)
Addresses
associated withillegal websites

Table 4-4-6-3 Fraud, etc. transactions / amount data by category
Category

Illegal address: 422 addresses

Other VASP, DeFi,
Unhosted Wallet,
etc.

(3)

Number of transactions/amount
Outside: Number of transactions
Inside: Transaction amount (M$)

DeFi
BridgeToken Contract
VASP
Unhosted Wallet
Other
Total
Identification ofaddress holder

Incoming

Incoming
Unhosted
WalletOther

Between Illegal Addresses

VASP(3) OutgoingTransactions

Transaction
ratio

Amount M$

Transactions

Transaction
ratio

Amount M$

Identified
address holders

25,83214.3%

303

12,173

13.7%

1,206

Unknownaddress holders

154,762

85.7%

3,002

76,399

86.3%

2,267

Total

180,594

100.0%

3,305

88,572100.0%

3,472

Unhosted Wallet[Discussion]•

The largest number and amount of transactions were “(2) Between Illegal Addresses”→ Possible that large number of internal transactions are executed by dummies to prevent
transactions from being identified. (e.g., mixing services)•

The largest number of transactions by category was DeFi for both “(1) Incoming” and “(3)Outgoing”. The largest transaction amount was Token Contract.
→ DeFi is considered to be token exchange by decentralized exchanges such as Uniswap for both“(1) Incoming” and “(3) Outgoing”, and token contract is considered to be by token transfer(mainly WETH, etc.).

Token
Contract
DeFi

Token
Contract

Bridge(1) Incoming

Outgoing

Outside: Numberof transactions
Inside:Transaction
amount (M$)

VASP

Outgoing(1) Incoming(2) Between Illegal Addresses(3) OutgoingTransactio Transactio Amount Transactio Transactio Amount Transactio Transactio Amountns
n ratioM$
ns
n ratioM$
ns
n ratio
M$
5,496
45.1%
23
9,902
38.3%
37
2,246
18.5%
323
9,544
36.9%
27
1,846
15.2%
584
4,187
16.2%
144
1,568
12.9%
177
910
3.5%
75
728
6.0%
62
903
3.5%
14
289
2.4%
37
386
1.5%
5
12,173100.0
1,206
92,730100.0
3,945
25,832
100.0
303

DeFi

Bridge96

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(4) Number and amount of transactions for addresses remittance from online gambling services
Figure 4-4-6-4 Gambling services number of transactions / amount by category

Table 4-4-6-4 Gambling services transactions / amount data by category

Transaction Category(3) Outgoing
Category

OnlineGambling
Service
9 Address(3)

Other VASP, DeFi,
Unhosted Wallet,
etc.

Number oftransactions

Transactions
Ratio

Amount M$

VASP

24,495

85.6%

8

Token Contract

3,961

13.8%

0

Unhosted Wallet

143

0.5%

13

Attacks, fraud, ransomware, etc.

Attacks, fraud, ransomware,
etc.

12

0.0

0

Unhosted Wallet

Total

28,611

100.0

21

Number of transactions/ amount by category

Token Contract
Identification of address
holder(3) Outgoing
Number oftransactions

Transactions
Ratio

Amount M$

Identified address holders

28,611

7.4% (1)21

Unknown address holders

357,354

92.6% (%)

874

Total

385,965

100.0

895

[Discussion]

Outside: Number of transactions
Inside: Transaction amount (M$)

VASP

•

By category, VASPs are the largest number of transactions.
→ Wallets of major VASPs are most likely to be used as a destination for money transfers foronline gambling services•

The next large number of transaction is token contract.
→ This is considered to be for the transfer of stablecoins (USDC, DAI, etc.)974-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(5) Number and amount of transactions for addresses transferred from the mixing service
Figure 4-4-6-5 Mixing services number of transactions / amount by category

Table 4-4-6-5 Mixing services transactions / amount data by category

Transaction Category(3) Outgoing
Category

Mixing Services(3)37
Address

Other VASP
Unhosted Wallets,
etc.

Number of transactions / amount by category
Attacks, fraud,…

VASP

Other

Unhosted Wallet

Token

Contract

Outside: Number of transactions
Inside: Transaction amount (M$)

Transactions
Ratio

Amount M$

Token Contract

9,341

47.1%

22

Bridge

9,183

46.3%

22

Unhosted Wallet

572

2.9%

5

Attacks, fraud, ransomware,
etc.

280

1.4%

5

VASP

157

0.8%

3

Other

287

1.4%

11

Total

19,820100.068

Identification of address
holderBridgeNumber oftransactions(3) Outgoing
Number oftransactions

Transactions
Ratio

Amount M$

Identified address holders

19,820

11.7%

68

Unknown address holders

148,879

88.3%

2,491

Total

168,699

100.0%

2,560[Discussion]•

By category, token contracts are the largest number of transactions.
→ Likely used for money transfers (mainly WETH, etc.) to access mixing services.

•

The next large number of transactions is Bridge (transferring funds to other chains).
→ This is considered to transfer of mixing funds on Ethereum blockchain to other blockchains.

984-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (1/9)
Research Results

Red Flag Indicators Related to Transactions

Category

Surveyed Items

Structuring VA
transactions (e.g.
exchange or transfer)in small amounts, or inamounts under record
keeping or reporting
thresholds, similar to
structuring cashtransactions

Making multiple highvalue transactions inshort succession, suchas within a 24 hour
periodSurveyed Data

Data Source

Data Obtaining
MethodData Obtaining
Results *1

Data Obtaining
Results *2

Etherscan
In-house database

Obtain addresses
where smalltransactions of lessthan 0.1 ETH (approx.
25,000 yen) were sent

65,482 addresses[Breakdown]
VASP 42,889,
Unhosted Wallet
11,362, etc.

27,013,172 addresses

Etherscan
In-house database

Obtain the number oftransactions andamount of money forthe above address

Number oftransactions:
69,669,928
Amount: 4.61 billion
USD

Number oftransactions:
167,267,783
Amount: 9.14 billion
USD

User (account) address

Etherscan
In-house database

Get addresses where
the same sender has
sent transactions of 10
ETH (approx. 18,000USD) or more in thelast 24 hours

3,351 addresses[Breakdown]
VASP 1,436,
Unhosted Wallet 846,
etc.

59,127 addresses

Number of transactionsand value oftransactions at theabove addresses

Etherscan
In-house database

Obtain the number oftransactions andamount of money forthe above address

Number oftransactions:2,631,151
Amount: 810.9 billion
USD

Number oftransactions:3,874,440
Amount: 1,202.7 billion
USD

Frequency oftransactions at theabove addresses

Etherscan
In-house database

Obtain the ratio of thenumber of transactions
by time for the abovetransactions

Within 1 hour: 88.4%,
1-12 hours: 10.1%,
13-24 hours: 1.5%-

User (account) address

Number of transactionsand value oftransactions at theabove addresses

Data held andinvestigative
potential ofsupervised entities

VASP stores CDD/EDD
information inassociation with theuser's address.
Is it possible to narrow
down and reportsuspicious transactions
with a higher degree ofcertainty by combining
with the left column?

*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)99

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (2/9)
Research Results

Red Flag Indicators Related toTransactions

Category

Surveyed Items

Surveyed Data

Data Source

Data Obtaining
MethodData Obtaining
Results *1

Data Obtaining
Results *2

Etherscan
In-house database

Get addresses thathave sent 10 ETH or
more transactions
within 24 hours of theaddress's first deposit

703 Addresses[Breakdown]
Unhosted Wallet 335,
VASP 151, etc.

213,668 addresses

Number of transactionsand value oftransactions at theabove addresses

Etherscan
In-house database

Obtain the number oftransactions andamount of money forthe above address

Number oftransactions: 2,164
Amount: 885 million
USD

Number oftransactions: 259,264
Amount: 74.56 billion
USD

Frequency oftransactions within acertain period of time
for the above
addresses

Etherscan
In-house database

Obtain the ratio of thenumber of transactions
by time for the abovetransactions

Within 1 hour: 36.5%,
1-12 hours: 57.5%,
13-24 hours: 6.0%

Number of transactions
at the above address

Etherscan
In-house database

Obtain the number oftransactions by time forthe above transactions

Within 1 hour: 813,
1-12 hours: 1,280,
13-24 hours: 132

User (account) address

Making multiple highvalue transactions to a
newly created or to apreviously inactive
account-

Data held andinvestigative
potential ofsupervised entities

VASP stores CDD/EDD
information inassociation with theuser's address.
Is it possible to narrow
down and reportsuspicious transactions
with a higher degree ofcertainty by combining
with the left column?

*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)100

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (3/9)
Research Results

Red Flag Indicators Related to Transactions

Category

Surveyed Items

Transferring VAs
immediately tomultiple VASPs

Accepting funds
suspected as stolen or
fraudulent depositing
funds from VA
addresses that havebeen identified asholding stolen funds,
or VA addresses
linked to the holdersof stolen funds

Surveyed Data

Data Source

Data Obtaining
MethodData Obtaining
Results *1

User (account)
identification

Etherscan
In-house database

Obtain wallets to send
to multiple VASPs

997 addresses[Breakdown]
Unhosted Wallet 731,
VASP 200, etc.

1,086 addresses

Identification of VASPs,
etc.

Etherscan
In-house database

Identify the VASPs sentby the above wallets

9,268 addresses-

Addresses that are
being misused

Etherscan
In-house database

Obtain addresses ofknown attackers

422 addresses--

163,109 addresses

VASP stores CDD/EDD
information inassociation with theuser's address.
Is it possible to narrow
down and reportsuspicious transactions
with a higher degree ofcertainty by combining
with the left column?

Address of thecounterparty to theabove address

Etherscan
In-house database

Obtain a wallet to send
to known attackers

4,191 addresses[Breakdown]
VASP 1,567,
Unhosted Wallet 1,285,
etc.

Data Obtaining
Results *2

Data held andinvestigative
potential ofsupervised entities
VASP stores CDD/EDD
information inassociation with theuser's address.
Is it possible to narrow
down and reportsuspicious transactions
with a higher degree ofcertainty by combining
with the left column?

*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)101

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (4/9)
Research Results

Red Flag Indicators Related to Transaction
Patterns

Category

Surveyed Items

A new user attempts
to trade the entirebalance of VAs, orwithdraws the VAs
and attempts to send
the entire balance off
the platform

Making frequenttransfers in a certain
period of time (e.g. aday, a week, amonth, etc.) to thesame VA accountSurveyed Data

Data Source

Data Obtaining
MethodData Obtaining
Results *1

Data Obtaining
Results *2

New User Accounts

Etherscan
In-house database

Get a new user for
VASP

137 addresses

57,415 addresses

All balance transactions
made by the above
accounts

Etherscan
In-house database

Calculate the user's
balance and obtain thetransaction thatempties the balance

175 transactions

75,993 transactions

User (account) address

Etherscan
In-house database

Obtain the same
address from multiple
addresses that havebeen remitted torepeatedly

106 addresses[Breakdown]
Fraud/Attackers 28,
VASP 23m
Unhosted Wallet 12, etc.

25,543 addresses

Number of transactionsand value oftransactions at theabove addresses

Etherscan
In-house database

Obtain the number oftransactions andamount of money forthe above address

Number oftransactions: 21,338
Amount: 50.87 million
USD

Number oftransactions: 1,688,075
Amount: 1.46 billion
USD

Frequency oftransactions at theabove addresses

Etherscan
In-house database

Obtain the ratio of thenumber of transactions
by time for the abovetransactions

Within 1 hour: 44.3%,
Within 24 hours: 54.7%,
1 day - 30 days: 1.0%-

Data held andinvestigative
potential ofsupervised entities

VASP stores CDD/EDD
information inassociation with theuser's address.
Is it possible to narrow
down and reportsuspicious transactions
with a higher degree ofcertainty by combining
with the left column?

*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)102

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (5/9)
Research Results

Red Flag Indicators Related toAnonymity

Category

Surveyed Items

VAs transferred to orfrom wallets that show
previous patterns ofactivity associated withthe use of VASPs that
operate mixing or
tumbling services or
P2P platforms
Transactions making
use of mixing andtumbling services,suggesting an intent to
obscure the flow ofillicit funds between
known walletaddresses and darknet
marketplaces

Surveyed Data

Address of VASP,
which operates
mixing and tumbling
services and P2P
platforms
Addresses such as
wallet (user account)where transactions
were made withmixing services, etc.

Addresses withtransactions using
mixing and tumbling
services.

Data Source

Data Obtaining
MethodData Obtaining
Results *1

Data Obtaining
Results *2

Etherscan
In-house database

Obtain VASP address
with mixing service

116 addresses-

Etherscan
In-house database

Obtain addresses ofhosted/unhosted
wallets, etc., wheremixing services, etc.,
have been transacted

33,236 addresses[Breakdown]
VASP 22,257,
Unhosted Wallet 6,304,
etc.

9,035,176 addresses

Obtain the address ofthe mixing service

37 Addresses
All Tornado Cash
*Related tools include
Railgun*OFAC sanctioned listof 90 addresses (26 ofwhich overlap)-

Etherscan
In-house database

Data held andinvestigative
potential ofsupervised entities

VASP stores CDD/EDD
information inassociation with theuser's address.
Is it possible to narrow
down and reportsuspicious transactions
with a higher degree ofcertainty by combining
with the left column?

*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)103

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (6/9)
Research Results

Red Flag Indicators Related to Anonymity

Category

Surveyed Items

Funds deposited orwithdrawn from a VA
address or wallet withdirect and indirect
exposure links to known
suspicious sources,
including darknet
marketplaces,
mixing/tumbling
services, questionablegambling sites, illegal
activities (e.g.
ransomware) and/or
theft reports

Receiving funds from or
sending funds to VASPs
whose CDD or knowyour-customer(KYC) processes aredemonstrably weak or
non-existent.

Surveyed Data

Data Source

Data Obtaining
MethodData Obtaining
Results *1

Data Obtaining
Results *2

Data identifying
known andsuspected sources of
information

Etherscan, DappRadar,
IC3, CISA, OFAC
In-house database

Obtain rogue walletaddresses linked to thedarknet from
DappRadar, IC3, CISA,
and OFAC

12 addresses
Identify the URL of the
Darknet Web and theaddress associated withit.

-

Addresses that havedone business withthe abovequestionable sources

Etherscan, DappRadar,
IC3, CISA, OFAC
In-house database

Obtain the above
fraudulent address andthe address where thetransaction took placeSent:
38 addresses/50 items
Received:
4 addresses/5 items

Sent:
38 addresses/50 items
Received:
143 addresses/
193 items

Identification of
VASPs that appear tohave no CDD/KYC
process at the timeof joining the VASP

Etherscan
In-house database

Obtain addresses of
VASPs that do notappear to have
CDD/KYC processes
when joining VASPs

788 Addresses*Get the address of a
VASP that is notdecentralized and notregistered in thelocation-

Data held andinvestigative
potential ofsupervised entities

If a VASP itself
identifies anunregistered VASP in
the process ofconducting due
diligence on acounterparty VASP, is it
possible to provide
information to theauthorities, etc.?

*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)104

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (7/9)
Research Results

Red Flag Indicators in the
Source of Funds or Wealth

Red Flag Indicators
about Senders orRecipients

Category

Surveyed Items

Surveyed Data

Data Source

Data Obtaining
MethodData Obtaining
Results *1

Data Obtaining
Results *2

Obtain unauthorized
wallet addresses linked
to the darknet from
DappRadar, Reddit
message boards, anddarknet forums

12 addresses-

Collect sanctioned
addresses published in
IC3, CISA, and OFAC

Sent: 50 addresses/
61 transactions
Received:
61 addresses/74
transactions

Sent: 629 addresses/
2,224 transactions
Received:
311 addresses/
580 transactions

Identify addresses
associated with fraud
and extortion,
ransomware schemes,
sanctioned addresses,
darknet marketplaces,or other illegal websites

Sent: 2,407 addresses/
117,650 transactions
Received: 142
addresses/
14,357 transactions

Sent:
56,235 addresses/
264,817 transactions
Received:
172 addresses/
91,344 transactions

Identify the address ofthe online gambling
service and obtain theaddress of thetransaction using that
address

Sent: Gambling
7 addresses,
User 257 addresses/
28,742 transactions
Received: Gambling
2 addresses,
User 2 addresses,
3 transactions

Sent: Gambling
13 addresses,
User 102,392
addresses/
386,225 transactions
Received: Gambling
11 Addresses,
User 113,524
addresses/
627,912 transactions

A customer’s VA
address appears on
public forums associatedwith illegal activity

Address of
unauthorizedtransactions

Etherscan, Reddit
Bulletin Board, Darknet
forums & marketplaces
In-house database

A customer is known via
publicly available
information to law
enforcement due toprevious criminal
association

Address of
unauthorizedtransactions

Etherscan, IC3, CISA,
OFAC
In-house database

Transacting with VA
addresses that areconnected to known
fraud, extortion, or
ransomware schemes,
sanctioned addresses,
darknet marketplaces,or other illicit websites

VA transactions
originating from ordestined to online
gambling services

Cryptographic asset
addresses associatedwith illegal websites

Addresses of users of
online gambling
services

CISO OFAC ,.
Threat Intelligence(Darknet), CISA, IC3 ,CSAM Data (Hades)
In-house database

Etherscan Word Cloud,ETHplorer.io
In-house database

Data held andinvestigative
potential ofsupervised entities

Is it possible for VASPs
themselves to provide
information to theauthorities by reporting
suspicious transactions,etc., if they discoverfraudulent transactions
by their customers?

*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)105

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (8/9)

Surveyed Items

Surveyed Data

A customer’s funds
which are sourced
directly from thirdparty mixing services
or wallet tumblers

Addresses withtransactions using
mixing and tumbling
services.

Bulk of a customer’ssource of wealth isderived frominvestments in VAs,
ICOs, or fraudulent
ICOs, etc.

Address of
unauthorizedtransactions

Red Flag Indicators Related
to Geographical Risks

Category

Red Flag Indicators in
the Source of Funds or
WealthResearch Results

Customer’s funds
originate from, or aresent to, an exchangethat is not registeredin the jurisdiction
where either thecustomer or exchange
is located

Addresses of exchanges
not registered in thejurisdiction in which
they are locatedUser addresses
transferred fromexchanges that are notregistered in yourjurisdiction

Data Source

Data Obtaining
MethodData Obtaining
Results *1

Data Obtaining
Results *2

Etherscan
In-house databaseobtain the remitted
address from themixing service

Mixing 37 addresses,
Recipients 769
addresses/
19,820 transactions

Recipients 53,268
addresses/
168,699 transactions

Etherscan
In-house databaseobtain addresses
transferred from known
fraudulent/fraudulent
addresses

Fraud/Illicit 147
addresses,
Recipients 59
addresses/
1,121 transactions

Fraud/Illicit 1,142
addresses,
Recipients 1,186
addresses/
4,742 transactions

Etherscan
In-house database

Obtain the address of
the VASP fortransactions transferredto the user from a VASP
that is not registered in
the jurisdiction in which
it is located

139 addresses-

Etherscan
In-house database

Obtain the user's
address for transactionstransferred to the userfrom a VASP that is notregistered in thejurisdiction in which it islocated8,275 addresses[Breakdown]
VASP 8,266 addresses(including hosted
wallets) 8,266,
Other 9, etc.

-

Data held andinvestigative
potential ofsupervised entities

Is it possible for VASPs
themselves to provide
information to theauthorities by reporting
suspicious transactions,etc., if they discoverfraudulent transactions
by their customers?

VASP stores CDD/EDD
information inassociation with theuser's address.
Is it possible to narrow
down and reportsuspicious transactions
with a higher degree ofcertainty by combining
with the left column?

*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)106

4-4. Results of AML/CFT-Related Data Analysis
4-4-6. AML/CFT Related(6) AML/CFT related
Table 4-4-6-6 AML/CFT data survey results (9/9)
Research Results

Red Flag Indicators Related
to Geographical Risks

Category

Surveyed Items

Customer sends funds
to VASPs operating injurisdictions that have
no VA regulation, or
have notimplemented
AML/CFT controls.

Surveyed Data

Identification ofjurisdictions requiring
attention

Data Source

Etherscan, OFAC,
In-house database

Data Obtaining
MethodObtain countries on the
OFAC Sanctioned
Countries List and U.S.
Military Export
Prohibited Countries
List

Data Obtaining
Results *1

49 countries/regions*(1) List of OFAC
sanctioned 25
countries/regions,(2) List of U.S. military
export prohibited
24
countries/regions*(1) and (2) include
duplicates.

Data held andinvestigative
potential ofsupervised entities

Data Obtaining
Results *2-•same as above•

VASPs themselves
should be utilized
after confirming that
there are nodifferences between
the high-riskcountries, etc.
defined by theblockchain analytics
company and thesettings on the toolside.

*[Jurisdictions requiring attention] 49 countries/regions(1) List of 25 OFAC sanctioned countries/regions https://ofac.treasury.gov/sanctions-programs-and-country-information
Afghanistan, Balkans, Belarus, Myanmar, Central Africa, China, Cuba, North Korea, Congo, Ethiopia, Hong Kong, Iran, Iraq, Lebanon, Libya, Mali, Nicaragua, Somalia, South Sudan, Sudan, Syria,
Russia, Venezuela, Yemen, Zimbabwe(2) List of 24 U.S. military export prohibited countries/regions https://www.ecfr.gov/current/title-22/chapter-I/subchapter-M/part-126/section-126.1
Belarus, Myanmar, China, Cuba, Iran, North Korea, Syria, Venezuela, Afghanistan, Cambodia, Central Africa, Cyprus, Congo, Ethiopia, Eritrea, Haiti, Iraq, Lebanon, Libya, Russia, Somalia, South
Sudan, Sudan, Zimbabwe*1: Data Obtaining Results: Survey results for addresses for which the blockchain analytics company identified category names and account names*2: Data Obtaining Results: All data obtained in this survey (including addresses for which category names, etc., were not identified)107

4-4. Results of AML/CFT-Related Data Analysis
4-4-7. Main Findings
Table 4-4-7 Main Findings

Main Findings

Contents

Certain usefulness of researchof experts compared toblockchain analytic tools• Regarding the possibility of acquiring the data pointed out in the FATF report, among the AML/CFT-related data examined in thisstudy, many of the data were difficult to obtain using analytical tools, but many of the data could be obtained using research byexperts. In addition to analytical tools, experts can also be used to understand AML/CFT-related data.
• However, since this study also confirmed the existence of a certain number of data that are difficult to obtain, it will benecessary in the future to clarify the possibility of obtaining such data, including the provision of information by supervisors.

Difficulty in high-risk
determination• Although many VASP-related transactions are classified as "high-risk" under the definition of high-risk addresses andtransactions (VASP-related addresses with an extremely large number of transactions are labeled as "high-risk"), it is highly
likely that many transactions related to such addresses are normal transactions, and it is difficult to draw a general conclusionfrom the results of this survey.
• Although many of the addresses with unidentified accounts may be considered to be unhosted wallets, the nature of the risk is
unknown, and it is extremely difficult to accurately understand the actual situation (e.g., volume of transactions) and assess therisk of P2P and other types of transactions.

Close interconnection between
VASP, Unhosted Wallet(including P2P), and DeFi

• In addition to transactions between VASPs, close business relationships existed between VASPs, unhosted wallets (including
P2P), and DeFi, and a significant number of high-risk transactions were identified (although difficult to define).
• In order to reduce ML/TF/PF risk for P2P and highly decentralized DeFi, which are not regulated in many jurisdictions, andtaking into account the difficulties of data analysis for unhosted wallets and P2P mentioned above, it is possible that stronger
controls on VASPs, which are relatively easier to deal with in terms of regulatory measures (such as EDDs for transactions
between VASPs and unhosted wallets, etc.) may be effective.

Need for efforts to improve thereliability of data and data
analysis• The results of this research confirmed that only about 10% of the Ethereum blockchain data had been identified by theblockchain analytics companies as category or account names, which means that the company was not necessarily able toanalyze data from the entire decentralized financial system.
• The category and account names identified, as well as the determination of high-risk transactions, all rely on information fromblockchain analytics companies, and since the identification of account names, etc. and the calculation of risk scores are done
independently by blockchain analytics companies, the content may differ from one analytics company to another.
• Therefore, when conducting a fact-finding survey, it is considered necessary to improve the reliability of the data by not relying
on data from a single company, for example, by comparing survey results from multiple analysis companies.
108

Chapter 5: Conclusion

10Chapter 5: Conclusion(1) Insights gained from conducting data analysis••

A certain amount of training is required for financial authorities to properly use analytical tools and researchers. It is also
necessary to understand the limitations of the current tools.
(It is not a silver bullet that will significantly improve monitoring capabilities.)

Sanitization of data obtained from tools and researchers (e.g., removal of outliers) and understanding of context (e.g.,that token contract-related transactions are not actual token transfers, but rather smart contract behavior associatedwith transfers between addresses (EOAs) such as stablecoins) are required.



Tool companies and solutions vary (some are specialized for investigative authorities, others are mainly used bybusinesses rather than authorities), and it is necessary to select the right tool for the right application, understand theoverall picture of tools, and gather information on the latest technology trends.

On the other hand, there is a possibility that the tools may provide useful insights into the interconnections in the crypto
asset market (e.g., connections between VASPs, unhosted wallets, and DeFi) and the actual state of illicit transactions,
including those not subject to regulatory oversight (DeFi, P2P, etc.), and the identification rate and accuracy of the tools will
improve in the future. Should the use of tools and researchers be explored, taking into account the possibility that theidentification rate and accuracy of tools will improve in the future?

(2) Initial Financial Regulatory Implications•

Given the close relationship between regulated entities (VASPs) and others (unhosted wallets, P2P, (fully decentralized)
DeFi), the possibility that strengthening controls over VASPs could indirectly reduce the risk of P2P and other unregulated
activities.

•

There are many smart contract related transactions on Ethereum, such as DeFi, ERC-20 related token contracts (e.g.
stablecoin), and bridges. Hacking, etc., has been frequent, and is improving security a major issue?

•

Although it is possible to identify addresses that appear to be involved in crimes, it is not easy to identify the actual owners,
etc. (The analysis tools used in this research were not intended for criminal investigations, but specialized analysis tools may
yield different results).
110Appendix

11

Appendix 1. Data Analysis Methods Utilized in the Research Literature(1) Data analysis methods of the research literature•

We surveyed the main research literature analyzing on-chain/off-chain data to understand the actual status of blockchain, and investigated thedata analysis methods used.

•

The investigation identified multiple analytical methods utilizing address clustering and graph theory/machine learning for the purpose ofidentifying multiple account administrators and suspicious transactions for blockchain addresses.

•

However, the results of the research are limited to understanding overall trends and identifying some target addresses, and analysis from publicly
available on-chain/off-chain data is considered to be limited in understanding the actual status of the blockchain.
Table A-1-1 Methods of data analysis of research literature

Data
distinction

Data Analysis
Methodology

Clustering

On-chaindata
analysis•Grouping blockchain addresses and other data analysis methods into broadgroups of desired data•

Applying mathematical theory, which consists of a set of nodes (vertices)and edges (lines connecting nodes), the characteristics of nodes and edges
are analyzed based on relationships such as their concentration, with nodes
as blockchain addresses and edges as transactions.

Graph Theory

MachineLearning

Other

Off-chaindataanalysis

Overview of Analysis Methodology

Publicly
available data
screening

•
•

•

•

Build models based on sample data and make predictions (judgments)
without being explicitly programmed
Multiple algorithms exist for different learning methods(supervised learning, unsupervised learning, reinforcement learning, etc.)
Investigate methods of analyzing on-chain data other than those listed
above

Using publicly available off-chain data to select valid data for blockchain
analysis

Results of a research•

The following three studies on clustering of blockchain addresses wereidentifiedAddresses appearing in the input of a transaction
Classification of addresses by transaction patterns such as address
reuse and airdrop (token distribution)

Bloom filter (probabilistic data structure) to characterize addresses, etc.

•

Confirmed that graph theory has been studied in clustering blockchain
addresses and analyzing transaction trends•

Of the machine learning, the following three analysis methods wereidentified as being studied, including detection of illegal addresses

Random ForestSupport Vector Machine
XGBoost•Identified 1 case of analysis of smart contract code and transaction logs
(Analyzed behavior suspected to be a ponzi scheme and investigated with
detection tools)•

The following three data analysis cases were identified
Twitter's 'Whale Alert' Tied to Bitcoin Price Rise
Identifying criminal activity-related addresses from public blockchain
posting sites

Search academic databases and journals to investigate market
manipulation techniques, etc.

112Appendix 1. Data Analysis Methods Utilized in the Research Literature(2) Research literature survey results
Table A-1-2 Results of research literature survey (1/5)
On-chain data analysis
No.

SourceDocument-nameSummary•

1-1

The Journal of Finance
Volume 75, Issue 4

Is Bitcoin Really
Untethered?
•

•

1-2

Finance Research
Letters Volume 49

The Intraday Bitcoin
Response to Tether
Minting and Burning
Events: Asymmetry,
Investor Sentiment, and"Whale Alerts" on Twitter

•

•

2

Financial Cryptography
and Data Security
2020

Address Clustering
Heuristics for Ethereum•

Analysis of blockchain data from
October 2014 to March 2018 on
Bitcoin's relationship with Tether(USDT: a stablecoin pegged to the
US dollar) to price increases.
Reported speculation of market
manipulation of the Bitcoin pricethrough the issuance of Tether

Clustering

Other

Off-chaindata analysis

Grouping
addresses thatappear in thetransaction
input

Analyzed blockchain data from
October 2014 to January 2021,
when Tether was issued, toinvestigate Bitcoin's response toTether's issuance.
Investigators reported that
Tether's issuance event was a"Whale Alert" notifying Twitter ofhigh-value transactions in nearreal-time, and positive investor
sentiment drove Bitcoin prices
higher.

Clustering Ethereum addresses
based on transaction patterns
such as address reuse andairdrops to analyze entities thatare likely to be managing multiple
accounts
Approximately 18% of Ethereum
addresses can be clustered,identifying more than 340,000
entities managing multiple
addresses

Graph Theory

MachineLearning

Analyzing Whale
Alert's Twitter
Announcement
and Bitcoin's
Price RiseGrouping ofaddresses basedon transaction
patterns such asaddress reuse,
airdrop, etc.

113Appendix 1. Data Analysis Methods Utilized in the Research Literature(2) Research literature survey results
Table A-1-2 Results of research literature survey (2/5)
On-chain data analysis
No.

SourceDocument-name32021 IEEE
International
Conference on
Decentralized
Applications andInfrastructures

Blockchain is Watching
You: Profiling and
Deanonymizing Ethereum
Users

Summary•••

4

30th USENIX Security
Symposium

Frontrunner Jones andthe Raiders of the Dark
Forest: An Empirical
Study of Frontrunning on
the Ethereum Blockchain

•

•

5

IEEE INFOCOM 2018 IEEE Conference on
ComputerCommunications

Understanding Ethereum
via Graph Analysis

Clustering

Graph Theory

MachineLearning

Research on how to identify users
by association of Ethereum
addresses using machine learningwith graph theory
User profiling explains superiority
of node embedding method

Researchmethods for
identifying users
by clustering
addresses

Utilize graphtheory (nodeembeddingtechnique) fordata analysis

Leveraging
machinelearning for data
analysis

Researching methods to efficiently
measure the behavior of Ethereum
front-running attacks (attacks inwhich miners intentionally swap
transactions during block
generation to gain profit).
Identified approximately 200,000
attack transactions, 1.5,000
attacker accounts, bots, profit
amounts, etc.

Bloom filter(probabilistic
data structure)to groupattackeraddresses

Graph-theoretic analysis of
Ethereum transaction data for an
approach to security issues.
 Attack Forensics: Detection ofmalicious smart contract
attacker accounts
 Anomaly detection: detectionof accounts that create a large
number of smart contracts thatare not invoked De-anonymization: extract
important keywords frominformation such as comments
about nodes

Other

Off-chaindata analysis

Money Flow/
Smart Contract
Creation/SmartContract Calling
Analyzed byGraph Theory114

Appendix 1. Data Analysis Methods Utilized in the Research Literature(2) Research literature survey results

Table A-1-2 Results of research literature survey (3/5)
On-chain data analysis

No.

SourceDocument-nameSummary•6

FC 2019: Financial
Cryptography andData Security

Measuring Ethereumbased ERC20 Token
Networks••

7

WWW '20:
Proceedings of The
Web Conference 2020

Measurements, Analyses,
and Insights on the
Entire Ethereum
Blockchain Network

•

•

8

Financial Cryptography
and Data Security
2023
Accepted papers

Understanding Polkadot
Through Graph Analysis:
Transaction Model,
Network Properties, andInsights••

9

Web Information
Systems Engineering WISE 2019

Detecting Fraudulent
Accounts on Blockchain:.
A Supervised Approach•

Graph theory analysis of therelationship between Ethereum's
top 1,000 token transfers andtoken owners.
While the majority of token
transfers are concentrated on
exchanges and other venues, wefound that many token holders do
not move their tokens at all

Clustering

Graph Theory

Other

Off-chaindata analysis

Graph theory
analysis of
token transfers
and token
holders

Analyze the interaction of
Ethereum users, transactions,smart contracts, token transfers,
etc. using graph theory to study
similarities and differences withSNS and the Web.
Explain that the distribution oftransactions is different from thatof social networking sites, etc.

Graph theory
analysis of
relevance oftransactions,token transfers,
etc.

Model user transactions in the
Polkadot blockchain and analyze
them in the Transaction Action
Graph
Detecting the concentration of
power in Binance and the realitythat the majority of native tokens
are occupied by 2% of users.

Graph Theory
Analysis of User
Transaction
Concentration

Comparing three machine learning
analysis methods for the purposeof detecting fraudulent accounts ofmalicious actors
Random forests yielded the best
results in reproducibility and false
positive rate

MachineLearning【Machine Learning Algorithm】• Random Forest:
Algorithm in which multiple decision trees trainedin parallel are asked to make predictions, and thefinal output is determined by majority vote or
average.
• Support Vector Machines:
Algorithms for classification and regression bydetermining boundaries, etc. that divide two classesof data groups.
• XGBoost:An algorithm that combines ensemble learning (amethod of outputting comprehensive results using
multiple methods that do not have highperformance) and decision trees (a method ofsolving problems by conditional branching), called
gradient boosting.
Comparing
three methods:
random forests,
support vector
machines, and
XGBoost115

Appendix 1. Data Analysis Methods Utilized in the Research Literature(2) Research literature survey results
Table A-1-2 Results of research literature survey (4/5)
On-chain data analysis
No.

SourceDocument-nameSummary•

10

Expert Systems with
Applications
Volume 150

Detection of illicit
accounts
over the Ethereum
Blockchain••

11

Future Generation
Computer Systems
Volume 102

Dissecting Ponzi schemeson Ethereum:
Identification, analysis,
and impact

•

•

12

ICIS 2021 International
Conference on
Information Systems

Cryptocurrency Market
Manipulation: A
Systematic Literature
Review•Detect illegal activity in transaction
history using XGBoost based on
approximately 2,000 addresses
flagged as illegal by the Ethereumcommunity (Etherscam DB) andnormal addresses
Achieved an average accuracy of
96% with 10 cross-validations
Investigate ponzi schemes(investment fraud) on Ethereum
and analyze behavior and impact
Analyzed smart contract code andtransaction logs for suspected
ponge scheme behavior using
validation tools and identified 184
ponge schemes
Keyword search of 7 academic
databases and 3 leading journal
journals for market manipulationof crypto assets
Analysis identifies 7 major
methods of market manipulation(Pump&Dump, money laundering,order books, stablecoin, front
running, insider trading, DDOS
attacks)

Clustering

Graph Theory

MachineLearning

Other

Off-chaindata analysis

EtherscamDB
information istied to an
Ethereum
account

Use XGBoost fordata analysis

Analyze smart
contract codeand transaction
logs to identify
ponge schemes

Academic
databases,
keywordsearches ofleading journals
to analyze
trends116

Appendix 1. Data Analysis Methods Utilized in the Research Literature(2) Research literature survey results
Table A-1-2 Results of research literature survey (5/5)
On-chain data analysis
No.

SourceDocument-nameSummary•

13

Financial Cryptography
and Data Security
2023
Accepted papers

Short paper: DeFi
Deception-Uncovering
the prevalence of rug
pulls in cryptocurrency
projects•

Analyzed time to fraud execution
and methods related to DeFi's
take-away fraud (rug pull) andinvestigated fraud trends
Fraud has been declining sincelate 2022 due to IDO (funded by
DEX token issuance), NFT, andother new services since then,which have reduced take-away
fraud.

Clustering

Graph Theory

MachineLearning

Other

Off-chaindata analysis

Bitcointalk(discussion
forum), analysis
of market site
information

117