Sydykova A.
CRYPTOCURRENCY TRACKING TECHNOLOGIES: HOW BLOCKCHAIN MONEY LAUNDERING IS BEING COMBATED *
Аннотация:
the growing use of cryptocurrencies in illicit activities, including money laundering, requires adapting traditional financial controls to decentralized systems. The article analyzes key challenges for AML/CFT in the blockchain environment, modern transaction tracking technologies (graph analysis, machine learning) and regulatory initiatives. Special attention is paid to the tension between anonymity and transparency, as well as the role of international standards (FATF, MiCA) in shaping the legal framework.
Ключевые слова:
cryptocurrency, blockchain, AML, transaction tracking, FATF, MiCA, decentralization, privacy
DOI 10.24412/2712-8849-2025-586-736-745
Blockchain technologies and cryptocurrencies have opened new horizons in the world of financial transactions, making it possible to carry out fast, cheap and relatively anonymous transactions. However, along with the advantages came risks: cryptocurrencies were used in terrorist financing schemes, money laundering, and anonymous trading on the darknet. According to Chainalysis report "The 2023 Crypto Crime Report", the volume of cryptocurrencies associated with money laundering in 2022 amounted to $23.8 billion, with 44% of funds passed through decentralized services [1]. This actualizes the development of specialized AML solutions combining data analysis, machine learning and regulatory mechanisms. There is a need to develop and implement new technological solutions to ensure transparency and traceability of blockchain transactions.One of the fundamental problems is transaction pseudonymization. In most public blockchains (e.g. Bitcoin), transactions are linked to cryptographic addresses (keys) rather than real user names. Although all transactions are recorded in a publicly available registry, establishing a link between a pseudonymous address and the real identity of the owner presents a serious obstacle to Know Your Customer (KYC) and Customer Due Diligence (CDD) procedures [2]. Attackers can use this pseudonymity to conceal the origin and destination of illicit funds (Figure 2).Fig. 2 Scheme of symmetric cryptography.The decentralized nature of many blockchain systems complicates traditional regulatory approaches. The absence of a centralized governing body or intermediary that can be held accountable for AML compliance (as banks are) creates regulatory gaps [3]. Identifying jurisdiction and responsible parties to enforce AML/CFT measures becomes challenging, especially in the context of decentralized financial applications (DeFi) and decentralized autonomous organizations (DAOs).The cross-border nature of blockchain transactions exacerbates regulatory and enforcement challenges. Funds can move instantly across borders without the involvement of traditional financial intermediaries, making international cooperation and enforcement of national AML laws difficult [4]. Differences in regulation of virtual assets between countries create opportunities for regulatory arbitrage, with attackers exploiting jurisdictions with weaker AML regimes.The emergence of privacy enhancing technologies and obfuscation techniques presents another major challenge. Tools such as:1.Mixers (Mixers) or Tumblers (Tumblers): Services that mix transactions from different users to break the connection between sender and receiver [5].2.Privacy Coins: cryptocurrencies (e.g., Monero, Zcash) that use advanced cryptographic techniques (ring signatures, zero-disclosure proofs) to hide transaction details such as sender, recipient, and amount).3.Chain Hopping: the practice of rapidly exchanging one cryptocurrency for another across multiple blockchains to obfuscate trails.These techniques are purposefully used to make it difficult to track the flow of illicit funds, making traditional blockchain analysis less effective.In addition, the volume and speed of transactions in popular blockchains pose challenges for monitoring. Analyzing huge data sets requires significant computing resources and specialized analytics tools. The effectiveness of AML systems depends on the ability to process and interpret this information in a timely manner to identify suspicious activity.Finally, regulatory uncertainty and the lag of legislation behind the pace of technological innovation create risks. New blockchain-based products and services are emerging faster than regulators have time to develop and implement appropriate AML requirements, leaving loopholes for illicit activities [6].Thus, the blockchain environment presents a complex landscape for AML. Pseudonymity, decentralization, cross-border nature, obfuscation technologies and regulatory challenges require the development of specialized approaches, tools and international cooperation to effectively counter financial crime in this area.The public nature of the blockchain allows the history of transactions to be explored, linking addresses to real entities. Each transaction contains inputs (sources of funds) and outputs (recipients), which forms chains of asset movement. Heuristics such as Common Input Ownership Heuristic and behavior pattern analysis are used to cluster addresses belonging to the same user. For example, if multiple addresses are used to pay commissions from a single wallet, they can be clustered together.Blockchair and Etherscan tools automate the analysis process by providing visualization of transaction chains. Studies by scientists have demonstrated that more than 60% of bitcoin addresses can be deanonymized through communication with exchange services [7].Modeling transactions as graphs allows us to identify suspicious activities such as mixing funds or interacting with darknet marketplaces. The nodes of the graph represent addresses and the edges represent the transactions between them. Algorithms such as PageRank and Louvain help detect centralized nodes (e.g., exchanges) and communities with anomalous patterns (Figure 3.).Fig. 3. Googles PageRank algorithm.Machine learning (ML) techniques are used to classify transactions into legitimate and suspicious transactions. Characteristics include frequency of transactions, size of transfers, and links to blacklisted addresses. Random Forest and LSTM algorithms have demonstrated high accuracy in detecting money laundering through cryptocurrency exchanges.Despite the progress, technology faces methods to enhance anonymity: mixing (CoinJoin), use of private blockchains (Monero, Zcash) and protocols like zk-SNARKs.The most effective way to combat money laundering is to analyze blockchain data using specialized platforms such as Chainalysis, CipherTrace, Elliptic and others. These tools allow clustering wallet addresses, identifying links between transactions, building behavioral models of network participants, and identifying suspicious activity based on patterns.Modern solutions actively use AI and machine learning to identify atypical transactions and automatically label potentially suspicious transactions. This increases the efficiency of monitoring and reduces the number of false positives.There is a practice of creating "blacklists" of addresses associated with criminal activity. Financial institutions compliance systems can automatically block interactions with such wallets.One of the key approaches to mitigating AML risks in a blockchain environment is the use of labels and blacklists applied to crypto-addresses. These tools, developed and maintained both by specialized blockchain analytics companies and by virtual asset service providers (VASPs) themselves, as well as formed on the basis of official sanctions lists, serve as a basis for risk assessment and transaction monitoring [8].Tags are annotations attached to specific addresses or clusters of addresses, indicating their association with known entities or activities. They may include identification of addresses belonging to:- regulated exchanges and other VASPs,- mixing services (mixers),- darknet platforms,- ransomware operators,-gambling,- fraudulent schemes (scams),-sanctioned entities and jurisdictions.Tagging allows for contextual analysis of transactions. For example, a transfer of funds from an address labeled as associated with the darknet market to an address of a well-known exchange will be automatically classified as high-risk and will require additional verification by the exchanges compliance service [9].Blacklists, in turn, contain a list of addresses reliably associated with illegal activities or under sanctions (e.g., addresses on the SDN list of the US Office of Foreign Assets Control (OFAC)). Interactions with blacklisted addresses are typically direct grounds for blocking a transaction or freezing assets in accordance with regulatory requirements. These lists play a critical role in preventing the use of cryptocurrencies to finance terrorism, circumvent sanctions, and launder the proceeds of the most serious crimes.Tagging and blacklists are powerful tools for automating AML monitoring and regulatory compliance. They allow VASPs and other market participants to quickly identify and respond to transactions with a high level of risk. However, their effectiveness has a number of limitations. Table 1. Limitations of Using Labels and Blacklists in AML. Despite these limitations, tags and blacklists remain an integral part of modern AML/CFT systems in a blockchain environment. Their effectiveness is enhanced when used in conjunction with other technologies such as advanced behavioral analysis, machine learning algorithms for anomaly detection, and enhanced KYC/CDD procedures. Further development of methods to identify and verify the actors behind pseudonymous addresses, as well as international cooperation in sharing data on risky addresses, are key areas for improving the effectiveness of these tools.Organizations such as FATF have developed guidelines for applying KYC (know your customer) and AML principles to the cryptocurrency industry. Many states are implementing these standards by mandating registration of cryptocurrency exchanges, user identification and transaction monitoring. Examples include the EUs Markets in Cryptoassets Regulation (MiCA) and the US Bank Secrecy Act, which covers digital assets [10].The future of anti-money laundering (AML) in fintech is seen not in individual technological breakthroughs, but in the deep integration and synergy of existing and emerging solutions, improving regulatory approaches and strengthening cooperation. The outlook is for integrated AML platforms, where artificial intelligence and machine learning will work closely with blockchain analytics to detect anomalies, and biometrics and decentralized identification on blockchain to create robust and secure KYC processes. AI is expected to further evolve towards explainability (XAI) for increased transparency, predictive analytics for proactive risk detection, and federated learning to address privacy concerns with collaborative model improvement. In parallel, regulatory and supervisory technologies (RegTech/SupTech) will evolve to automate compliance, reporting and move towards continuous monitoring, while privacy enhancing technologies (PETs) such as zero-disclosure evidence will help balance data analytics with privacy requirements.Key to success will be increased collaboration through public-private partnerships and international information sharing, especially in the context of cross-border virtual asset transactions. Overall, the AML landscape is moving toward more intelligent, integrated, adaptive and preventive systems, but their successful implementation will require addressing ongoing challenges related to data, ethics and global coordination to secure the financial system.Combating money laundering in blockchain requires an integrated approach: a combination of technological solutions, regulatory and international cooperation. The development of analytical platforms and strengthening regulatory requirements are key to increasing trust in cryptocurrencies and their integration into the global financial system.
Номер журнала Вестник науки №5 (86) том 2
Ссылка для цитирования:
Sydykova A. CRYPTOCURRENCY TRACKING TECHNOLOGIES: HOW BLOCKCHAIN MONEY LAUNDERING IS BEING COMBATED // Вестник науки №5 (86) том 2. С. 736 - 745. 2025 г. ISSN 2712-8849 // Электронный ресурс: https://www.вестник-науки.рф/article/22917 (дата обращения: 20.07.2025 г.)
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