Combating Illicit Cryptocurrency Transactions with Blockchain Analytical Methods
In its Crypto Crime Report 2023, Chainalysis reported that the volume of illicit cryptocurrency transactions hit an all-time high of USD 20.6 billion in 2022. This was a 13.8% Year-on-Year (YoY) increase over the USD18.1 billion recorded in 2021.
The illicit transaction volume of 2022 represented 0.24% of all cryptocurrency transaction volume last year where by this was double that of 0.12% in 2021.
Given the rising volume of illicit cryptocurrency transactions, let's explore the use of blockchain analytical methods to stem the tide of such transactions:
Identifying Controlling Individual or Entity with Cluster Analysis
Cluster analysis is an analytical method that groups together addresses on a blockchain that are likely to be controlled by the same individual or entity. This identification of a common controlling individual or entity is made based on the transaction behaviour of the addresses. An example is where a group of addresses on a blockchain receive illicit funds from a particular source before transferring these funds to other addresses. Through the use of cluster analysis, the addresses from and to which these funds are transferred can be grouped together as they are likely to be controlled by the same individual or entity.
A use case of cluster analysis is cryptocurrency intelligence company, CipherTrace's platform which deploys proprietary clustering algorithms to collate and generate correlations between a variety of indicators from the metrics of a blockchain network. The generated correlations serve as actionable intelligence for anti-money laundering (AML) investigations and compliance as they facilitate the identification of individuals or entities that are controlling blockchain addresses which are involved with illicit cryptocurrency transactions.
Visualizing Patterns and Trends with Time Series Analysis
Time series analysis is an analytical method that involves collecting and organizing data such as timestamps and transaction volume from a blockchain over a period of time. This data is then plotted on a graph to visualize the patterns and trends of the sequence of events on the blockchain. An example is where the transactions of a blockchain address suspected of being involved in illicit cryptocurrency transactions can be visualized using time series analysis to look for patterns and trends such as the receipt of frequent transactions in small amounts from multiple sources whereby these amounts are then consolidated into larger amounts sent to another address. The visualization of such a pattern or trend would indicate the use of the address for illicit cryptocurrency transactions.
A use case of time series analysis is by blockchain analytics platform Elliptic which has partnered with researchers from Massachusetts Institute of Technology (MIT) and tech giant IBM to use machine learning (ML) methods such as Logistic Regression, Random Forest, Multilayer Perceptrons to build data sets of cryptocurrency payments flows. These data sets are then subjected to time series analysis to identify the blockchain addresses that are suspected to be involved in illicit cryptocurrency transactions based on their payment flows.
Examining Internodal Relationships with Graph Algorithms
Graph algorithms is an analytical method that involves the use of mathematical techniques to study the structure and behaviour of a blockchain with the aim of identifying relational patterns between different nodes on the blockchain. A common graph algorithm is the centrality algorithm which measures the importance of a node on a blockchain based on its connections to other nodes on a blockchain. In order to safeguard the operational integrity of a blockchain, nodes that are identified as being of critical importance to the blockchain would be subjected to more stringent suspicious transaction detection, risk assessment, monitoring and surveillance as well as Investigations and attribution to ensure that they are not involved in illicit cryptocurrency transactions.
A use case of graph algorithms is by Web3, AI-based security company Cyvers which deployed these algorithms that studies blockchain data to detect patterns which indicate the possibility of illicit cryptocurrency transactions. Cyvers' graph algorithms create a graph of the transactions on a blockchain before analyzing the graph for patterns that may indicate possible interactions between addresses on the blockchain with mixer services. The algorithm does this by examining the flow of funds and the frequency of transactions of the addresses. The ability of Cyvers' graph algorithms to detect addresses which receive funds from mixers was illustrated by its detection of the CoW hack even before the execution of any malicious transaction by the hackers.
With the impending dawn of Web3 as the next generation of the Internet, the increasing use of cryptocurrency as the financial powerhouse of Web3 is set to bring about a corresponding rise in illicit cryptocurrency transactions. This renders the role of blockchain analytical methods for the combating of these transactions to be all the more important moving forward.