Money laundering has been a financial crime throughout the modern era, and has grown in sophistication as technologies for financial records and transactions have developed. Organized and transnational criminal networks, regardless of the nature of their illicit enterprises, have to hide their activities and cover their tracks to make their illegal income look legitimate. This is often executed by creating cash businesses,such as bars and restaurants, with a limited paper trail. Counter to expectations that in the Digital Age, the electronic records of transactions would make money laundering more difficult, the advent of block chain programming and cryptocurrency technology has given criminal groups some new and effective means to launder money and hide their business dealings.
Cryptocurrencies, such as Bitcoin, are based on binary digital data that is heavily encrypted in blockchains, created for the purpose of conducting transactions. Cryptocurrencies are created to be decentralized, avoiding generation, control, or monitoring by any central authority. Given that governments and official financial institutions have no overview or authority over cryptocurrencies, they are an ideal means for conducting illegal financial transactions that are global, anonymous, and very difficult to monitor. These factors pose major obstacles for law enforcement and agencies responsible for investigating digital money laundering, crypto fraud, and online crime. Tax evasion, terrorism funding, drug trafficking, credit card data trade, ransomware attacks, human trafficking, and any other illegal activity involving financial exchange can be conducted in better secrecy and security through cryptocurrencies, often on the “dark web.” Some credible estimates put the amount of Bitcoin trade resulting from money laundering and other illegal activities at about US$4.5 billion annually.
Recent developments and advances in Artificial Intelligence are significantly and dramatically helping investigators identify patterns, trends, and connections in cryptocurrency activity, with cutting-edge algorithms combing through massive amounts of data to flag suspicious activities. Machine learning systems can process massive amounts of data in a matter of hours or even minutes, taking the burden off what large teams of trained researchers would otherwise have to try to accomplish over a period of months. Subject Matter Experts, investigators, and analysts are successfully leveraging AI and applying strategies such as “clustering” and “peeling” to spot trends and gain information to identify cryptocurrency users who appear to be involved in illegal activities.
Algorithms that are programmed for “clustering” isolate and group activities in order to trace them to particular entities. “Peeling” involves identifying transactions in which partial amounts of a cryptocurrency unit are “peeled” off, with the remainder transferred back to a one-off change address. The contact information from partial currency units which were peeled and returned can provide analysts with valuable lead information and insights. These are among many analytical strategies enabled by Artificial Intelligence for investigators and financial crimes analysts. Patterns in input addresses and one-off change addresses, as well as general trends in timing, frequency, and transaction amounts, are used by analysts to uncover the layers of secrecy and reveal the criminals behind the code.
