How Does Blockchain Intelligence Prevent Money Laundering?

Money laundering is described as the transfer of illegally obtained funds through legitimate people or accounts in order to conceal the source of the funds. Financial institutions, such as banks and other credit-granting organizations, use anti-money laundering (AML) systems to prevent it by identifying and fighting against money laundering threats. If you want to know how to prevent money laundering in cryptocurrency, then you must understand the power of blockchain intelligence in combating money laundering. In this article, we will discuss cryptocurrency money laundering typologies and the cryptocurrency money laundering case study of the Coincheck hacking incident and others.

Cryptocurrency Money Laundering Typologies

Those who examine their blockchain transactions will look for several things. One such typology to look at is the use of non-compliant crypto exchanges. In this case, the criminal party is frequently attempting to ‘clean up’ an illegitimate funding source by trading it for cash or other cryptocurrencies. For instance, Payza, an unregulated exchange, laundered $250 million through altcoins and Bitcoin, as discovered by the US Department of Justice in 2018. Cross-wallet activity by the same customer multiple times is also among cryptocurrency money laundering typologies that help launder money through the dark web or via offshore gambling. It highlights suspicious behavior than illegal activity, though. For instance, it’s usual for purchasers to pay for their drugs using Bitcoin, which is subsequently exchanged for a privacy coin by the suppliers. Dirty fiat and illicit crypto assets are also laundered through crypto ATMs. Cryptocurrencies located in one place are moved to another using this medium. Though financial inclusion is being improved with cryptocurrencies, it also welcomes financial crime simultaneously that needs to be controlled.

Cryptocurrency Money Laundering Case Study

On January 26, 2018, 58 billion yen ($530 million) worth of NEM, a cryptocurrency, was unlawfully accessed and then stolen from Japan’s Coincheck Exchange. This hacking crime was remarkable because it was one of the largest cryptocurrency heists in history. Additionally, the stolen NEM was sold, and funds were laundered through a cryptocurrency exchange, making it a worth reading cryptocurrency money laundering case study of the unregulated decentralized world. The US Department of Justice stated that Roger Nils-Jonas Karlsson, a Swedish man, had been sentenced to 15 years in prison for securities fraud on July 4, 2021. He committed money laundering offenses stemming from an investment scam that duped thousands of people out of over US$16 million. As seen by a significant surge in cases of money laundering through virtual currencies in recent years, Silk Road, Liberty Reserve, and Western Express International are the incidents that signal a painful awakening. Furthermore, the takedown of a criminal network laundering tens of millions of euros in stolen funds that were promoting its services in internet forums was the outcome of a complex investigation involving 20 countries. The money was subsequently returned to the cybercriminals who had laundered it. More cases can also be found in crypto money laundering Reddit discussions.

How to Prevent Money Laundering in Cryptocurrency?

Firms like Chainalysis and Elliptic are here to the rescue of regulators to identify and punish money launderers. Blockchain analysis, which operates by ‘scraping’ the publicly available transactional data on the distributed ledger, can be used to identify money laundering typologies, as described above. In addition, businesses can view transactional data in this way to see if any illegal behavior has occurred. Additionally, they can identify the red flags such as non-compliant behavior like poor KYC or engagement on the dark web. Based on such signals, criminals can be caught and punished by the regulators to protect the trust of the citizens both in the law and then in the digital assets. Furthermore, blockchain analysis software includes visualization tools for investigating crypto hazards and analyzing blockchain addresses using transaction graphs to identify the relationship between two or more transactions. To assign a risk score to each blockchain transaction, risk models are developed and trained in machine learning techniques such as clustering. Finally, risk models are built using a variety of characteristics such as the transaction amount, destination and source of funds, and money flow history to investigate and prevent money laundering cases.

Final Thoughts

Cryptocurrency’s illegal application is no longer limited to cybercrime but increasingly encompasses all sorts of crimes that need the transmission of monetary value. However, estimating the size and scope of the illegal usage of cryptocurrencies in criminal operations is complex. In addition, due to growing value transfer opportunities, criminals and their networks involved in organized crime continue to rely on traditional fiat money and transactions to a great extent. However, blockchain analytics can help prevent money laundering by identifying cryptocurrency money laundering typologies in real-time.

   
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