An Analysis of the MTI Crypto Investment Scam: User Case

Since the start of the Covid-19 pandemic, blockchain and cryptocurrency adoption has increased significantly. The adoption rate of blockchain-based technologies has surpassed the Internet adoption rate in the 90s and early 2000s. As this industry has grown significantly, so too has the instances of crypto scams. Numerous cryptocurrency scams exist to exploit users. The generally limited understanding of how cryptocurrencies operate has increased the possible number of scams, relying on people’s misplaced sense of trust and desire for making money quickly and easily. As such, investment scams have also been growing in popularity. Mirror Trading International (MTI) has been named South Africa’s biggest crypto scam in 2020, resulting in losses of $1.7 billion. It is also one of the largest reported international crypto investment scams. This paper focuses on a specific aspect of the MTI scam; an analysis on the fund movements on the blockchain from the perpetrators and members who benefited the most from the scam. The authors used various Open-Source Intelligence (OSINT) tools, alongside QLUE, as well as news articles and blockchain explorers. These tools and techniques are used to follow the money-trial on the blockchain, in search of possible mistakes made by the perpetrator. This could include instances where some personal information might have been leaked. With such disclosed personal information, OSINT tools and investigative techniques can be used to identify the criminals. Due to the CEO of MTI having been arrested, and the case currently being dealt with in the court of law in South Africa, this paper also presents investigative processes that could be followed. Thus, the focus of this paper is to follow the money and consequently propose a process for an investigator to investigate crypto crimes and scams on the blockchain. As the adoption of blockchain technologies continues to increase at unprecedented rates, it is imperative to produce investigative toolkits and use cases to help reduce time spent trying to catch bad actors within the generally anonymous realm of cryptocurrencies.