Abstract
Cryptocurrency has become a new venue for money laundering. Bitcoin mixing services deliberately obfuscate the relationship between senders and recipients, making it difficult to trace suspicious money flow. We believe that the key to demystifying the bitcoin mixing services is to discover agents’ roles in the money laundering process. We propose a goal-oriented approach to modeling, discovering, and analyzing different types of roles in the agent-based business process of the bitcoin mixing scenario using historical bitcoin transaction data. It adopts the agents’ goal perspective to study the roles in the bitcoin money laundering process. Moreover, it provides a foundation to discover real-world agents’ roles in bitcoin money laundering scenarios.
Highlights
Financial crimes directly disturb the national financial order and affect social stability and occur with other crimes to provide financial support for various types of organized crimes
We propose that the key to demystifying bitcoin mixing services is to discover agents’ roles in the money laundering process
We propose that the key to demystifying bitcoin mixing services is to discover agents’ roles in the money laundering process and present a goal-oriented modeling framework to model different roles in the money laundering process
Summary
Financial crimes directly disturb the national financial order and affect social stability and occur with other crimes to provide financial support for various types of organized crimes. Money laundering is a financial criminal activity, which mainly refers to the processing of illegal income by various means to cover up and conceal its source and nature It damages the security of the financial system and the reputation of financial institutions and destroys the normal economic order and social stability of the country. As studies have revealed that the pseudonyms of bitcoin addresses can be broken by aggregating addresses into clusters with identified users [1], more and more third-party bitcoin mixing services emerged to provide additional anonymity [2]. We propose a goal-oriented approach to modeling, discovering, and analyzing different types of roles in the agent-based business process of the money laundering scenario using historical transaction data from bitcoin mixing services. This article concludes with contributions and future research plans in Conclusion
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