Abstract
Blockchain has rapidly become one of the hottest Internet technologies as a decentralized and distributed data management solution. Cryptocurrency, one of the most successful applications of blockchain, quickly attracted attention from investors because of its high anonymity, flexibility and rapidity. However, cryptocurrencies are also used by some criminals to commit crimes secretly and they integrate illicit funds into real economy through cryptocurrency exchanges, causing serious impact to economies and societies. Traditional methods for exchanges to prevent financial crimes like Know Your Customer (KYC) have limit effect in the peer-to-peer and decentralized blockchain system. In this work, we propose a system aiming to help cryptocurrency exchanges to detect suspicious customers on the blockchain network. Besides the traditional KYC procedures, several machine learning models are used to check the account addresses submitted by the customers and to detect suspicious address in daily transaction. The detection models use the open-source data from websites and forums instead of private data from governments or institutions, and can detect the top five financial crimes in cryptocurrencies simultaneously. Finally, a visualization of transactions is proposed to better demonstrate the fund flows of suspicious customers for further analysis.
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Acknowledgements
This work was supported by the National Key R&D Program of China (No. 2020YFB1006000).
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Jiang, H., Zhang, K., Ma, X., Sun, Y., Ma, Y. (2022). Suspicious Customer Detection on the Blockchain Network for Cryptocurrency Exchanges. In: Svetinovic, D., Zhang, Y., Luo, X., Huang, X., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2022. Communications in Computer and Information Science, vol 1679. Springer, Singapore. https://doi.org/10.1007/978-981-19-8043-5_19
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DOI: https://doi.org/10.1007/978-981-19-8043-5_19
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