Abstract
In this paper, we propose the modelling of patterns of financial transactions - with a focus on the domain of cryptocurrencies - as splittings and present a method for generating such splittings utilizing integer partitions. We further exemplify that, by having these partitions fulfill certain criteria derived from financial policies, the splittings generated from them can be used for modelling illicit transactional behavior as seen in smurfing.
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Notes
- 1.
- 2.
We assume the existence of a smallest currency unit; in the real world, this assumption holds—in particular—for the US$, for the Euro and for Bitcoin.
- 3.
Each element p of S(n) is a finite sequence, we write \(p=(p(1),p(2),\dots ,p(\text {length}(p))\).
- 4.
- 5.
Here, we adopt the notation from SageMath and denote sequences as \([\lambda _1, \dots , \lambda _r]\).
- 6.
Note that it follows from Definition 1 that we have \(\lambda _{i+1}-\lambda _{i}\le 0\), for all \(1 \le i<r\).
References
Andrews, G.E.: The Elementary Theory of Partitions. Encyclopedia of Mathematics and its Applications, pp. 1–15. Cambridge University Press (1984)
Demetis, D.S.: Fighting money laundering with technology: a case study of Bank X in the UK. Decis. Support Syst. 105, 96–107 (2018)
Gao, S., Xu, D., Wang, H., Wang, Y.: Intelligent Anti-Money Laundering System. In: 2006 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 851–856 (2006)
Han, J., Huang, Y., Liu, S., Towey, K.: Artificial intelligence for anti-money laundering: a review and extension. Digit. Finance 2, 211–239 (2020)
Haslhofer, B., Stüt z, R., Romiti, M., King, R.: GraphSense: A General-Purpose Cryptoasset Analytics Platform (2021)
Kalodner, H., Möser, M., Lee, K., Goldfeder, S., Plattner, M., Chator, A., Narayanan, A.: BlockSci: Design and applications of a blockchain analysis platform. In: 29th USENIX Security Symposium (USENIX Security 20). pp. 2721–2738. USENIX Association (2020). https://www.usenix.org/conference/usenixsecurity20/presentation/kalodner
Kingdon, J.: AI fights money laundering. IEEE Intell. Syst. 19(3), 87–89 (2004)
Levi, M.: Money laundering and its regulation. Ann. Am. Acad. Pol. Soc. Sci. 582(1), 181–194 (2002)
Mehmet, M., Wijesekera, D., Fuentes, M.: Money laundering detection framework to link the disparate and evolving schemes. J. Digit. Forensics, Sec. Law (JDFSL) 1558-7223 8, 41 (01 2013)
Starnini, M., et al.: Smurf-based anti-money laundering in time-evolving transaction networks. In: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, pp. 171–186. Springer International Publishing, Cham (2021)
Sun, Y., Xiong, H., Yiu, S.M., Lam, K.Y.: Bitanalysis: a visualization system for bitcoin wallet investigation. IEEE Trans. Big Data 9(2), 621–636 (2023)
Wang, H.M., Hsieh, M.L.: Cryptocurrency is new vogue: a reflection on money laundering prevention. Sec. J. (01 2023)
Weber, M., et al.: Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
Acknowledgements
SBA Research (SBA-K1) is a COMET Center within the COMET - Competence Centers for Excellent Technologies Programme and funded by BMK, BMAW, and the federal state of Vienna. The COMET Programme is managed by FFG. Moreover, this work was performed partly under the following financial assistance award 70NANB21H124 from U.S. Department of Commerce, National Institute of Standards and Technology.
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Garn, B., Kieseberg, K., Çulha, C., Koelbing, M., Simos, D.E. (2023). A Mathematical Approach on the Use of Integer Partitions for Smurfing in Cryptocurrencies. In: Pardalos, P., Kotsireas, I., Knottenbelt, W.J., Leonardos, S. (eds) Mathematical Research for Blockchain Economy. MARBLE 2023. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-48731-6_10
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