Anti-money laundering supervision by intelligent algorithm

Yang, Guangyi; Liu, Xiaoxing; Li, Beixin

doi:10.1016/j.cose.2023.103344

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…From the perspective of the Bitcoin transaction, in metapath A, both Tx 1 and Tx 2 have output to the common wallet address, which is more consistent with the situation of the centralized transfer of transactions (lines 21,22). In metapath B, Tx 1 and Tx 2 show obvious one-way flow characteristics (lines 24, 25).…”

Section: Metapath-type Divisionsupporting

confidence: 52%

“…Jensen et al [20] used gated recurrent units and a self-attention model, which can reduce the number of false positives when qualifying alarms. Yang et al [21] effectively discerned conspicuous anomalous money laundering patterns across diverse transaction datasets by training a combined long short-term memory and graph convolutional neural network model. Zhao et al [22] combined mutual information and self-supervised learning to create a model based on mutual information that is used to improve the massive amount of untagged data in the dataset.…”

Section: Related Workmentioning

confidence: 99%

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HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering

Song,

2023

Applied Sciences

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In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions.

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Section: Metapath-type Divisionsupporting

confidence: 52%