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Journal of Social Computing 2022, 3(3): 219-230 https://doi.org/10.23919/JSC.2022.0011
Open Access | Issue | Published: 30 September 2022

Transactional Network Analysis and Money Laundering Behavior Identification of Central Bank Digital Currency of China

Show Author's Information Ziyu Li1,  Yanmei Zhang1( ),  Qian Wang2,  Shiping Chen3
School of Information, Central University of Finance and Economics, Beijing 100081, China
Global Anti-Money Laundering Center, Agricultural Bank of China, Beijing 100005, China
CSIRO Data61, Sydney 1710, Australia
Keywords:
central bank digital currency (CBDC), transactional network, money laundering, behavior identification
Cite this article:
Li Z, Zhang Y, Wang Q, et al. Transactional Network Analysis and Money Laundering Behavior Identification of Central Bank Digital Currency of China. Journal of Social Computing, 2022, 3(3): 219-230. https://doi.org/10.23919/JSC.2022.0011
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Abstract Full text About this article

With the gradual application of central bank digital currency (CBDC) in China, it brings new payment methods, but also potentially derives new money laundering paths. Two typical application scenarios of CBDC are considered, namely the anonymous transaction scenario and real-name transaction scenario. First, starting from the interaction network of transactional groups, the degree distribution, density, and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed, so as to clarify the characteristics and paths of money laundering transactions. Then, according to the two typical application scenarios, different transaction datasets are selected, and different models are used to train the models on the recognition of money laundering behaviors in the two datasets. Among them, in the anonymous transaction scenario, the graph convolutional neural network is used to identify the spatial structure, the recurrent neural network is fused to obtain the dynamic pattern, and the model ChebNet-GRU is constructed. The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior, with a precision of 94.3%, a recall of 59.5%, an F1 score of 72.9%, and a micro-average F1 score of 97.1%. While in the real-name transaction scenario, the traditional machine learning method is far better than the deep learning method, and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%, which can effectively identify money laundering in currency transactions.


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About this article

Transactional Network Analysis and Money Laundering Behavior Identification of Central Bank Digital Currency of China

Show Author's information Ziyu Li1, Yanmei Zhang1( ), Qian Wang2, Shiping Chen3
School of Information, Central University of Finance and Economics, Beijing 100081, China
Global Anti-Money Laundering Center, Agricultural Bank of China, Beijing 100005, China
CSIRO Data61, Sydney 1710, Australia

Abstract

With the gradual application of central bank digital currency (CBDC) in China, it brings new payment methods, but also potentially derives new money laundering paths. Two typical application scenarios of CBDC are considered, namely the anonymous transaction scenario and real-name transaction scenario. First, starting from the interaction network of transactional groups, the degree distribution, density, and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed, so as to clarify the characteristics and paths of money laundering transactions. Then, according to the two typical application scenarios, different transaction datasets are selected, and different models are used to train the models on the recognition of money laundering behaviors in the two datasets. Among them, in the anonymous transaction scenario, the graph convolutional neural network is used to identify the spatial structure, the recurrent neural network is fused to obtain the dynamic pattern, and the model ChebNet-GRU is constructed. The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior, with a precision of 94.3%, a recall of 59.5%, an F1 score of 72.9%, and a micro-average F1 score of 97.1%. While in the real-name transaction scenario, the traditional machine learning method is far better than the deep learning method, and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%, which can effectively identify money laundering in currency transactions.

Keywords: central bank digital currency (CBDC), transactional network, money laundering, behavior identification

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Received: 17 April 2022
Revised: 06 August 2022
Accepted: 06 September 2022
Published: 30 September 2022
Issue date: September 2022

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