Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain

Alarab, Ismail; Prakoonwit, Simant; Nacer, Mohamed Ikbal

doi:10.1145/3409073.3409080

Cited by 78 publications

(47 citation statements)

References 8 publications

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“…Money laundering detection solutions are categorized in two groups. First category is to identify the suspicious transactions, example - [23,40] has presented the AutoEncoder and Graph CNN deep learning methods respectively to identify suspicious transactions; and second category is to help investigate the identified suspicious transactions or alerts identified by rule-based systems, which is commonly called as decision support systems, example - [22,39] has presented a multi-channel CNN using NLP and scalable GCN method as a decision support system for investigating the alerts, respectively.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

et al. 2021

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Money laundering has been a global issue for decades, which is one of the major threat for economy and society. Government, regulatory and financial institutions are combating it together in their respective capacity, however still billions of dollars in fines by authorities make the headlines in the news. High-speed internet services have enabled financial institutions to deliver better customer experience through multi-channel engagements, which has led to exponential growth in transactions and new avenues for laundering the money for fraudsters. Literature shows the usage of statistical methods, data mining and Machine Learning (ML) techniques for money laundering detection, but limited research on Deep Learning (DL) techniques, primarily due to lack of model interpretability and explainability of the decisions made. Several studies are conducted on application of ML for Anti-Money Laundering (AML), and Explainable Artificial Intelligence (XAI) techniques in general, but lacks the study on usage of DL techniques together with XAI. This paper aims to review the current state-of-the-art literature on DL together with XAI for identifying suspicious money laundering transactions and identify future research areas. Key findings of the review are, researchers have preferred variants of Convolutional Neural Networks, and AutoEncoder; graph deep learning together with natural language processing is emerging as an important technology for AML; XAI use is not seen in AML domain; 51% ML methods used in AML are non-interpretable, 58% studies used sample of old real data; key challenges for researchers are access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced. Future research directions are, application of XAI techniques to bring-out explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research programs between research community and industry to benefit from domain knowledge and controlled access to data.

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Section: Discussionmentioning

confidence: 99%

“…Weber et al [40] presented a novel approach based on graph convolutional neural network to predict illicit transactions in the bitcoin transaction graph. The proposed method has used GCN along with multi-layer perceptron, which has given better results than only GCN as used in the original research paper.…”

Section: • Graph Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

et al. 2021

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“…We finally highlight two recent studies that use the Elliptic data set [137]. Alarab et al [2] propose a neural network structure where graph convolutional embeddings are concatenated with linear embeddings of the original features. This increases model performance significantly.…”

Section: Supervised Suspicious Activity Flaggingmentioning

confidence: 99%

Fighting Money Laundering with Statistics and Machine Learning

Jensen¹,

Iosifidis²

2022

Preprint

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Money laundering is a profound, global problem. Nonetheless, there is little statistical and machine learning research on the topic. In this paper, we focus on anti-money laundering in banks. To help organize existing research in the field, we propose a unifying terminology and provide a review of the literature. This is structured around two central tasks: (i) client risk profiling and (ii) suspicious behavior flagging. We find that client risk profiling is characterized by diagnostics, i.e., efforts to find and explain risk factors. Suspicious behavior flagging, on the other hand, is characterized by non-disclosed features and hand-crafted risk indices. Finally, we discuss directions for future research. One major challenge is the lack of public data sets. This may, potentially, be addressed by synthetic data generation. Other possible research directions include semi-supervised and deep learning, interpretability and fairness of the results.

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“…Simultaneously, many graph representation methods are applied to capture the dependency relationships between objects in the Blockchain network structure. Alarab et al [2] adopted Graph Convolutional Networks (GCN) intertwined with linear layers to predict illicit transactions in the Bitcoin transaction graph and this method outperforms graph convolutional methods used in the original paper of the same data. Liu et al [12] introduced an identify inference approach based on big graph analytics and learning, aiming to infer the identity of Blockchain addresses using the graph learning technique based on Graph Convolutional Networks.…”

Section: Graph Representationmentioning

confidence: 99%

TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts

Wang¹,

Chen²,

Yu³

et al. 2021

Preprint

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Blockchain technology and, in particular, blockchain-based transaction offers us information that has never been seen before in the financial world. In contrast to fiat currencies, transactions through virtual currencies like Bitcoin are completely public. And these transactions of cryptocurrencies are permanently recorded on Blockchain and are available at any time. Therefore, this allows us to build transaction networks (TN) to analyze illegal phenomenons such as phishing scams in blockchain from a network perspective. In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum. Firstly we extract transaction subgraphs for each address and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. We find that TSGNs can provide more potential information to benefit the identification of phishing accounts. Moreover, Directed-TSGNs, by introducing direction attributes, can retain the transaction flow information that captures the significant topological pattern of phishing scams. By comparing with the TSGN, Directed-TSGN indeed has much lower time complexity, benefiting the graph representation learning. Experimental results demonstrate that, combined with network representation algorithms, the TSGN model can capture more features to enhance the classification algorithm and improve phishing nodes' identification accuracy in the Ethereum networks.

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Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain

Cited by 78 publications

References 8 publications

Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

Fighting Money Laundering with Statistics and Machine Learning

TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts

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