Artificial intelligence for knowledge graphs of cryptocurrency anti-money laundering in fintech

Day, Min-Yuh

doi:10.1145/3487351.3488415

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…Note that a pragmatic approach involves integrating technology to enhance compliance. Regulatory technology solutions can adeptly manage the substantial regulatory burden, ensuring more effective oversight (Day, 2021) and yielding innovative regulatory solutions (Lieonov, Vasylieva, Mynenko, & Dotsenko, 2021;Nikiforova & Nikiforov, 2021). Accordingly, these solutions not only deter illicit activities but also safeguard investors, preserving the financial system's integrity.…”

Section: Money Laundering and The Digital Agementioning

confidence: 99%

Money laundering in global trade and the role of financial technology: The structured reviews

Yousop,

Razak,

Noordin

2024

5007

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Money laundering in international trade facilitates the concealment of illicit funds through the manipulation of trade invoices and the exploitation of regulatory gaps. Addressing this issue involves improving traceability through financial technology, a promising solution. However, the adoption of such technology poses inherent risks and challenges that require careful consideration. This review synthesises knowledge on trade-related money laundering and financial technology, highlighting critical areas for additional research and improvement. Employing a Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) approach, academic databases, including Scopus and Web of Science (WoS), were systematically searched to identify relevant studies and articles. Advanced search techniques using keywords such as money laundering, trade-based money laundering, and financial technology resulted in the identification of 57 articles for eligibility. After excluding 20 articles due to being out of the field, having titles not significantly related, or abstracts not aligning with the study's objective, 37 eligible articles and conference proceedings were further examined. Two main themes were developed, concluding that the convergence of money laundering through digital trade and financial technology requires collaborative efforts among regulatory bodies, financial institutions, and technology experts. Such collaboration is essential to balance innovation and security, ensuring stability and prosperity in global economies.

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Section: Money Laundering and The Digital Agementioning

confidence: 99%

Money laundering in global trade and the role of financial technology: The structured reviews

Yousop,

Razak,

Noordin

2024

5007

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“…Tharani, et al (2021) proposed a visualization of functions related to transactions in the Bitcoin and Ethereum networks, which helps to quickly identify cases of cyber fraud. The knowledge graph presented by Day (2021) can be used to combat the misuse of electronic payment instruments and cryptocurrencies.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the maturity of the current global system for combating financial and cyber fraud

Kuzmenko,

Yarovenko,

Perkhun

2023

SiTns

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The purpose of the article is to assess the maturity of systems for counteracting financial and cyber fraud with the view of their future integration at global-level. The calculations made by the authors were based on indicators for 76 countries, which characterized each country's level of cybersecurity and its ability to combat financial fraud in 2018. After optimising the input data and selecting relevant indicators, the authors built an integrated cybersecurity index using the Sundarovsky convolution method. Sigma-limited parameterisation and Pareto-optimisation were then used to identify the determinants of the ability to counter financial and cyber fraud, which were used as predictors. Nonlinear regression was applied to determine the dependency of the integrated cybersecurity index on the government efficiency index, the ease of doing business and on the crime indices. On this basis, the authors conducted a bifurcation analysis of the maturity of current global system for combating financial and cyber fraud and produced its phase portraits. It was found to be mature (“Government Efficiency Index – Ease of Doing Business” and “Ease of Doing Business – Crime Index”) and insufficient mature (“Government Efficiency Index – Crime Index”), with the components' imbalance indicating high system's sensitivity to react on changes. The constructed 'Equilibrium States' phase portraits showed non-equilibrium phase portraits of the 'saddle' type. The obtained results made it possible to identify determinants of a global integrated system's instability to combat financial and cyber fraud.

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“…Currently, there are relatively few AML methods based on knowledge graph. Day et al 16,17 applied meta-learning few-shot models to transform a general knowledge graph into a cryptocurrency AML knowledge graph. However, their work focuses on using the constructed knowledge graph for crime situation analysis in AML and financial technology.…”

Section: Related Workmentioning

confidence: 99%

Anti-money laundering method based on hierarchical risk control knowledge graph

Tang,

Sun

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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With the development of the Internet and advances in technology, financial transactions have become more convenient, but money laundering has also become more concealed, complex, and can be quickly adjusted according to regulatory policies, making traditional methods based on rule libraries and blacklists difficult to cope with the current Anti-Money Laundering (AML) challenges. To improve the efficiency and accuracy of AML work, various machine learning methods have been proposed. However, these methods are often difficult to be widely used due to their complex models and poor interpretability. To overcome the shortcomings of existing methods and adapt to the changing trends in money laundering while ensuring good interpretability, this paper proposes an AML method based on Hierarchical Risk Control Knowledge Graph (HRCKG). Firstly, a simulated AML dataset is generated based on a small amount of real data and money laundering patterns. Next, various transaction features are extracted to construct graded risk control indicators, which are used to automatically search for and filter risk control rules to form a rule library, and then construct an HRCKG. Finally, integrating rule-based reasoning and graph-based reasoning, this paper uses rule matching, node importance measurement, and community discovery algorithms to evaluate the money laundering risks of accounts, identify money laundering accounts, and discover money laundering groups. Through comparison with other machine learning methods and analysis of experimental results, the effectiveness and application value of the proposed method is demonstrated.

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Artificial intelligence for knowledge graphs of cryptocurrency anti-money laundering in fintech

Cited by 11 publications

References 19 publications

Money laundering in global trade and the role of financial technology: The structured reviews

Money laundering in global trade and the role of financial technology: The structured reviews

Assessing the maturity of the current global system for combating financial and cyber fraud

Anti-money laundering method based on hierarchical risk control knowledge graph

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