The role of financial investigations in combating money laundering

Zhamiyeva, Roza; Sultanbekova, Gulmira; Abzalbekova, Maral T.; Zhakupov, Bakytzhan A.; Kozhanov, Murat G.

doi:10.1504/ijesdf.2022.121183

Cited by 6 publications

(3 citation statements)

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“…The other is the index to measure the motivation or ability of the company to violate the rules [18][19] . The research results of the model construction show that there are related indicators that can be used to identify the risks of financial report fraud, which makes the model construction significant, and then financial report users can use the model to measure and avoid risks, making contributions to standardizing the efficiency of the capital market.…”

Section: Results and Analysismentioning

confidence: 99%

Risk Identification Method of Enterprise AccountingInformation Fraud Based on Weighted Association Rule Algorithm

Li¹,

Yao²

2022

Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)

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In order to reduce the financial risks existing in enterprises, this paper studies the risk identification of enterprise accounting information fraud with weighted key algorithm. Based on the study of fuzzy association rules, this study analyzes the financial risks of enterprises, puts forward an analysis model of enterprise financial risks based on fuzzy association rules mining algorithm, and makes a qualitative analysis of enterprise financial crises, so as to predict enterprise financial risks and provide reference for enterprises to avoid risks. Comparing the correlation algorithm with the linear regression model, the result shows that the prediction error rate of the linear regression model is higher than that of the correlation algorithm model. Class I misjudgment rate is 40% higher, class II misjudgment rate is flat, overall misjudgment rate is 20% higher, and accuracy linear regression is 20% lower than correlation algorithm. It can be seen that the misjudgment rate of the correlation algorithm model is low, the prediction accuracy is high, and the discrimination and prediction ability of the linear regression model is poor, which proves the effectiveness of the application of the correlation algorithm in this paper.

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Section: Results and Analysismentioning

confidence: 99%

Risk Identification Method of Enterprise AccountingInformation Fraud Based on Weighted Association Rule Algorithm

Li¹,

Yao²

2022

Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)

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“…The anonymity inherent in transactions and the paucity of regulatory measures in the cryptocurrency arena render money laundering a profound concern in probes [38,39]. Nevertheless, enhancements in blockchain analytical tools are progressively equipping investigators to pinpoint suspicious transactions and isolate the involved parties [40].…”

Section: The Predicament Of Money Launderingmentioning

confidence: 99%

Cryptocurrency Investigations in Digital Forensics: Contemporary Challenges and Methodological Advances

Raza,

Shaikh,

Tahira

2023

IDA

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Digital forensics, a crucial subset of cybersecurity, encompasses sophisticated tools and methodologies for the interpretation, analysis, and investigation of digital evidence, facilitating the identification and mitigation of cybercrimes and security breaches. With the advent of cryptocurrencies, an array of unique challenges has emerged in the domain of digital forensic investigations. This review elucidates the prevailing state of digital forensic practices vis-à-vis cryptocurrencies, emphasizing the obstacles and limitations inherent in probing decentralized and intricate technologies. Notable deficiencies in extant investigative practices were observed. Solutions proffered encompass the formulation of novel software applications tailored for cryptocurrency analyses, the integration of machine learning and artificial intelligence capabilities, and the employment of advanced analytics to discern patterns and irregularities within blockchain transactions. Furthermore, a pioneering methodology, merging traditional digital forensic strategies with blockchain-specific techniques, is posited for efficacious cryptocurrency inquiries. The analysis underscores the imperative for a renewed paradigm in digital forensic examinations to surmount the challenges integral to cryptocurrency probes. By forging novel methodologies and standardizing investigative procedures, support for legal enforcement endeavors can be enhanced, facilitating the efficacious detection and prosecution of cryptocurrency-associated misdemeanors.

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“…Traditional money laundering prediction methods rely on a series of detection, identification, and reporting procedures to assist financial institutions and regulatory authorities in detecting suspicious transactions and taking appropriate countermeasures [8]. However, with the continuous advancement of technology, emerging digital financial technologies and intelligent criminal methods have also propelled innovation in money laundering prediction technologies [9]. Statistical methods and machine learning methods are playing increasingly important roles in money laundering prediction strategies and are currently the most widely used approaches in the field.…”

Section: Introductionmentioning

confidence: 99%

A Novel Money Laundering Prediction Model Based on a Dynamic Graph Convolutional Neural Network and Long Short-Term Memory

Wan,

2024

Symmetry

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Money laundering is an illicit activity that seeks to conceal the nature and origins of criminal proceeds, posing a substantial threat to the national economy, the political order, and social stability. To scientifically and reasonably predict money laundering risks, this paper focuses on the “layering” stage of the money laundering process in the field of supervised learning for money laundering fraud prediction. A money laundering and fraud prediction model based on deep learning, referred to as MDGC-LSTM, is proposed. The model combines the use of a dynamic graph convolutional network (MDGC) and a long short-term memory (LSTM) network to efficiently identify illegal money laundering activities within financial transactions. MDGC-LSTM constructs dynamic graph snapshots with symmetrical spatiotemporal structures based on transaction information, representing transaction nodes and currency flows as graph nodes and edges, respectively, and effectively captures the relationships between temporal and spatial structures, thus achieving the dynamic prediction of fraudulent transactions. The experimental results demonstrate that compared with traditional algorithms and other deep learning models, MDGC-LSTM achieves significant advantages in comprehensive spatiotemporal feature modeling. Specifically, based on the Elliptic dataset, MDGC-LSTM improves the Macro-F1 score by 0.25 compared to that of the anti-money laundering fraud prediction model currently considered optimal.

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The role of financial investigations in combating money laundering

Cited by 6 publications

References 0 publications

Risk Identification Method of Enterprise AccountingInformation Fraud Based on Weighted Association Rule Algorithm

Risk Identification Method of Enterprise AccountingInformation Fraud Based on Weighted Association Rule Algorithm

Cryptocurrency Investigations in Digital Forensics: Contemporary Challenges and Methodological Advances

A Novel Money Laundering Prediction Model Based on a Dynamic Graph Convolutional Neural Network and Long Short-Term Memory

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