The effectiveness of China’s anti-money laundering policies

Finding the best method to assess the effectiveness of Anti-Money Laundering(AML)policies is a controversial issue. Based on about 9,000 questionnaires circulated to AML professionals and other related staff at the People's Bank of China and other banking institutions,this study acquired first-hand data from respondents and has resulted in the following key findings:The effectiveness of the whole AML system is rated as"largely effective"in respect to China’s legislation,regulation and supervision,suspicious transaction monitoring and analyses and administrative investigation;the system is rated as"basically effective"in respect to money-laundering prosecutions and convictions and international cooperation.Financial institutions'compliance with AML regulations is rated as"largely effective"in respect to internal control,customer identification,large-value transaction and suspicious transaction reporting,and the record-keeping of ID information and transactions.Statistically,58.48%of respondents said they think that China’s AML regime is"completely effective"or"largely effective;"35.21%say it is"basically effective,"and the remaining 4.68% call it"largely ineffective"or"completely ineffective."The authors conclude by proposing some policy recommendations to enhance the effectiveness of AML policy.

Application Technologies and Challenges of Big Data Analytics in Anti-Money Laundering and Financial Fraud Detection

As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.

Applying Internal Control Procedures for Detecting and Preventing Money Laundering Operations in Banks＂ A Field Study in the Hashemite Kingdom of Jordan

This study aimed at identifying the role and importance of internal control procedures for detecting and preventing money laundering operations in banks through defining the internal control procedures which contribute to detecting money laundering operations. These procedures include the guide and policies issued by the administration of banks in order to combat laundering money operations as well as to train employees on matters pertaining to the money laundering operations. The study showed the role of the internal control procedures in detecting practically the money laundering through the automated programs and the system of saving the files and records. Furthermore, the study showed the factors affecting the internal control procedures to anti-money laundering operations. The researcher used an analytical descriptive approach for collecting data which relate to the main elements of the study, analyzing and explaining them. This study aimed at building the theoretical framework depending on audit literature which addressed internal control system, anti-money laundering systems, and control procedures of anti-money laundering. Through the theoretical framework, a questionnaire related to the application of internal control procedures and its relation to anti-money laundering operations was designed. It was distributed to the population of the study which includes internal and external auditors and the head of anti-money laundering operations unit in the Jordanian banks. The study found that applying internal control procedures is important for detecting and preventing money laundering operations in the Jordanian banks and that there are factors affecting the nature and the extent of internal control standards pertaining to anti-money laundering operations in the Jordanian banks.

TMAS:A transaction misbehavior analysis scheme for blockchain

Blockchain-based cryptocurrencies,such as Bitcoins,are increasingly popular.However,the decentralized and anonymous nature of these currencies can also be(ab)used for nefarious activities such as money laundering,thus reinforcing the importance of designing tools to effectively detect malicious transaction misbehaviors.In this paper,we propose TMAS,a transaction misbehavior analysis scheme for blockchain-based cryptocurrencies.Specifically,the proposed system includes ten features in the transaction graph,two heuristic money laundering models,and an analysis method for account linkage,which identifies accounts that are distinct but controlled by an identical entity.To evaluate the effectiveness of our proposed indicators and models,we analyze 100 million transactions and compute transaction features,and are able to identify a number of suspicious accounts.Moreover,the proposed methods can be applied to other cryptocurrencies,such as token-based cryptocurrencies(e.g.,Bitcoins)and account-based cryptocurrencies(e.g.,Ethereum).