The Research of an Incremental Conceptive Clustering Algorithm and Its Application in Detecting Money Laundering

Considering the constantly increasing of data in large databases such as wire transfer database, incremental clustering algorithms play a more and more important role in Data Mining （DM）. However, Few of the traditional clustering algorithms can not only handle the categorical data, but also explain its output clearly. Based on the idea of dynamic clustering, an incremental conceptive clustering algorithm is proposed in this paper. Which introduces the Semantic Core Tree （SCT） to deal with large volume of categorical wire transfer data for the detecting money laundering. In addition, the rule generation algorithm is presented here to express the clustering result by the format of knowledge. When we apply this idea in financial data mining, the efficiency of searching the characters of money laundering data will be improved.

Using Link Analysis Technique with a Modified Shortest-Path Algorithm to Fight Money Laundering

Effective link analysis techniques are needed to help law enforcement and intelligence agencies fight money laundering. This paper presents a link analysis technique that uses a modified shortest-path algorithms to identify the strongest association paths between entities in a money laundering network. Based on two-tree Dijkstra and Priority＇First-Search （PFS） algorithm, a modified algorithm is presented. To apply the algorithm, a network representation transformation is made first.

Money Laundering in the Central and Eastern Europe

The purpose of this paper is to provide an economic overview of the costs and benefits of anti-money laundering （AML） rules. After defining and explaining the three stages of money laundering, the paper provides an insight into the volume and development of money laundering activities in the Central and Eastern Europe. It relies on international, comparative studies outlines the impact of AML measures on banks and other financial intermediaries Conditions of reporting suspicious activity and government agencies, which use these reports to identify investigation targets, are also analysed. Moreover, the paper discusses possible reasons for the failure of AML rules to fight against the crimes and collateral damage caused by AML. These figures, which are presented in this scientific research, give an indication of how important the money laundering problem and the level of organized crime are.

The Correlation and Linear Regression Analysis between Annual GDP Growth Rate and Money Laundering in Albania during the Period 2007-2011

This study is the first attempt to investigate the relationship between the annual GDP growth rate and money laundering in the Republic of Albania during the period 2007-2011. The main result of the study: there is a negative correlation between money laundering process and economic growth rate in Albania during the specified period;there is a negative correlation between money laundering and import, but there is a positive correlation between money laundering and the government expenditure, as well a positive correlation between money laundering and export.

Identifying Money Laundering Process Using Indicators： The Case of Albania

The main purpose of this study is to develop a mathematical model for calculating the probability of money laundering process, by monitoring the behavior of the client using 70 indicators of money laundering. The scientific method used in this study （received from the Modern Criminology） has great investigative power and it is widely applicable. Hopefully the practical application of this study will increase greatly the probability of detection and punishment of the clients who are implicated in the process of money laundering. In particular, this study will be useful for banks, Financial Intelligence Unit （FIU） of Albania, Department of Economic Crime at the Ministry of Domestic Affairs and Albanian State Intelligence Service （SIS）. Also, the investigation of money laundering will be a useful tool to detect other crimes, such as drug trafficking, human trafficking, illegal arms trade, etc. The prevention of money laundering is simultaneously a powerful strike against terrorism both on national and international levels.

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

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 microaverage 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.

Are suspicious activity reporting requirements for cryptocurrency exchanges effective?

This study analyzes the impact of a newly emerging type of anti-money laundering regulation that obligates cryptocurrency exchanges to report suspicious transactions to financial authorities.We build a theoretical model for the reporting decision structure of a private bank or cryptocurrency exchange and show that an inferior ability to detect money laundering(ML)increases the ratio of reported transactions to unreported transactions.If a representative money launderer makes an optimal portfolio choice,then this ratio increases further.Our findings suggest that cryptocurrency exchanges will exhibit more excessive reporting behavior under this regulation than private banks.We attribute this result to cryptocurrency exchanges’inferior ML detection abilities and their proximity to the underground economy.

Fighting Financial Crime:Who Designs Global Governance and Who Does The Work?

Fighting financial crime is a highly institutionalised global governance task.At a time of crisis for many of the institutions of global governance,tackling money laundering and combatting terrorist financing through global cooperation continues to be a priority for public officials.The global regime,if anything,is intensifying.This essay provides an overview of the regime’s development and addresses questions of design and implementation.It is structured around three sets of questions:(1)What does the regime look like and what is it for?(2)Who does the work?(3)And,in conclusion,what can we say about winners and losers?