scholarly journals A Data Mining-Based Solution for Detecting Suspicious Money Laundering Cases in an Investment Bank

2010 ◽  
Author(s):  
Nhien An Le Khac ◽  
Sammer Markos ◽  
M-Tahar Kechadi
Keyword(s):  
Data Mining ◽  
Money Laundering ◽  
Investment Bank

Data mining in anti-money laundering field

2017 ◽  
Vol 20 (3) ◽  
pp. 301-310 ◽  
Author(s):  
Noriaki Yasaka
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Money Laundering ◽  
Design Methodology ◽  
Point Of View ◽  
Important Task ◽  
Content Type ◽  
Suspicious Transaction ◽  
Mining Methods ◽  
The Creation

Purpose This report aims to focus on how suspicious transaction report is created with data mining methods and used from the point of view of knowledge management. Design/methodology/approach This paper considers data mining versus knowledge management in the anti-money laundering (AML) field. Findings In the AML field, the information and knowledge gained are not necessarily used for or shared with the related shareholders. Creating and co-evolving the network of “knowledge professionals” is the impending assignment in this industry. The first and most important task is knowledge management in the global AML field. Originality/value The report considers the creation with data mining methods and utilization from the point of view of knowledge management.

scholarly journals Data mining for statistical analysis of money laundering transactions

2019 ◽  
Vol 22 (4) ◽  
pp. 753-763
Author(s):  
Mark Eshwar Lokanan
Keyword(s):  
Data Mining ◽  
Statistical Analysis ◽  
Money Laundering ◽  
Service Sector ◽  
Empirical Studies ◽  
Financial Service ◽  
Statistical Techniques ◽  
Content Type ◽  
Regulatory Policies ◽  
Statistical Data Mining

Purpose The purpose of this paper is to use statistical techniques to mine and analyze suspicious transactions. With the increase in money laundering activities across various sectors in some of the world’s leading democracies, the ability to detect such transactions is gaining grounds with more urgency. Regulators and practitioners have been calling for an approach that can mine the large volume of unstructured data form suspicious money laundering transactions to inform public policies. Design/methodology/approach By deducing from the results of empirical studies in the field of money laundering detection, this paper presented an overview of data mining technology for detecting suspicious transactions. Findings After chronicling the data mining process, the paper delves into an analysis of the statistical approaches that can be used to differentiate between legitimate and suspicious money laundering transactions. The different stages of the data mining process are carefully explained in relation to their application to anti-money laundering compliance. The results indicate that statistical data mining methodology is a very efficient and useful technique to detect suspicious transactions. Practical implications The paper is of relevance to regulators and the financial service sector. A discussion of how data can be mined to facilitate statistical analysis can be used to inform regulatory policies on the detection and prevention of money laundering activities in the financial service sector. Originality/value The paper discuss approaches that illustrate how analysts can use statistical techniques to analyze data for suspicious money laundering transactions

scholarly journals A methodology for highlighting nodes in graphs applied to the analysis of financial intelligence reports

2020 ◽  
Author(s):  
Roberto Zaina ◽  
Gustavo Medeiros de Araujo ◽  
Vinicius Faria Culmant Ramos
Keyword(s):  
Data Mining ◽  
Money Laundering ◽  
Link Analysis ◽  
Computational Techniques ◽  
Large Set ◽  
Analysis Program ◽  
Research Information ◽  
Proposal A ◽  
Criminal Investigators ◽  
Financial Intelligence

Money laundering is a category of crime that requires great efforts by criminal investigators to gather a variety of information in order to set the context for an investigation. One of the sources for starting the investigation and the search for adjacent information is the Financial Intelligence Report. From this report, the researcher dives into a large set of data and information to form the panorama of the investigation. With all the information gathered and interconnected, a graph is obtained in which one can use computational techniques to search for and highlight the main ones involved in the report. Since the size of the graph and the number of nodes can take on large proportions, which would make it difficult to identify the main people, companies and financial operations, this work presents as a 2 Zaina, Roberto; Araujo*, Gustavo Medeiros de; Ramos, Vinicius Faria Culmant (2020). Uma metodologia para destaque de nós em grafos aplicada à análise de relatórios de inteligência financeira (preprint). Ciência da Informação. Disponibilizado em EmeRI - Emerging Research Information. (preprints.ibict.br) DOI: 10.21452/15188353202000002. proposal, a methodology supported by technology to highlight the main ones involved in the investigation. The methodology adopted was data mining guided by metrics such as "suspicious companies" and "suspicious accountants". With the result of the data mining, a link analysis program was loaded forming the graph with the information from the highlighted nodes, representing the main ones involved in the investigation. This methodology helps the criminal investigator, as it facilitates the processing of large volumes of data and helps to decrease the complexity of the information arising from the Financial Intelligence Reports.

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