On the use of data mining methods for money laundering detection based on financial transactions information

2018 ◽  
Author(s):  
Ayshan Gasanova ◽  
Alexander N. Medvedev ◽  
Evgeny I. Komotskiy ◽  
Kamen B. Spasov ◽  
Igor N. Sachkov
Keyword(s):  
Data Mining ◽  
Money Laundering ◽  
Use Of Data ◽  
Mining Methods ◽  
Financial Transactions

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.

The use of Data Mining methods to assess the reliability of bank lending borrowers

2017 ◽  
Vol 31 (2) ◽  
pp. 108-112
Author(s):  
N. P. Sidorova
Keyword(s):  
Data Mining ◽  
Bank Lending ◽  
Scoring Method ◽  
Credit Risks ◽  
Use Of Data ◽  
Mining Methods ◽  
Banking Risk

Estimates of credit banking risk is one of the topical tasks of banking. Correct and timely assessment of the reliability of the bank's customers who applied for the loan will help reduce the bank's losses associated with credit risks. To increase the efficiency and validity of making decisions on the issuance of a loan, Data Mining methods can be used. The article considers Data Mining technologies, which are applicable for the implementation of the scoring method of the borrower's assessment.

Use of data mining methods in planning and management at Russian wood enterprises

2021 ◽  
Vol 806 (1) ◽  
pp. 012038
Author(s):  
I A Zakharenkova ◽  
T P Belyaeva ◽  
I N Igotti ◽  
T O Terenteva
Keyword(s):  
Data Mining ◽  
Use Of Data ◽  
Planning And Management ◽  
Mining Methods

The Application of Data Mining in the Honeypot System

2014 ◽  
Vol 519-520 ◽  
pp. 189-192
Author(s):  
Zhuo Shi Li ◽  
Ran Shi Jiang ◽  
Jian Li
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Technology Research ◽  
Active Defense ◽  
Security Technologies ◽  
Use Of Data ◽  
Audit Data ◽  
Virtual Machine Technology ◽  
New Type ◽  
Mining Methods

Honeypot is a new type of active defense security technologies. This paper attempts to use of data mining methods to be mining and analysis of information collected on the honeypot system. Build a Windows system based on virtual machine technology research honeynet. Data collection be standardized and sequential pattern mining. Finding out the correlation between different data records and frequent with time-based sequence of audit data, which found that,select the law of value of the attack.

scholarly journals The Use of Data Mining Methods to Predict the Result of Infertility Treatment Using the IVF ET Method

2014 ◽  
Vol 39 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Paweł Malinowski ◽  
Robert Milewski ◽  
Piotr Ziniewicz ◽  
Anna Justyna Milewska ◽  
Jan Czerniecki ◽  
...  
Keyword(s):  
Data Mining ◽  
Treatment Process ◽  
Infertility Treatment ◽  
New Methods ◽  
Use Of Data ◽  
Treat Ment ◽  
Modern Methods ◽  
Mining Methods ◽  
Effective Analysis ◽  
Ivf Et

Abstract The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process.

S33.2: The use of data mining methods in the risk analysis of food borne diseases

2004 ◽  
Vol 46 (S1) ◽  
pp. 70-70
Author(s):  
Sabine Glaser ◽  
Susanne Menzler ◽  
Dirk Werber ◽  
Andrea Ammon ◽  
Lothar Kreienbrock
Keyword(s):  
Data Mining ◽  
Risk Analysis ◽  
Use Of Data ◽  
Food Borne ◽  
Mining Methods

Using data mining to process business data

2020 ◽  
pp. 71-77
Author(s):  
Chubukova ◽  
Ponomarenko ◽  
Nedbailo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Programming Languages ◽  
Complex Analysis ◽  
Primary Data ◽  
Business Analytics ◽  
Use Of Data ◽  
The Subject ◽  
Mining Methods ◽  
Using Data

The subject of the research is the approach to the possibility of applying data mining methods in the framework of business analytics in order to optimize the adoption of management decisions by the company.The purpose of writing this article is to study of data mining methods features use of primary data, which act as a valuable resource of the company, which can be used to ensure competitive- ness in a particular market. Methodology. The research methodology is system- structural and comparative analyzes (to study the approaches of data mining data for the complex analysis of first data); monograph (studying the preconditions for the growth of data mining companies’ use in the process of data research); eco- nomic analysis (when assessing the feasibility of using machine learning methods to ensure the goals of business intelligence). The scientific novelty consists the peculiarities of data mining application as one of the directions of business analyt- ics are determined, which makes it possible to determine implicit relationships between known factor and result characteristics on the basis of primary data. The main directions of data manipulation are revealed: classification and forecasting, as well as correlation-regression analysis. The importance of using the basic meth- ods of statistical analysis in the process of studying primary data is proved. The specifics of the use of neural networks as one of the most important methods of machine learning are given. Conclusions. The use of data mining allows companies to increase the efficiency of the use of available data and optimize development strategies accordingly. The presence of a large number of machine learning meth- ods and statistical approaches expands the possibilities of comprehensive data analysis. Innovative technologies and specialized programming languages play an important role in this case.

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