BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Michalak Krzysztof (Wrocław University of Economics, Poland), Korczak Jerzy (Wrocław University of Economics, Poland)
Title
Evolutionary Graph Mining in Suspicious Transaction Detection
Ewolucyjne drążenie grafów w wykrywaniu podejrzanych transakcji
Source
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 2011, nr 206, s. 120-129, rys., tab., bibliogr. 10 poz.
Research Papers of Wrocław University of Economics
Issue title
Advanced Information Technologies for Management - AITM 2011: Inteligent Technologies and Applications
Keyword
Grafy, Algorytmy, Pranie brudnych pieniędzy
Graphs, Algorithms, Money laundering
Note
streszcz., summ.
Abstract
W procederze prania brudnych pieniędzy wykorzystywane są złożone schematy organizacyjne mające na celu ukrycie prawdziwego celu wykonywanych transakcji. W tej publikacji opisana została metoda drążenia grafów, która pozwala na wykrywanie podgrafów zawierających podejrzane transakcje. Model reprezentujący podejrzane podgrafy jest parametryzowany za pomocą liczb rozmytych, które reprezentują parametry transakcji oraz niektóre własności strukturalne modelowanych podgrafów. Prezentowana metoda dokonuje rozmytego dopasowania struktury grafów, co pozwala na wykrywanie także takich podgrafów, które do pewnego stopnia różnią się od tych, które zostały zaanotowane przez eksperta. (abstrakt oryginalny)

Money laundering may involve complex organizational schemes designed to obfuscate the real purpose of money transfers. In this paper, we present a graph mining method that allows detection of transaction subgraphs containing suspicious transactions. Suspicious subgraph model is parameterized using fuzzy numbers which represent parameters of transactions and some structural features of the transaction subgraphs itself. The method presented in this paper uses fuzzy matching of graph structures which allows detecting money-laundering schemes which differ to some extent from those annotated by an expert. (original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business
The Main Library of the Wroclaw University of Economics
Bibliography
Show
  1. Buckley J.J., Eslami E. (2002), Introduction to Fuzzy Logic and Fuzzy Sets, Physica-Verlag, Heidelberg.
  2. Cook D.J., Holder L.B. (2007), Mining Graph Data, John Wiley and Sons, Hoboken.
  3. Fetz Th., Jager J., Koll D., Krenn G., Lessmann H., Oberguggenberger M., Stark R. (1999), Fuzzy models in geotechnical engineering and construction management, Computer-Aided Civil and Infrastructure Engineering, Vol. 14, No. 2, pp. 93-106.
  4. Goldberg D. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading.
  5. Goldberg D., Sastry K. (2011), Genetic Algorithms: The Design of Innovation, Springer.
  6. Hasancebi O., Erbatur F. (2000), Evaluation of crossover techniques in genetic algorithm based optimum structural design, Computers & Structures, Vol. 78, No. 1-3, pp. 435-448.
  7. Korczak J., Marchelski W., Oleszkiewicz B. (2008), A new technogical approach to money laundering discovery using analytical SQL server, [in:] J. Korczak, H. Dudycz, M. Dyczkowski (Eds.), Advanced Information Technologies for Management - AITM 2008, Research Papers of Wrocław University of Economics No. 35, Wrocław University of Economics, Wrocław, pp. 80-104.
  8. Korczak J., Oleszkiewicz B. (2009), Modelling of data warehouse dimensions for AML systems, [in:] J. Korczak, H. Dudycz, M. Dyczkowski (Eds.), Advanced Information Technologies for Management - AITM 2009, Research Papers of Wrocław University of Economics No. 85, Wrocław University of Economics, Wrocław, pp. 146-159.
  9. Truman E.M., Reuter P. (2004), Chasing Dirty Money: The Fight Against Anti-money Laundering, Peterson Institute for International Economics.
  10. Zhong J., Hu X., Zhang J., Gu M. (2005), Comparison of performance between different selection strategies on simple genetic algorithms, [in:] Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06), Vol. 02, IEEE Computer Society, pp. 1115-1121.
Cited by
Show
ISSN
1899-3192
Language
eng
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu