International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Design of a Monitor for Detecting Money Laundering and Terrorist Financing

Author NameAuthor Details

Tamer Hossam Eldin Helmy, Mohamed zaki Abd-ElMegied, Tarek S. Sobh, Khaled Mahmoud Shafea Badran

Tamer Hossam Eldin Helmy[1]

Mohamed zaki Abd-ElMegied[2]

Tarek S. Sobh[3]

Khaled Mahmoud Shafea Badran[4]

[1]System and Computer Engineering, Military Technical Collage, Cairo, Egypt.

[2]Systems and Computer Department, Al Azhar University, Cairo, Egypt.

[3]Information System Department, Egyptian Armed Forces, Cairo, Egypt.

[4]System and Computer Engineering, Military Technical Collage, Cairo, Egypt.

Abstract

Money laundering is a global problem that affects all countries to various degrees. Although, many countries take benefits from money laundering, by accepting the money from laundering but keeping the crime abroad, at the long run, “money laundering attracts crime”. Criminals come to know a country, create networks and eventually also locate their criminal activities there. Most financial institutions have been implementing anti-money laundering solutions (AML) to fight investment fraud. The key pillar of a strong Anti-Money Laundering system for any financial institution depends mainly on a well-designed and effective monitoring system. The main purpose of the Anti-Money Laundering transactions monitoring system is to identify potential suspicious behaviors embedded in legitimate transactions. This paper presents a monitor framework that uses various techniques to enhance the monitoring capabilities. This framework is depending on rule base monitoring, behavior detection monitoring, cluster monitoring and link analysis based monitoring. The monitor detection processes are based on a money laundering deterministic finite automaton that has been obtained from their corresponding regular expressions.

Index Terms

Anti Money Laundering system

Money laundering monitoring and detecting

Cycle detection monitoring

Suspected Link monitoring

Reference

  1. 1.
    P. Lilley, “Dirty dealing, the untold truth about global money laundering, international crime and terrorism”, Kogan page limited, 2000.
  2. 2.
    P. Reuter, and E. Truman, “Chasing dirty money”, Institute for international economics, Washington, USA, 2005.
  3. 3.
    G. SHIJIA, X. Dongming, W. Huaiqing, W. Yingfeng, “Intelligent Anti Money Laundering System”, Service Operations And Logistics, And Informatics, Ieee International Conference, Pp. 851-856, 2006.
  4. 4.
    R. Molander, D. Mussington, and P. Wilson, “Cyberpayments and money laundering problems and promise”, Financial crime enforcement network, RAND Critical technology institute, Washington, USA, 1998.
  5. 5.
    F. Kwong, “Anti Money Laundering”, Computer Assisted Auditing Techniques, Retrieved 1 July 2011 available at http://uwcisa.uwaterloo.ca/Biblio2/Topic/ACC626%20Anti%20Money%20Laundering%20F%20Kwong.pdf
  6. 6.
    Pellegrina, L. D, and Masciandaro, “The Risk Based Approach in the New European Anti-Money Laundering Legislatin”,Retrieved May 29, 2011, available at http://www.bepress.com/cgi/viewcontent.cgi?article=1422&context=rle
  7. 7.
    J. Wit, “A Risk-Based Approach to AML: A Controversy between Financial Institutions and Regulators”, Journal of Financial Regulation and Compliance, Vol. 15, PP. 156-165, 2007.
  8. 8.
    T. Senator, H. Goldberg, J. Wooton, M. Cottini, U. Khan, C. Klinger, W. Liamas, M. Marrone, R. Wong, “The FinCEN Artificial Intelligence System: Identifying Potential Money Laundering from Reports of large cash transactions”, U.S. Department of the Treasury - Financial Crimes Enforcement Network (FinCEN), Association for the Advancement of Artificial Intelligence, USA, Vol. 16, No. 4, PP. 156-170, 1995.
  9. 9.
    T. Jun. “A Peer Dataset Comparison Outlier Detection Model Applied to Financial Surveillance”. Proceedings of the 18th International Conference on Pattern Recognition, IEEE Computer Society, Washington, USA, Vol. 4, PP. 900-903, 2006.
  10. 10.
    S. Wang, J. Yang, “A money laundering risk evaluation method based on decision tree”, Proceedings of the International Conference on Machine Learning and Cybernetics, PP. 283-286, Hong Kong, 2007.
  11. 11.
    Y.Wang, H. Wang, S. Gao, D. Xu, “Intelligent Money Laundering Monitoring and Detecting System”, European and Mediterranean Conference on Information Systems, Dubai, available at “http://www.iseing.org/emcis/emcis2008/Proceedings/Refereed%20Papers/Contributions/C%2045/Intelligent_Money_Laundering_Monitoring_and_Detecting_System.pdf “, 2008.
  12. 12.
    N. Le-Khac, S. Markos, M. Kechadi, “A Heuristics Approach for Fast Detecting Suspicious Money Laundering Cases in an Investment Bank”, International Scholarly and Scientific Research AND Innovation, Engineering and Technology, Vol. 3, No. 12, PP. 70-74, 2009.
  13. 13.
    J. Luell, “Employee Fraud Detection under Real World Conditions”, Ph.D. thesis, University of Zurich, 2010.
  14. 14.
    Anti-Money Laundering Countering Financing of Terrorism, “National Risk Assessment 2010”, Financial Intelligence Unit, New Zealand Police, New Zealand, available at http://www.justice.govt.NZ/policy/criminal-justice/AML-CFT/publications-AND-consultation/national-risk-assessment-2010, 2010.
  15. 15.
    Financial Action Task Force, “International Standards on Combating Money Laundering and the Financing of Terrorism and Proliferation – The FATF Recommendations”, available at “www.fatf-gafi.org/recommendations “, 2012.
  16. 16.
    Financial Transactions and Reports Analysis Center, Typologies and Trends, “Money Laundering and Terrorist Financing (ML/TF) Typologies and Trends for Canadian Money Services Businesses (MSBs)”, FINTRAC Reports of Canada, Canada, 2010.
  17. 17.
    The Canadian Institute of Chartered Accountants, “Canada’s Anti-Money Laundering and Anti-Terrorist Financing Requirements”, available at www.cica.ca, 2008.
  18. 18.
    Belgian Financial Intelligence Processing Unit, “Money Laundering Indicators”, available at http://www.ctif-cfi.be/website/images/EN/typo_ctifcfi/NL1175eENG.pdf, 2007.
  19. 19.
    E. Garcia, P. Regan, J. Stern, W. Johnson, R. Macallister, J. Reidenberg, D. vogt, R. Serino, B. Proter, S. Welling, “Information Technologies for the Control of Money Laundering”, OTA-ITC-630, Washington, USA, 1995.
  20. 20.
    Australian Transaction Reports and Analysis Center, “Money Laundering in Australia, Australia Government, available at http://www.austrac.gov.au/files/money_laundering_in_australia_2011.pdf, 2011.
  21. 21.
    R. Fuhrer, “Sequential Optimization of Asynchronous and Synchronous Finite-State Machine: Algorithms and Tools”, Springer, 2001.
  22. 22.
    D. Lee, “Principles and Methods of Testing Finite State Machine survey”, Proceedings of the IEEE, Vol. 84, No. 8, PP. 1089-1123, 1996.
  23. 23.
    A. Aho, R. Sethi, J. Ullman, “Compilers: Principles, Techniques, and Tools Second Edition”, Addison Wesley, 2006.
  24. 24.
    The Center for Public Integrity, available at http://www.publicintegrity.org/2012/10/18/11498/pennsylvania-governor-benefited-untraceable-15-million-donation, 2012.
IJCNA NPC