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International Journal of Latest Research in Science and Technology

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Expert System for the Detection of Suspicious Banking Transactions of Money Laundering

Research Paper Open Access

International Journal of Latest Research in Science and Technology Vol.7 Issue 3, pp 1-9,Year 2018

EXPERT SYSTEM FOR THE DETECTION OF SUSPICIOUS BANKING TRANSACTIONS OF MONEY LAUNDERING

Juan Francisco Sabas González,Tonáhtiu Arturo Ramírez Romero ,Miguel Patiño Ortiz

Received : 27 April 2018; Accepted : 17 May 2018 ; Published : 27 June 2018

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Article No. 10938
Abstract

Money laundering (ML) is one of the main issues today, because it has a great impact on the economy and society of the countries. This crime is carried out by criminal organizations that need to hide the origin of illicit money obtained from their activities, and this is achieved by disguising illicit money transactions through banks. In Mexico, in the year 2015 ten thousand millions of dollars were washed, for this reason an expert system was created, in this paper presents how the structure of the proposed expert system works, and that its main function is the evaluation of clients' transactions by an algorithm with a decision tree approach and based on rules to identify if their transactions are suspicious.

Key Words   
Detection, Expert system, Money Laundering, Suspicious Transactions
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References
  1. (2015). Obtenidode http://www.gob.mx/shcp/ documentos / shcp- unidad- de- inteligencia - financiera - uifJ.
  2. Governance, B. I. (2015). Obtenido de https://www.baselgovernance.org/publications.
  3. Patcha, A., & Park, J.-M. (2007). An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 3448-3470. doi:dx.doi.org/10.1016/j.comnet.2007.02.001
  4. Gilmour, N. (2016). Understanding the practices behind money laundering – A rational choice interpretation. International Journal of Law, Crime and Justice, 1-13. doi:10.1016/j.ijlcj.2015.03.002
  5. Nan Wang, S., & Gang Yang, J. (2007). A Money Laundering Risk Evaluation Method Based on Decision Tree. Sixth International Conference on Machine Learning and Cybernetics, 19-22. doi: 10.1109/ICMLC.2007.4370155
  6. Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning 1(1), 81–106. doi:10.1023/A:1022643204877
  7. Jayasree, V., & Siva Balan, R. V. (2016). Money Laundering Regulatory Risk Evaluation Using Bitmap Index-Based Decision Tree. Journal of the Association of Arab Universities for Basic and Applied Sciences. doi:10.1016/j.jaubas.2016.03.001
  8. Dre_zewski, J. S. (2012). System supporting money laundering detection. Information Science, 8–21.
  9. Gao, S., & Xu, D. (2009). Conceptual Modeling and Development of an Intelligent Agent-Assisted Decision Support System for Anti-Money Laundering. Expert Systems with Applications, 1493–1504. doi:10.1016/j.eswa.2007.11.059
  10. Wang, X., & Dong, G. (2009). Research on Money Laundering Detection Based on Improved Minimum Spanning Tree Clustering and Its Application. Knowledge Acquisition and Modeling, 62-64. doi:10.1109/KAM.2009.221
  11. Larik , A., & Haider, S. (2011). Clustering Based Anomalous Transaction Reporting. Elsevier, 606-610. doi:10.1016/j.procs.2010.12.101
  12. Raza, S., & Haider, S. (2011). Suspicious activity reporting using dynamic bayesian networks. Procedia Computer Science 3, 987–991. doi:10.1016/j.procs.2010.12.162
  13. Luo, X. (2014). Suspicious Transaction Detection for Anti Money Laundering. International Journal of Security and Its Applications, 157-166. doi:10.14257/ijsia.2014.8.2.16
  14. Hong, X., Liang, H., Gao, Z., & Li, H. (2016). An Adaptive Resource Allocation Model in Anti-Money Laundering System. Springer Science. doi:10.1007/s12083-016-0430-y
  15. Tao Lv, L., Ji, N., & Long Zhang, J. (2008). A RBF Neural Network Model for Anti-Money Laundering. IEEE Computer Society, 209-215. doi:10.1109/ICWAPR.2008.4635778
  16. Umadevi, P., & Divya, E. (2011). Money Laundering Detection Using TFA System. doi:10.1049/ic.2012.0150
  17. Dreżewski, R., Sepielak, J., & Filipkowski, W. (2014). The Application of Social Network Analysis Algorithms in a System Supporting Money Laundering Detection. Information Science, 18-32. doi:10.1016/j.ins.2014.10.015
  18. Shafea, T. H.-M., Hossam Moustafa, T., Abd El-Megied, M. Z., Salah Sobh, T., & Mohamed Shafea, K. (2015). Anti Money Laundering Using a Two-Phase System. Journal of Money Laundering Control, 304 - 329. doi:10.1108/JMLC-05-2014-0015
  19. Flores, D. A., Angelopoulou, O., & Self, R. J. (2012). Combining Digital Forensic Practices and Database Analysis as an Anti-Money. Emerging Intelligent Data and Web Technologies, 218-224. doi:10.1109/EIDWT.2012.22
  20. Heidarinia, N., Harounabadi, A., &Sadeghzadeh, M. (2014). An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems. International Journal of Computer Applications, 35-39. doi:10.5120/17141-7780
  21. Yunkai, C., Quanwe, M., & Zhengding, L. (2006). Using Link Analysis Technique with a Modified Shortest.Path Algorithm to Fight Money Laundering. Wuhan University Journal of Natural Sciences, 1352-1356. doi:10.1007/BF02829265
  22. Young, C. (2014). Periodic Account Activity and Automated Money Laundering Detection. Journal of Money Laundering Control, 295-297. doi:10.1108/13685200310809707
  23. Ramírez Romero, T. A. (2013), Filtering the input data by production rules on open database, International Journal of Latest Research in Science and Technology, Volume 2, Issue 5; Page 1-8, September - October 2013
To cite this article

Juan Francisco Sabas González,Tonáhtiu Arturo Ramírez Romero ,Miguel Patiño Ortiz , " Expert System For The Detection Of Suspicious Banking Transactions Of Money Laundering ", International Journal of Latest Research in Science and Technology . Vol. 7, Issue 3, pp 1-9 , 2018


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