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Data Mining Model for Money Laundering Detection in Financial Domain

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 85 - Number 16
Year of Publication: 2014
Authors:
Mahesh Kharote
V. P. Kshirsagar
10.5120/14929-3337

Mahesh Kharote and V P Kshirsagar. Article: Data Mining Model for Money Laundering Detection in Financial Domain. International Journal of Computer Applications 85(16):61-64, January 2014. Full text available. BibTeX

@article{key:article,
	author = {Mahesh Kharote and V. P. Kshirsagar},
	title = {Article: Data Mining Model for Money Laundering Detection in Financial Domain},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {85},
	number = {16},
	pages = {61-64},
	month = {January},
	note = {Full text available}
}

Abstract

One of the likeliest problems faced by the banks is the way the nature of money transaction takes place is not always known to the bank. One such problem consisting large amounts of money transferring through various accounts by the same person or entity is Money Laundering. Money laundering system is quite a convoluted process. It takes some understanding of the fund transferring activities at various stages. The system that works against Money laundering is Anti-Money Laundering (AML) system. The existing system for Anti-Money Laundering accepts bulk of data and converts it to large volumes reports that are tedious and topsy-turvy for a person to read without any help of software. Hence, the purpose of making decision within specified time span is de-facilitated. The basic motive behind designing a system is that it should either notify or generate alarm for Money Laundering process within time when the illicit actions are been carried out so that, some action can be taken or an decision could be made before the actual laundering task is completed. The Transaction flow analysis (TFA) is a system that facilitates the basic ideology of Anti-Money Laundering system. TFA consists of various segments those results in clusters of various sizes. However, the existing TFA system allows the data to be taken from a single entity may be bank or a financial institution. Also the cluster generated are large in volume hence the analyst sometime may find it difficult to analyze such huge amount of data. This project uses combination of TFA system with the system designed to extract customer behavior in order to make decision whether the customer is suspicious customer or not. Also this project will allow taking data as input from various sources, integrating and then performing operations on the data.

References

  • Mark K. Y. Mak, George T. S. Ho, S. L. Ting "A Financial Data Mining Model for extracting Customer Behavior" 2011.
  • P. Umadevi, E. Divya "MONEY LAUNDERING DETECTION USING TFA SYSTEM" ICSEMA, International Conference on Digital Object Identifier, 2012.
  • Irina Ionita, Liviu Ionita "A Decision Support Based on Data Mining in e-Banking" 2011.
  • Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto "PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern" 2004.
  • Rujuta Shinde, Priya Vaghurdekar, Santaji Shinde "Deliberation of Data Mining in Banking" International Journal of Engineering Reaserch & Technology. ISSN: 2278-0181 Vol 1 Issue8, 2012.
  • Lv LT, Ji N, Zhang JL "A RBF neural network model for anti-money laundering". ICWAPR Internation Conference, 2008.
  • Kazi Imran Moin, Dr. Qazi Baseer Ahmed " Use of Data Mining in Banking" International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 2, Mar-Apr 2012.
  • Agrawal, R. , Imielinski, T. , Swami, A. N. "Data Mining: A performance perspective", IEEE Transactions on Knowledge and Data Engineering, vol, 5, no. 6, 1993.
  • Nenad, J. , Svetlozar, N. "Comprehensive data warehouse exploration with qualified association-rule mining, Decision Support Systems", vol. 42, no. 2, 2006.
  • Vivek Bhambri "Role of Data Mining in Banking Sector", International Indexed & Reffered Research Journal Vol. 3 Issue-33, 2012.
  • Dr. Madan Lal Bhasin, "Data Mining: A Competitive Tool in the Banking and Retail Industries", The Chartered Accountant October 2006.