CFP last date
20 June 2024
Reseach Article

An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems

by Neda Heidarinia, Ali Harounabadi, Mehdi Sadeghzadeh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 22
Year of Publication: 2014
Authors: Neda Heidarinia, Ali Harounabadi, Mehdi Sadeghzadeh
10.5120/17141-7780

Neda Heidarinia, Ali Harounabadi, Mehdi Sadeghzadeh . An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems. International Journal of Computer Applications. 97, 22 ( July 2014), 35-39. DOI=10.5120/17141-7780

@article{ 10.5120/17141-7780,
author = { Neda Heidarinia, Ali Harounabadi, Mehdi Sadeghzadeh },
title = { An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 22 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number22/17141-7780/ },
doi = { 10.5120/17141-7780 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:24:50.245886+05:30
%A Neda Heidarinia
%A Ali Harounabadi
%A Mehdi Sadeghzadeh
%T An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 22
%P 35-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

During the last decades, universal economy has experienced money laundering and its destructive impact on the economy of the countries. Money laundering is the process of converting or transferring an asset in order to conceal its illegal source or assist someone that is involved in such crimes. Criminals generally attempt to clean the sources of the funds obtained by crime, using the banking system. Due to the large amount of information in the banks, detecting such behaviors is not feasible without anti-money laundering systems. Money laundering detection is one of the areas, where data mining tools can be useful and effective. In this research, some of the features of the users are extracted from their profiles by studying them. These features may include large financial transactions in risky areas regarding money laundering, reactivation of dormant accounts with considerable amounts, etc. Network training is performed by designing a fuzzy system, developing an adaptive neuro-fuzzy inference system and adding feature vectors of the users to it. The network output can determine the riskiness of the user behavior. The evaluation results reveal that the proposed method increases the accuracy of detecting risky users.

References
  1. Qifeng, Y. Bin, F. Ping, S. Sept. 2007. Study on Anti-Money Laundering Service System of Online Payment based on Union-Bank mode. International Conference on Wireless Communications, Networking and Mobile Computing. pp: 4991-4994.
  2. TIANQING, Z. Dec. 2006. An Outlier Detection Model Based on Cross Datasets Comparison for Financial Surveillance. Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06), pp: 601-604.
  3. WANG, S. YANG, J. August 2007. A Money Laundering Risk Evaluation Method Based on Decision Tree. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong.
  4. Xuan, L. Pengzhu, Z. Sept. 2007. An Agent based Anti-Money Laundering System Architecture for Financial Supervision. International Conference on Wireless Communications, Networking and Mobile Computing. pp: 472-5475.
  5. Gao, SH. Xu, D. 2007. Conceptual Modeling and Development of an Intelligent Agent-Assisted Decision Support System for Anti-Money Laundering. Expert System with Applications, doi:10. 1016/j. eswa.
  6. Gao, SH. Xu, D. Wang, H. Wang, Y. 2006. Intelligent Anti-money Laundering System. International Conference on Service Operation and Loqistics, and Informatics, SOLI'06, IEEE. pp: 851-856.
  7. LV , L. T. , JI , N. , ZHANG, J. L. , 2008. A RBF Neural Network Model for Anti-Money Laundering. Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition. IEEE, PP:209-215.
  8. Michalak, K. , Korczak, J. 2011. Graph Mining Approach to Suspicious Transaction Detection. Proceedings of the Federated Conference on Computer Science and Information Systems. IEEE , PP:69-75.
  9. Quah, J. T. S, Sriganesh, M. 2007. Real-Time Credit Card Fraud Detection using Computational Intelligence. Expert Systems with Applications, pp. 9-17.
  10. Fang, L. Cai, M. Fu, H. Dong, J. 2007. Ontology-Based Fraud Detection. in Computational Science. pp:1048-1055 .
  11. Jang, J. R. Sun, C. 1995. Nero Fuzzy Modelling and Control. Proc. of the IEEE. PP: 378-405.
  12. Kuo, R. J. Chen, C. H. Hwang, Y. C. 2001. An Intelligent Stock Trading Decision Support System through Integration of Genetic Algorithm based Fuzzy Neural Network and Artificial Neural Network. Fuzzy Sets and Systems118.
  13. jang , J. S. R. 1993. ANFIS : Adaptive – Network based Fuzzy Inference Systems. Department of Electrical Engineering and Computer Science ,University of California Berkeley.
  14. Parker, D. B. 1985. Learning Logic: Casting the Cortex of the Human Brain in Silicon. technical report TR-47. Cambridge, MA: Center for Computational Research in Economics and Management, MIT.
  15. Siler W. Buckley J. J. 2005. Fuzzy Expert Systems and Fuzzy Reasoning. New Jersey, Wiley Interscience, 54.
  16. Ata, R. Kocyigit, Y. 2010. An Adaptive Neuro-Fuzzy Inference System Approach for Prediction of Tip Speed Ratio in Wind Turbines. Expert Systems with Applications. pp: 5454-5460.
Index Terms

Computer Science
Information Sciences

Keywords

Money laundering Neuro-Fuzzy ANFIS