Nida S. Khan, Asma S. Larik, Quratulain Rajput, and Sajjad Haider
[1] J. Pearl, Probabilistic reasoning in intelligent systems: Network of plausible inference (San Francisco, California: Morgan Kaufmann Publishers Inc., 1987). [2] Z. Gao and M. Ye, A framework for data mining-based anti-money laundering research, Journal of Money Laundering Control, 10(2), 2007, 170–179. [3] P. Lewisch, Money laundering laws as a political instrument: The social cost of arbitrary money laundering enforcement, European Journal of Law and Economics, 26(3), 2008, 405–417. [4] L.T. Lv, N. Ji, and J.L. Zhang, A RBF neural network model for anti-money laundering, Proc. Int’l Conf. on Wavelet Analysis and Pattern Recognition, Hong kong, China, 2008. [5] S.N. Wang and J.G. Yang, A money laundering risk evaluation method based on decision tree, Proc. Int’l Conf. on Machine Learning and Cybernetics, Hong kong, China, 2007. [6] J. Tang and J. Yin, Developing an intelligent data discriminating system of anti-money laundering based on SVM, Proc. Int’l Conf. on Machine Learning and Cybernetics, Guangzhou, China, 2005. [7] J. Han and M. Kamber, Data mining: Concepts and techniques (San Francisco, California: Morgan Kaufmann Publishers Inc., 2006). [8] M. Anderberg, Cluster analysis for applications (USA: Academic Press, 1973). [9] G. Zengan, Application of cluster-based local outlier factor algorithm in anti-money laundering, Proc. Int’l Conf. on Management and Service Science, Wuhan/Beijing, China, 2009. [10] T. Zhu, An outlier detection model based on cross datasets comparison for financial surveillance, Proc. 2006 IEEE Asia-Pacific Conf. on Service Computing, Guangzhou, China, 2006. [11] T. Jun, A peer dataset comparison outlier detection model applied to financial surveillance, Proc. 18th International Conf. on Pattern Recognition, Hong kong, China, 2006. [12] M.F. Jaing, S.S. Tseng, and C.M. Su, Two-phase clustering process for outliers detection, Pattern Recognition Letters, 22, 2001, 691–700. [13] X. Wang and G. Dong, Research on money laundering detection based on improved minimum spanning tree clustering and its application, Proc. Int’l Symposium on Knowledge Acquisition and Modeling, Wuhan, China, 2009. [14] S. Gao, D. Xu, H. Wang, and Y. Wang, Intelligent anti-money laundering system, Proc. IEEE Int’l Conf. on Service Operations and Logistics, and Informatics, Shanghai, 2006. [15] S. Raza and S. Haider, Suspicious activity reporting using dynamic Bayesian network, Procedia Computer Science, 3, 2011, 987–991. [16] A. Larik and S. Haider, Clustering based anomalous transaction reporting, Procedia Computer Science, 3, 2011, 606–610. [17] R. Kenaya and K.C. Choek, Euclidean ART neural networks, Proc. World Congress on Engineering and Computer Science, 2008. [18] Y. Li, D. Duan, G. Hu, and Z. Lu, Discovering hidden group in financial transaction network using hidden Markov model and genetic algorithm, Proc. 6th Int’l Conf. on Fuzzy Systems and Knowledge Discovery, Tianjin, China, 2009. [19] V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: A survey, ACM Computer Survey, 41, 2009, 1–58.
Important Links:
Go Back
