Reference: | 1.Anti-Money Laundering Division (AMLD) (2017). Anti-Money Laundering Annual Report, 2017. 2.Anti-Money Laundering Division (AMLD) (2018). Anti-Money Laundering Annual Report, 2018. 3.Anti-Money Laundering Division (AMLD) (2019). Anti-Money Laundering Annual Report, 2019. 4.Anti-Money Laundering Office, Executive Yuan (2018, May). National Money Laundering and Terrorist Financing Risk Assessment Report. 5.Asia/Pacific Group on Money Laundering (2019). Mutual Evaluation Report of Chinese Taipei. 6.Ben-Gal, I. (2005). Outlier detection. In Data mining and knowledge discovery handbook (pp. 131-146). Springer, Boston, MA. 7.Bolton, R. J., and Hand, D. J. (2002). Statistical fraud detection: A review. Statistical science, 17(3), 235-255. 8.Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992, July). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory (pp. 144-152). 9.Breslow, S., Hagstroem, M., Mikkelsen, D., and Robu, K. (2017). The new frontier in anti–money laundering. McKinsey and Company, November. 10.Chen, Z., Teoh, E. N., Nazir, A., Karuppiah, E. K., and Lam, K. S. (2018). Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowledge and Information Systems, 57(2), 245-285. 11.Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297. 12.Dreżewski, R., Sepielak, J., and Filipkowski, W. (2015). The application of social network analysis algorithms in a system supporting money laundering detection. Information Sciences, 295, 18-32. 13.European Commission (2021). Proposal for a Regulation establishing the Authority for Anti-Money Laundering and Countering the Financing of Terrorism and amending Regulations (EU) No 1093/2010, (EU) 1094/2010, (EU) 1095/2010. 14.Fenergo officials (2018). Firms Fined $26B Over the Past Decade: Fenergo. 15.Financial Action Task Force on Money Laundering (FATF) (2009). Money Laundering and Terrorist Financing in the Securities Sector. 16.Financial Action Task Force on Money Laundering (FATF) (2012). FATF Recommendations 2012. 17.Financial Examination Bureau, FSC (2017). Template for Guidelines Governing Anti-Money Laundering and Countering Terrorism Financing of Securities Firms. 18.Financial Supervisory Commission (2017). Regulations Governing Anti-Money Laundering of Financial Institutions. 19.Gao, Z., and Ye, M. (2007). A framework for data mining-based anti-money laundering research. Journal of Money Laundering Control, 10(2), 170-179. 20.Garcia, E., Regan, P., Stern, J., Johnson, W., Macallister, R., Reidenberg, J., ... and Welling, S. (1995). Information Technologies for the Control of Money Laundering. OTA-ITC-630, Washington, DC. 21.Hartigan, J. A. (1975). Clustering algorithms. John Wiley and Sons, Inc.. 22.Hartigan, J. A., and Wong, M. A. (1979). AK‐means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 100-108. 23.Hsu, C. W., Chang, C. C., and Lin, C. J. (2003). A practical guide to support vector classification. 24.Hubert, M., and Vandervieren, E. (2008). An adjusted boxplot for skewed distributions. Computational statistics and data analysis, 52(12), 5186-5201. 25.Keerthi, S. S., and Lin, C. J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural computation, 15(7), 1667-1689. 26.Keyan, L., and Tingting, Y. (2011, November). An improved support-vector network model for anti-money laundering. In 2011 Fifth International Conference on Management of e-Commerce and e-Government (pp. 193-196). IEEE. 27.Kingdon, J. (2004). AI fights money laundering. IEEE Intelligent Systems, 19(3), 87-89. 28.Kirkland, J. D., Senator, T. E., Hayden, J. J., Dybala, T., Goldberg, H. G., and Shyr, P. (1999). The nasd regulation advanced-detection system (ads). AI Magazine, 20(1), 55-55. 29.Le Khac, N. A., and Kechadi, M. T. (2010, December). Application of data mining for anti-money laundering detection: A case study. In 2010 IEEE International Conference on Data Mining Workshops (pp. 577-584). IEEE. 30.Le-Khac, N. A., Markos, S., and Kechadi, M. T. (2009, September). Towards a new data mining-based approach for anti-money laundering in an international investment bank. In International Conference on Digital Forensics and Cyber Crime (pp. 77-84). Springer, Berlin, Heidelberg. 31.LexisNexis (2020). Global True Cost of Compliance 2020 report. 32.LexisNexis Risk Solutions (2018). 2018 True Cost of AML Compliance report for the United States. 33.Lin, H. T., and Lin, C. J. (2003). A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. submitted to Neural Computation, 3(1-32), 16. 34.Liu, R., Qian, X. L., Mao, S., and Zhu, S. Z. (2011, May). Research on anti-money laundering based on core decision tree algorithm. In 2011 Chinese Control and Decision Conference (CCDC) (pp. 4322-4325). IEEE. 35.Liu, X., Zhang, P., and Zeng, D. (2008, June). Sequence matching for suspicious activity detection in anti-money laundering. In International conference on intelligence and security informatics (pp. 50-61). Springer, Berlin, Heidelberg. 36.Mylevaganam, S. (2017). The Analysis of Human Development Index (HDI) for Categorizing the Member States of the United Nations (UN). Open Journal of Applied Sciences, 7(12), 661-690. 37.Noble, J. C. (2021). 7. Money Laundering. In White-Collar and Financial Crimes (pp. 91-104). University of California Press. 38.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. 39.Romaniuk, P., Haber, J., and Murray, G. (2007). Suspicious activity reporting. The CPA Journal, 77(3), 70. 40.Senator, T. E., Goldberg, H. G., Wooton, J., Cottini, M. A., Khan, A. U., Klinger, C. D., ... and Wong, R. W. (1995). Financial crimes enforcement network AI system (FAIS) identifying potential money laundering from reports of large cash transactions. AI magazine, 16(4), 21-21. 41.Syarif, I., Prugel-Bennett, A., and Wills, G. (2016). SVM parameter optimization using grid search and genetic algorithm to improve classification performance. Telkomnika, 14(4), 1502. 42.Tan, P. N., Steinbach, M., and Kumar, V. (2016). Introduction to data mining. Pearson Education India. 43.Tang, J., and Yin, J. (2005, August). Developing an intelligent data discriminating system of anti-money laundering based on SVM. In 2005 International conference on machine learning and cybernetics (Vol. 6, pp. 3453-3457). IEEE. 44.Thorndike, R. L. (1953). Who belongs in the family?. Psychometrika, 18(4), 267-276. 45.Umadevi, P., and Divya, E. (2012, December). Money laundering detection using TFA system. In International Conference on Software Engineering and Mobile Application Modelling and Development (ICSEMA 2012) (pp. 1-8). IET. 46.United States Department of the Treasury (2015). National Money Laundering Risk Assessment. 47.Wang, S. N., and Yang, J. G. (2007, August). A money laundering risk evaluation method based on decision tree. In 2007 international conference on machine learning and cybernetics (Vol. 1, pp. 283-286). IEEE. 48.Watkins, R. C., Reynolds, K. M., Demara, R., Georgiopoulos, M., Gonzalez, A., and Eaglin, R. (2003). Tracking dirty proceeds: exploring data mining technologies as tools to investigate money laundering. Police Practice and Research, 4(2), 163-178. 49.Zhang, Y., and Trubey, P. (2019). Machine learning and sampling scheme: An empirical study of money laundering detection. Computational Economics, 54(3), 1043-1063. 50.Zhang, Z., Salerno, J. J., and Yu, P. S. (2003, August). Applying data mining in investigating money laundering crimes. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 747-752). |