Behavior monitoring methods for trade-based money laundering integrating macro and micro prudential regulation: a case from China
Abstract
Trade-based Money Laundering, a new form of money laundering using international trade as a signboard, always appears along with speculative capital movement which has been accepted as the most concerned and consensus incentive giving rise to the collapse of the financial market. Unfortunately, preventing money laundering is very difficult since money laundering always has a plausible trade characterization. To reach this goal, supervision for regulator and financial institutions aims to effectively monitor micro entities’ behavior in financial markets. The main purpose of this paper is to establish a monitoring method including accurate recognition and classified supervision for Trade-based Money Laundering by means of knowledge-driven multi-class classification algorithms associated with macro and micro prudential regulation, such that the model can forecast the predicted class from the concerned management areas. Based on empirical data from China, we demonstrate the application and explain how the monitor method can help to improve management efficiency in the financial market.
First published online 8 May 2019
Keyword : financial risk monitor, trade-based money laundering, macro- and micro- prudential regulation, machine learning
How to Cite
Chao, X., Kou, G., Peng, Y., & Alsaadi, F. E. (2019). Behavior monitoring methods for trade-based money laundering integrating macro and micro prudential regulation: a case from China. Technological and Economic Development of Economy, 25(6), 1081-1096. https://doi.org/10.3846/tede.2019.9383
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chao, X. R., & Peng, Y. (2017). A cost-sensitive multi-criteria quadratic programming model for imbalanced data. Journal of the Operational Research Society, 69(4), 500-516. https://doi.org/10.1057/s41274-017-0233-4
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Delston, R. S., & Walls, S. C. (2009). Reaching beyond banks: how to target trade-based money laundering and terrorist financing outside the financial sector. Case Western Reserve Journal of International Law, 41, 85-97.
Deng, X., Joseph, V. R., Sudjianto, A., & Wu, C. J. (2009). Active learning through sequential design, with applications to detection of money laundering. Journal of the American Statistical Association, 104(487), 969-981. https://doi.org/10.1198/jasa.2009.ap07625
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Giovanni, J. D. (2005). What drives capital flows? The case of cross-border M&A activity and financial deepening. Journal of International Economics, 65(1), 127-149. https://doi.org/10.1016/j.jinteco.2003.11.007
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Huang, Y., & Kou, G. (2014). A kernel entropy manifold learning approach for financial data analysis. Decision Support Systems, 64, 31-42. https://doi.org/10.1016/j.dss.2014.04.004
Huang, Y., Kou, G., & Peng, Y. (2017). Nonlinear manifold learning for early warnings in financial markets. European Journal of Operational Research, 258(2), 692-702. https://doi.org/10.1016/j.ejor.2016.08.058
Johnson, S., & Mitton, T. (2001). Cronyism and capital control: evidence from Malaysia. Journal of Financial Economics, 67(2), 351-382. https://doi.org/10.1016/S0304-405X(02)00255-6
Kannan, S., & Somasundaram, K. (2015). A review of outlier prediction techniques in data mining. Research Journal of Applied Sciences Engineering & Technology, 10(9), 1021-1028. https://doi.org/10.19026/rjaset.10.1869
Kannan, S., & Somasundaram, K. (2017). Autoregressive-based outlier algorithm to detect money laundering activities. Journal of Money Laundering Control, 20(2), 190-202. https://doi.org/10.1108/JMLC-07-2016-0031
Kashif, M., Iftikhar, S. F., & Iftikhar, K. (2016). Loan growth and bank solvency: evidence from the Pakistani banking sector. Financial Innovation, 2(1), 22. https://doi.org/10.1186/s40854-016-0043-8
Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. https://doi.org/10.1016/j.ins.2014.02.137
Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology & Decision Making, 11(01), 197-225. https://doi.org/10.1142/S0219622012500095
Larsen, K., & Gilani, S. (2017). Regtech is the new black – the growth of RegTech demand and investment. Journal of Financial Transformation, 45, 22-29.
Liao, J., & Acharya, A. (2011). Transshipment and trade‐based money laundering. Journal of Money Laundering Control, 14(1), 79-92. https://doi.org/10.1108/13685201111098897
Liu, X. Y., & Zhou, Z. H. (2006). Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 18, 66-37.
Lomax, S., & Vadera, S. (2013). A survey of cost-sensitive decision tree induction algorithms. ACM Computing Surveys, 45, 1-35. https://doi.org/10.1145/2431211.2431215
Masnadi-Shirazi, H., Vasconcelos, N., & Iranmehr, A. (2015). Cost-sensitive Support Vector Machines. Journal of Machine Learning Research (arXiv:1212.0975V2).
Mcskimming, S. (2010). Trade-based money laundering: responding to an emerging threat. Deakin Law Review, 15(1), 37-63. https://doi.org/10.21153/dlr2010vol15no1art116
Milne, A. (2009). Macroprudential policy: what can it achieve? Oxford Review of Economic Policy, 25(4), 608-629. https://doi.org/10.1093/oxrep/grp036
Melvin, R. J. S. (2014). A critical approach to trade-based money laundering. Journal of Money Laundering Control, 17(2), 230-242. https://doi.org/10.1108/JMLC-01-2013-0001
Naheem, M. A. (2016a). Trade based money laundering: a primer for banking staff. International Journal of Disclosure & Governance, 14(2), 95-117. https://doi.org/10.1057/jdg.2015.21
Naheem, M. A. (2016b). Risk of money laundering in the US: HSBC case study. Journal of Money Laundering Control, 19(3), 225-237. https://doi.org/10.1108/JMLC-01-2015-0003
Nitsch, V. (2010). The dynamics of illicit flows from developing countries. Paper presented at the World Bank conference, 14-15 September. Washington.
Pishdar, M., Ghasemzadeh, F., Antucheviciene, J., & Saparauskas, J. (2018). Internet of things and its challenges in supply chain management; a rough strength-relation analysis method. E & M Ekonomie a Management,, 21(2), 208-222. https://doi.org/10.15240/tul/001/2018-2-014
Rohit, K. D., & Patel, D. B. (2015). Review on detection of suspicious transaction in anti-money laundering using data mining framework. International Journal for Innovative Research in Science and Technology, 1, 129-133.
Song, Y., Wang, H., & Zhu, M. (2018). Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR. Financial Innovation, 4(1), 2. https://doi.org/10.1186/s40854-018-0086-0
Thanasegaran, H., & Shanmugam, B. (2007). International trade‐based money laundering: the malaysian perspective. Journal of Money Laundering Control, 10(4), 429-437. https://doi.org/10.1108/13685200710830916
Unger, B. (2007). The scale and impacts of money laundering. Cheltenham, UK: Edward Elgar. https://doi.org/10.4337/9781781007624
Unger, B., & den Hertog, J. (2012). Water always finds its way: Identifying new forms of money laundering. Crime Law & Society Change, 57(3), 287-304. https://doi.org/10.1007/s10611-011-9352-z
Walker, J. (1995). Estimates of the extent of money laundering in and throughout Australia. The Australian Financial Intelligence Unit AUSTRAC.
Wymeersch, E. (2010). Global and regional financial regulation: the viewpoint of a European securities regulator. Global Policy, 1(2), 201-208. https://doi.org/10.1111/j.1758-5899.2010.00031.x
Xue, Y. W., & Zhang, Y. H. (2016). Research on money laundering risk assessment of customers – based on the empirical research of China. Journal of Money Laundering Control, 19(3), 249-263. https://doi.org/10.1108/JMLC-01-2015-0004
Yousefi, V., Haji Yakhchali, S., Šaparauskas, J., & Kiani, S. (2018). The impact made on project portfolio optimisation by the selection of various risk measures. Inzinerine Ekonomika-Engineering Economics, 29(2), 168-175. https://doi.org/10.5755/j01.ee.29.2.17405
Zavadskas, E., Šaparauskas, J., & Antucheviciene, J. (2018). Sustainability in construction engineering. Sustainability, 10(7), 2236. https://doi.org/10.3390/su10072236
Zdanowicz, J. S. (2009). Trade-based money laundering and terrorist financing. Review of Law & Economics, 5(2), 855-878. https://doi.org/10.2202/1555-5879.1419
Ahmed, I., Socci, C., Severini, F., Yasser, Q. R., & Pretaroli, R. (2018). Forecasting investment and consumption behavior of economic agents through dynamic computable general equilibrium model. Financial Innovation, 4(1), 7. https://doi.org/10.1186/s40854-018-0091-3
Allen, F., Goldstein I., Jagtiani J., & Lang, W. W. (2016). Enhancing prudential standards in financial regulations. Journal of Financial Services Research, 49(2-3), 133-149. https://doi.org/10.1007/s10693-016-0253-2
Arnold, B., Borio, C., Ellis, L., & Moshirian, F. (2012). Systemic risk, macroprudential policy frameworks, monitoring financial systems and the evolution of capital adequacy. Journal of Banking & Finance, 36, 3125-3132. https://doi.org/10.1016/j.jbankfin.2012.07.023
Arthur, K. N. A. (2017). Financial innovation and its governance: Cases of two major innovations in the financial sector. Financial Innovation, 3(1), 10. https://doi.org/10.1186/s40854-017-0060-2
Asongu, S., Akpan, U. S., & Isihak, S. R. (2018). Determinants of foreign direct investment in fastgrowing economies: evidence from the BRICS and MINT countries. Financial Innovation, 4(1), 26. https://doi.org/10.1186/s40854-018-0114-0
Borio, C. (2003, February). Towards a macroprudential framework for financial supervision and regulation? (BIS Working Papers No. 128). Retrieved from https://www.bis.org/publ/work128.pdf
Borio, C. (2011). Implementing a macroprudential framework: blending boldness and realism. Capitalism and Society, 6(1), 1-21. https://doi.org/10.2202/1932-0213.1083
Cassara, J. A. (2011). Trade-based money Laundering. China Customs, 5, 226-230.
Chao, X. R., & Peng, Y. (2017). A cost-sensitive multi-criteria quadratic programming model for imbalanced data. Journal of the Operational Research Society, 69(4), 500-516. https://doi.org/10.1057/s41274-017-0233-4
Chao, X., Kou, G., Li, T., & Peng, Y. (2018). Jie Ke versus AlphaGo: A ranking approach using decision making method for large-scale data with incomplete information. European Journal of Operational Research, 265(1), 239-247. https://doi.org/10.1016/j.ejor.2017.07.030
Chen, T., He, J., & Li, X. (2017). An evolving network model of credit risk contagion in the financial market. Technological and Economic Development of Economy, 23(1), 22-37. https://doi.org/10.3846/20294913.2015.1095808
Cooper, G. F., & Herskovits, E. (1992). A bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309-347. https://doi.org/10.1007/BF00994110
McIntosh, D. (2016). The costs of anti-money laundering enforcements to noncompliant banks. Journal of Finance and Bank Management, 4(1), 1-14.
Delston, R. S., & Walls, S. C. (2009). Reaching beyond banks: how to target trade-based money laundering and terrorist financing outside the financial sector. Case Western Reserve Journal of International Law, 41, 85-97.
Deng, X., Joseph, V. R., Sudjianto, A., & Wu, C. J. (2009). Active learning through sequential design, with applications to detection of money laundering. Journal of the American Statistical Association, 104(487), 969-981. https://doi.org/10.1198/jasa.2009.ap07625
FATF. (2006). Trade based money laundering, Financial Action Task Force (FATF). Retrieved from http://www.fatf-gafi.org/media/fatf/documents/reports/TradeBasedMoneyLaundering.pdf
Ferwerda, J., Kattenberg, M., Chang, H. H., Unger, B., Groot, L., & Bikker, J. A. (2013). Gravity models of trade-based money laundering. Applied Economics, 11(22), 3170-3182. https://doi.org/10.1080/00036846.2012.699190
Gallagher, K. P. (2012). Regaining control? Capital controls and the global financial crisis. In The consequences of the global financial crisis: the rhetoric of reform and regulation. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199641987.003.0007
Gao, Z. A., & Weng, L. F. (2006). Transfer price-based money laundering in international trade. International Conference on Management Science and Engineering, 1128-1132.
Giovanni, J. D. (2005). What drives capital flows? The case of cross-border M&A activity and financial deepening. Journal of International Economics, 65(1), 127-149. https://doi.org/10.1016/j.jinteco.2003.11.007
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. (2009). The WEKA data mining software: an update. SIGKDD Explorations, 11, 10-18. https://doi.org/10.1145/1656274.1656278
Hamdi, H., Hakimi, A., & Zaghdoudi, K. (2017). Diversification, bank performance and risk: have Tunisian banks adopted the new business model? Financial Innovation, 3(1), 22. https://doi.org/10.1186/s40854-017-0069-6
Huang, Y., & Kou, G. (2014). A kernel entropy manifold learning approach for financial data analysis. Decision Support Systems, 64, 31-42. https://doi.org/10.1016/j.dss.2014.04.004
Huang, Y., Kou, G., & Peng, Y. (2017). Nonlinear manifold learning for early warnings in financial markets. European Journal of Operational Research, 258(2), 692-702. https://doi.org/10.1016/j.ejor.2016.08.058
Johnson, S., & Mitton, T. (2001). Cronyism and capital control: evidence from Malaysia. Journal of Financial Economics, 67(2), 351-382. https://doi.org/10.1016/S0304-405X(02)00255-6
Kannan, S., & Somasundaram, K. (2015). A review of outlier prediction techniques in data mining. Research Journal of Applied Sciences Engineering & Technology, 10(9), 1021-1028. https://doi.org/10.19026/rjaset.10.1869
Kannan, S., & Somasundaram, K. (2017). Autoregressive-based outlier algorithm to detect money laundering activities. Journal of Money Laundering Control, 20(2), 190-202. https://doi.org/10.1108/JMLC-07-2016-0031
Kashif, M., Iftikhar, S. F., & Iftikhar, K. (2016). Loan growth and bank solvency: evidence from the Pakistani banking sector. Financial Innovation, 2(1), 22. https://doi.org/10.1186/s40854-016-0043-8
Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. https://doi.org/10.1016/j.ins.2014.02.137
Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology & Decision Making, 11(01), 197-225. https://doi.org/10.1142/S0219622012500095
Larsen, K., & Gilani, S. (2017). Regtech is the new black – the growth of RegTech demand and investment. Journal of Financial Transformation, 45, 22-29.
Liao, J., & Acharya, A. (2011). Transshipment and trade‐based money laundering. Journal of Money Laundering Control, 14(1), 79-92. https://doi.org/10.1108/13685201111098897
Liu, X. Y., & Zhou, Z. H. (2006). Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 18, 66-37.
Lomax, S., & Vadera, S. (2013). A survey of cost-sensitive decision tree induction algorithms. ACM Computing Surveys, 45, 1-35. https://doi.org/10.1145/2431211.2431215
Masnadi-Shirazi, H., Vasconcelos, N., & Iranmehr, A. (2015). Cost-sensitive Support Vector Machines. Journal of Machine Learning Research (arXiv:1212.0975V2).
Mcskimming, S. (2010). Trade-based money laundering: responding to an emerging threat. Deakin Law Review, 15(1), 37-63. https://doi.org/10.21153/dlr2010vol15no1art116
Milne, A. (2009). Macroprudential policy: what can it achieve? Oxford Review of Economic Policy, 25(4), 608-629. https://doi.org/10.1093/oxrep/grp036
Melvin, R. J. S. (2014). A critical approach to trade-based money laundering. Journal of Money Laundering Control, 17(2), 230-242. https://doi.org/10.1108/JMLC-01-2013-0001
Naheem, M. A. (2016a). Trade based money laundering: a primer for banking staff. International Journal of Disclosure & Governance, 14(2), 95-117. https://doi.org/10.1057/jdg.2015.21
Naheem, M. A. (2016b). Risk of money laundering in the US: HSBC case study. Journal of Money Laundering Control, 19(3), 225-237. https://doi.org/10.1108/JMLC-01-2015-0003
Nitsch, V. (2010). The dynamics of illicit flows from developing countries. Paper presented at the World Bank conference, 14-15 September. Washington.
Pishdar, M., Ghasemzadeh, F., Antucheviciene, J., & Saparauskas, J. (2018). Internet of things and its challenges in supply chain management; a rough strength-relation analysis method. E & M Ekonomie a Management,, 21(2), 208-222. https://doi.org/10.15240/tul/001/2018-2-014
Rohit, K. D., & Patel, D. B. (2015). Review on detection of suspicious transaction in anti-money laundering using data mining framework. International Journal for Innovative Research in Science and Technology, 1, 129-133.
Song, Y., Wang, H., & Zhu, M. (2018). Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR. Financial Innovation, 4(1), 2. https://doi.org/10.1186/s40854-018-0086-0
Thanasegaran, H., & Shanmugam, B. (2007). International trade‐based money laundering: the malaysian perspective. Journal of Money Laundering Control, 10(4), 429-437. https://doi.org/10.1108/13685200710830916
Unger, B. (2007). The scale and impacts of money laundering. Cheltenham, UK: Edward Elgar. https://doi.org/10.4337/9781781007624
Unger, B., & den Hertog, J. (2012). Water always finds its way: Identifying new forms of money laundering. Crime Law & Society Change, 57(3), 287-304. https://doi.org/10.1007/s10611-011-9352-z
Walker, J. (1995). Estimates of the extent of money laundering in and throughout Australia. The Australian Financial Intelligence Unit AUSTRAC.
Wymeersch, E. (2010). Global and regional financial regulation: the viewpoint of a European securities regulator. Global Policy, 1(2), 201-208. https://doi.org/10.1111/j.1758-5899.2010.00031.x
Xue, Y. W., & Zhang, Y. H. (2016). Research on money laundering risk assessment of customers – based on the empirical research of China. Journal of Money Laundering Control, 19(3), 249-263. https://doi.org/10.1108/JMLC-01-2015-0004
Yousefi, V., Haji Yakhchali, S., Šaparauskas, J., & Kiani, S. (2018). The impact made on project portfolio optimisation by the selection of various risk measures. Inzinerine Ekonomika-Engineering Economics, 29(2), 168-175. https://doi.org/10.5755/j01.ee.29.2.17405
Zavadskas, E., Šaparauskas, J., & Antucheviciene, J. (2018). Sustainability in construction engineering. Sustainability, 10(7), 2236. https://doi.org/10.3390/su10072236
Zdanowicz, J. S. (2009). Trade-based money laundering and terrorist financing. Review of Law & Economics, 5(2), 855-878. https://doi.org/10.2202/1555-5879.1419