BEHAVIOR MONITORING METHODS FOR TRADE-BASED MONEY LAUNDERING INTEGRATING MACRO AND MICRO PRUDENTIAL REGULATION: A CASE FROM CHINA

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.

[1]  Nuno Vasconcelos,et al.  Cost-Sensitive Support Vector Machines , 2012, Neurocomputing.

[2]  Gao Zeng-an,et al.  Transfer Price-based Money Laundering in International Trade , 2006, 2006 International Conference on Management Science and Engineering.

[3]  Jianmin He,et al.  An evolving network model of credit risk contagion in the financial market , 2016 .

[4]  Irfan Ahmed,et al.  Forecasting investment and consumption behavior of economic agents through dynamic computable general equilibrium model , 2018, Financial Innovation.

[5]  Todd Mitton,et al.  Cronyism and capital controls: evidence from Malaysia☆ , 2003 .

[6]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[7]  Yi Peng,et al.  Nonlinear manifold learning for early warnings in financial markets , 2017, Eur. J. Oper. Res..

[8]  M. Soudijn.  A critical approach to trade-based money laundering , 2014 .

[9]  Jurgita Antucheviciene,et al.  Sustainability in Construction Engineering , 2018, Sustainability.

[10]  Xinwei Deng,et al.  Active Learning Through Sequential Design, With Applications to Detection of Money Laundering , 2009 .

[11]  M. Furry.  Trade-Based Money Laundering , 1956 .

[12]  Samuel McSkimming,et al.  Trade-based Money Laundering: Responding to an Emerging Threat , 2010 .

[13]  Julian di Giovanni.  What Drives Capital Flows? The Case of Cross-Border M&A Activity and Financial Deepening , 2002 .

[14]  Diversification, bank performance and risk: have Tunisian banks adopted the new business model? , 2017 .

[15]  Kamlesh D. Rohit,et al.  Review On Detection of Suspicious Transaction In Anti-Money Laundering Using Data Mining Framework , 2015 .

[16]  K. Somasundaram,et al.  Autoregressive-based outlier algorithm to detect money laundering activities , 2017 .

[17]  Reaching beyond Banks: How to Target Trade-Based Money Laundering and Terrorist Financing outside the Financial Sector , 2009 .

[18]  Gang Kou,et al.  A kernel entropy manifold learning approach for financial data analysis , 2014, Decis. Support Syst..

[19]  Brigitte Unger.  The Scale and Impacts of Money Laundering , 2007 .

[20]  C. Borio.  Implementing a Macroprudential Framework: Blending Boldness and Realism , 2011 .

[21]  Kari Larsen,et al.  RegTech is the New Black - The Growth of RegTech Demand and Investment , 2017 .

[22]  Joras Ferwerda,et al.  Gravity Models of Trade-Based Money Laundering , 2011 .

[23]  Yi Peng,et al.  Evaluation of clustering algorithms for financial risk analysis using MCDM methods , 2014, Inf. Sci..

[24]  M. Naheem.  Risk of money laundering in the US: HSBC case study , 2016 .

[25]  Yuan Song,et al.  Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR , 2018, Financial Innovation.

[26]  J. D. Hertog,et al.  Water always finds its way: Identifying new forms of money laundering , 2012 .

[27]  Luci Ellis,et al.  Systemic risk, macroprudential policy frameworks, monitoring financial systems and the evolution of capital adequacy. , 2012 .

[28]  Yi Peng,et al.  Jie Ke versus AlphaGo: A ranking approach using decision making method for large-scale data with incomplete information , 2018, Eur. J. Oper. Res..

[29]  Muhammad Kashif,et al.  Loan growth and bank solvency: evidence from the Pakistani banking sector , 2016, Financial Innovation.

[30]  Jurgita Antucheviciene,et al.  Internet of things and its challenges in supply chain management; a rough strength-relation analysis method , 2018, E+M Ekonomie a Management.

[31]  C. Borio.  Towards a Macroprudential Framework for Financial Supervision and Regulation? , 2003 .

[32]  Yi Peng,et al.  Evaluation of Classification Algorithms Using MCDM and Rank Correlation , 2012, Int. J. Inf. Technol. Decis. Mak..

[33]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[34]  Enhancing Prudential Standards in Financial Regulations , 2014 .

[35]  M. Aglietta,et al.  A systemic approach to financial regulation A European perspective , 2010 .

[36]  Simplice A. Asongu,et al.  Determinants of foreign direct investment in fast-growing economies: evidence from the BRICS and MINT countries , 2018, Financial Innovation.

[37]  S. Kannan and K. Somasundaram,et al.  A Review of Outlier Prediction Techniques in Data Mining , 2015 .

[38]  Bala Shanmugam,et al.  International trade‐based money laundering: the Malaysian perspective , 2007 .

[39]  Sunil Vadera,et al.  A survey of cost-sensitive decision tree induction algorithms , 2013, CSUR.

[40]  Kevin P. Gallagher,et al.  Regaining Control? Capital Controls and the Global Financial Crisis , 2011 .

[41]  A. Milne.  Macroprudential policy: what can it achieve? , 2009 .

[42]  John S. Zdanowicz.  Trade-Based Money Laundering and Terrorist Financing , 2009 .

[43]  Vahidreza Yousefi,et al.  The Impact Made on Project Portfolio Optimisation by the Selection of Various Risk Measures , 2018 .

[44]  M. Naheem.  Trade based money laundering: A primer for banking staff , 2017 .

[45]  Yao-wen Xue,et al.  Research on money laundering risk assessment of customers – based on the empirical research of China , 2016 .

[46]  K. N. A. Arthur.  Financial innovation and its governance: Cases of two major innovations in the financial sector , 2017 .

[47]  Yi Peng,et al.  A cost-sensitive multi-criteria quadratic programming model for imbalanced data , 2018, J. Oper. Res. Soc..

[48]  Todd Mitton,et al.  Cronyism and Capital Controls: Evidence from Malaysia , 2001 .

[49]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.