JFRM  Vol.2 No.1 , March 2013
Assessing Money Laundering Risk of Financial Institutions with AHP: Supervisory Perspective
Author(s) Ke Jia, Xi Zhao, Ling Zhang*
ABSTRACT

This paper proposed a risk assessment model with which supervisory authorities can calculate the money laundering risk (MLR) level of financial institutions and make comparisons among multiple institutions. The model is based on the Analytic Hierarchy Process (AHP) and decomposes MLR into two second-tier criteria, i.e. Inherent Risk & Control Risk. AHP pair wise comparisons made by the experts from various fields are processed through AHP software to get the weight of each factor. Using this model, MLR of each financial institution could be obtained and certain comparison among them could be carried out.


Cite this paper
Jia, K. , Zhao, X. & Zhang, L. (2013). Assessing Money Laundering Risk of Financial Institutions with AHP: Supervisory Perspective. Journal of Financial Risk Management, 2, 29-31. doi: 10.4236/jfrm.2013.21004.
References
[1]   Cai, Y. L., & Liu, Z. M. (2011). Establishment of Chinese anti money laundering supervision mode drawing on the experience of UK & US. Research on China’s anti-money laundering (pp. 374-376). Beijing: China Financial Publishing House Press.

[2]   Council of Europe (2010). Guidelines on bank risk analysis aimed at preventing money laundering and terrorism financing. http://www.coe.int/t/dghl/monitoring/moneyval/National_legislation/MNE_RBAguidelines.pdf

[3]   Federal Financial Institutions Examination Council (2010). Bank secrecy act/anti-money laundering examination manual. http://www.ffiec.gov/bsa_aml_infobase/pages_manual/manual_online.htm

[4]   Ferwerda, J., Kattenberg, M., Chang, H.-H., Unger, B., Groot, L., & Bikker, J. A. (2013). Gravity models of trade-based money laundering. Applied Economics, 45, 3170-3182. doi:10.1080/00036846.2012.699190

[5]   IIROC (Investment Industry Regulatory Organization of Canada). (2010). Anti-Money Laundering Compliance Guidance. Toronto.

[6]   Kishor, N., & Lescuyer, G. (2012). Controlling illegal logging in domestic and international markets by harnessing multi-level governance opportunities. International Journal of the Commons, 6, 255-270.

[7]   Ma, J. (2009). Financial institutions anti-money laundering practical manual (pp. 268-276). Beijing: China Financial Publishing House Press.

[8]   Reserve Bank of New Zealand (2011). Sector risk assessment for registered banks, non-bank deposit takers, and life insurers. http://www.rbnz.govt.nz/aml/4345201.pdf

[9]   Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48, 9-26. doi:10.1016/0377-2217(90)90057-I

[10]   Stokes, T. (2012). Virtual money laundering: The case of Bitcoin and the Linden dollar. Information and Communications Technology Law, 21, 221-236. doi:10.1080/13600834.2012.744225

[11]   Wang, S. N., & Yang, J. G. (2007). A money laundering risk evaluation method based on decision tree. The 6th International Conference on Machine Learning and Cybernetics, 1, 283-286.

 
 
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