Scoring models to detect foreign exchange money laundering

외국환 거래의 자금세탁 혐의도 점수모형 개발에 관한 연구

  • Hong, Seong-Ik (Dept. of Computer Science & Industrial System Engineering, Yonsei University) ;
  • Moon, Tae-Hee (Dept. of Computer Science & Industrial System Engineering, Yonsei University) ;
  • Sohn, So-Young (Dept. of Computer Science & Industrial System Engineering, Yonsei University)
  • 홍성익 (연세대학교 정보산업공학과) ;
  • 문태희 (연세대학교 정보산업공학과) ;
  • 손소영 (연세대학교 정보산업공학과)
  • Received : 2005.02.14
  • Accepted : 2005.05.24
  • Published : 2005.09.30

Abstract

In recent years, the money Laundering crimes are increasing by means of foreign exchange transactions. Our study proposes four scoring models to provide early warning of the laundering in foreign exchange transactions for both inward and outward remittances: logistic regression model, decision tree, neural network, and ensemble model which combines the three models. In terms of accuracy of test data, decision tree model is selected for the inward remittance and an ensemble model for the outward remittance. From our study results, the accumulated number of transaction turns out to be the most important predictor variable. The proposed scoring models deal with the transaction level and is expected to help the bank teller to detect the laundering related transactions in the early stage.

Keywords

References

  1. Berry, M.J .. & Linoff, G. (1997), Data Mining Techniques. John Wiley and Sons, New York, USA
  2. Buchanan, B. (2004), Money laundering global obstacle. Research in International Business and Finance, 18, 115-127 https://doi.org/10.1016/j.ribaf.2004.02.001
  3. Chen, M.e., & Huang, S.H. (2003), Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert System with Applications, 24, 433-441 https://doi.org/10.1016/S0957-4174(02)00191-4
  4. Desai,V., Crook,J., & Overstreet, G. (1996), A comparison of neural networks and linear scoring models in credit union environment. European Journal of Operations Management, 95, 24-37 https://doi.org/10.1016/0377-2217(95)00246-4
  5. Jo, D.H.(1993), A study of Management of Foreign Money in Korea, Hankuk Univ. of Foreign Studies., M.S. thesis, Korea
  6. Jung, S'y'(2002), A study of Visual Data Mining Based on link Analysis for insurance Fraud detection, Soogsil Univ., M.S. thesis, Korea
  7. Kang,H.e., Han, S.T. Choi, J.H., Kim, £.S., & Kim, M.K.(2002), Methodology and Application of Data Mining, Freedom Academy, Seoul, Korea
  8. Kim, J.S.(2001), Credit Scoring Model Using Bayesian Method, Korea Univ., M.S. thesis, Korea
  9. Kim, S.B.(200 1), A Study on the Characteristic of Credit Cards Customer for Building Credit Scoring System, Korea Univ., M.S. thesis, Korea
  10. Kim, Y.S. & Sohn, S. y. (2003), Managing loan customers using misclassification patterns of credit scoring model. Expert system with Applications 57(10),482-488
  11. Koker, I. (2002), Money Laundering Trends in South Africa. Journal of Money Laundering Control, 6( 1), 27 -41 https://doi.org/10.1108/13685200310809383
  12. Kuo-Ellen (2002), Fraud In Documentary Credit Transaction. Journal of Money Laundering Control, 5(3), 192-207 https://doi.org/10.1108/eb027304
  13. Lee, S.B.(2001), Logistic Analsys for Credit Scoring, Sookmyung Women's Univ., M.S. thesis, Korea
  14. Philippsohn, S. (2001), Money Laundering on the Internet. Computers & Security, 20, 485-490 https://doi.org/10.1016/S0167-4048(01)00606-X
  15. Rahman, F. & Sheikn, A. (2002), The Underground Banking Systems and their Impact on Control of Money Laundering. Journal of Money laundering Control, 6(1), 42-45 https://doi.org/10.1108/13685200310809392
  16. Rezaee, Z. (2003), Causes, consequences, and deterence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277-298 https://doi.org/10.1016/S1045-2354(03)00072-8
  17. Sohn, SY & Shin, H.W.$(1997)^{a}$, Data Mining for Road Traffic Accident Type Classification, Korean Society of Transportation. 6(4), pp.187-194
  18. Sohn, S.Y & Shin, H.W.$(1997)^{b}$, Comparison of Data Mining Classification Algorithms for Categorical Feature Variables, 12(4), 551- 556
  19. Westphal, e. (998), Data Mining Solutions. John Wiley and Sons, New York, USA