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研究生(外文):Chiang Wang
論文名稱(外文):The Application of Data Mining Technique to Dummy Bank Accounts in Frauds
指導教授(外文):Shing-Han Li
外文關鍵詞:Fraudulent DetectionAutomatic Teller Machine(ATM)Data Mining
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There are various means to generate defraud today based on the development of multimedia and communication networks. The commonness of such frauds is to attract victims to go to financial institutes or use ATMs to transfer money into dummy accounts by various reasons to get ramps. It does not matter which names of frauds they use, they receive the money through financial institutions or ATMs, so it has became an important issue to find out the patterns of such fraudulent accounts and detect these fraudulent accounts to reduce victims’ losses. This research uses Naïve Bayes and Association Rules to mine patterns of fraudulent accounts from accounts data and transaction data of an actual bank to design the fraudulent accounts detecting system. These substantial tests verify that this system can find fraudulent accounts as soon as possible and provide reference materials to fraudulent accounts based on early detection for financial institutions.
摘要 III
目錄 IV
圖目錄 VI
表目錄 VIII
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 研究範圍與限制 5
1.5 章節組織 6
第2章 文獻探討 8
2.1 近年詐欺案件之探討 8
2.2 ATM詐騙之探討 12
2.3 金融機構警示帳戶聯防機制之探討 15
2.4 資料探勘之探討 18
2.5 資料探勘與詐欺偵測相關文獻 27
第3章 研究方法 30
3.1 研究方法選擇 30
3.2 群集演算法 31
3.3 貝氏分類法 32
3.4 關聯規則理論 33
3.5 研究分析流程 34
第4章 實驗及分析 42
4.1 實驗環境及工具 42
4.2 資料取得及預處理 43
4.3 資料探勘分析 46
4.3.1 以群集演算法進行資料探勘 47
4.3.2 以貝氏分類演算法進行資料探勘 53
4.3.3 以關聯規則演算法進行資料探勘 63
4.4 實驗結果驗證 66
4.4.1 敍述性統計方式驗證 67
4.4.2 實作詐騙偵測系統驗證 76
第5章 結論與建議 81
5.1 研究結論 81
5.2 研究貢獻 82
5.3 未來研究方向 82
參考文獻 84
附錄一:資料表欄位格式說明 92
附錄二:資料預處理及詐欺偵測程式演算法 96
附錄三:銀行對疑似不法或顯屬異常交易之存款帳戶管理辦法 109
附錄四:金融機構辦理警示帳戶聯防機制作業程序 117
1.Berry M.J.A., and Linoff G.S., “Data Mining Techniques: For Marketing, Sales, and Customer Support”, John Wiley & Sons, Inc. ,1997.
2.Bolton R.J., and Hand D.J., “Peer Group Analysis”, Technical Report, department of Mathematics, Imperial College, London, 2001.
3.Brachman R.J., Khabaza W., Kloesgen G., Piatetsky S. and Simoudis E., “Mining business database”, Communication of ACM, pp.42- 48, 1996.
4.Braha D. and Shmilovici A., “On the Use of Decision Tree Induction for Discovery of Interactions in a Photolithographic Process”, IEEE Transactions On Semiconductor Manufacturing, Vol. 16, No. 4, pp.644-652, 2003.
5.Brause R., Langsdorf T., and Hepp M., “Neural Data Mining for Credit Card Fraud Detection”, 11th IEEE International Conference on Tools with Artificail Intelligence, pp.103-106, 1999.
6.Chen J.S., Ching R.K., and Lin Y.S., “An extended study of the K-means algorithm for data clustering and its applications ” The Journal of the Operational Research Society, vol.55, No.9, pp.976-987, 2004.
7.Cheung Y.M., “K-means: A New Generalized K-means Clustering Algorithm” Pattern Recognition Letters, vol.24, issue 1, pp.2883-2893, 2003.
8.Clark G., David M., Daryl P., and Padhraic S.,“Statistical Inference and Data Mining”, Communication of The ACM, Vol. 39, No. 11, pp.35-41, 1996.
9.Elovici Y. and Braha D., “A decision-theoretic approach to data mining”, Systems, Man and Cybernetics, Part A, IEEE Transactions on , Vol. 33(1), pp. 42–51, 2003.
10.Fayyad U.M., “Data Mining and Knowledge Discovery in Database: Implication for Scientific Databases”, Scientific and Statistical Database Management, pp.2-11, 1997.
11.Fayyad U.M., Piatetsky-Shapiro G., and Smyth P., “ The KDD Process for Extracting Useful Knowledge from Volumes of Data ”, Communication of ACM,vol. 39, no.11, pp.27-34, 1996.
12.Fayyad U.M.,Shapi G.P., Smyth P. and Uthursamy R., “Advances in Knowledge Discovery and Data Mining”, AAAI Press/The MIT Press,Menlo Park, CA., 1996.
13.Feelders A., Daniels H. and Holsheimer M., “Methodological and practical aspects of data mining”, Information and Management, vol. 37(5), pp. 271-281, 2000.
14.Ghosh S., and Reilly D.L., “Credit Card Fraud Detection with a Neural-Network”, Proceedings of the 27th Annual Hawaii International Conference on System, pp.621-630, 1994.
15.Groth and Robert, “Data mining: a hands-on approach for business professionals”, Prentice Hall PTR, 1998.
16.Han J. and Kamber M., “Data Mining: Concepts and Techniques”, 2nd ed., Morgan Kaufmann, 2006.
17.Krzysztof J.C., Witold P., and Roman W.S.,“Data Mining Methods for Knowledge Discovery”, Kluwer Academic, United States of America, 1998.
18.Leonard K.J., “The Development of a Rule Based Expert System Model for Fraud Alert in Consumer Credit”, European Journal of Operational Research 80(2) pp.350-356, 1995.
19.Owrang M.M, and Grupe F.H., “Using domain knowledge to guide database knowledge discovery”, Expert Systems With Application 10, pp. 173- 180, 1996.
20.Pieter A., and Dolf Z., “ Data Mining”, Addison-Wesley, Harlow, 1996.
21.Pyle D., “Data Preparation for Data Mining”, Morgan Kaufmann Publishers, San Francisco, California, 1999.
22.Romano D.,“Data mining leading edge: insurance & banking”, in Proceedings of Knowledge Discovery and Data Mining, Unicom, Brunel University, 1997.
23.Rothman M., and Murphy E.,“Data mining :a practical approach for database marketing”, IBM White Paper,Dallas,Taxas, 1995.
24.Rubin S.H., “A Fuzzy Approach Towards Inferential Data Mining” Computers & Industrial Engineering, pp. 267-270, 1998.
25.Shaw M. J., and Subramaniam C., and Tan G. W. and Welge M. E.,“Knowledge management and data mining for marketing”, Decision Support Systems 31 ,pages 127-137 , 2001.
26.Smith M.H. and Pedrycz W., “Expanding the meaning of and applications for data mining”, Systems, Man, and Cybernetics, 2000 IEEE International Conference on, Vol.3, pp. 1874 , 2000.
27.Song H.S., Kim J.K. and Kim S.H., “Mining the change of customer behavior in an internet shopping mall”, Expert Systems with Applications 21, pages 157-168, 2001.
28.Sung H.H. and Sang C.P., “Application of data mining tools to hotel data mart on the Intranet for database marketing”, Expert Systems with Applications, Vol. 15(1), pp. 1-31, 1998.
29.Thuraisingham B., “A primer for understanding and applying data mining”, IT Professional, Vol. 2, pp. 28–31, 2000.
30.Tom M.and Mitchell, “MACHINE LEARNING”, The McGraw-Hill Companies, Inc., 1997.
31.Wheeler R., and Aitken S., “Multiple Algorithms for Fraud Detection”, Knowledge-Based Systems 13(2-3) , pp.93-99, 2000.
32.Zormana M., Masudab G., Kokola P., Yamamotob R. and Stiglica B., “Mining Diabetes Database With Decision Trees and Association Rules,” Computer-Based Medical Systems, Proceedings of the 15th IEEE Symposium , pp.134–139, 2002.
33.中華資料採礦協會發行,“資料採礦與商業智慧data mining & business intelligence with SQL server 2005”,鼎茂圖書出版社,2005年。
34.尹相志,“SQL Server 2005資料採礦聖經”,學貫行銷股份有限公司,2006年6月。
52.賴添貴,“台灣地區經濟詐欺狀況處理與防制之實證研究”,大葉大學事業經營研究所,碩士論文,第14頁至第 21頁,2005年。
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