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研究生： 蘇志祐
Su, Zhi-You
論文名稱： 機器學習在證券業反洗錢監控之應用
Applying Machine Learning on Anti-Money Laundering Detection in Securities Firms
指導教授： 何靜嫺
Ho, Shirley J.
口試委員: 許素珠
宋豪漳
學位類別： 碩士
Master
系所名稱： 社會科學學院 - 經濟學系
Department of Economics
論文出版年： 2022
畢業學年度： 110
語文別： 英文
論文頁數： 77
中文關鍵詞： 洗錢防制 、K-means分群演算法 、支援向量機 、異常檢測 、疑似洗錢交易態樣 、證券業
外文關鍵詞： Anti-money laundering, K-means, Support vector machine, Anomaly detection, Suspicious types, Securities industry
DOI URL： http://doi.org/10.6814/NCCU202200104
相關次數： 點閱：191  下載：12 
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    • 本文為研究機器學習在證券業反洗錢交易監控的實證分析。利用台灣一家證券公司提供的實際交易數據，我們研究並比較了傳統監控方法和基於兩種機器學習演算法的監控方法：K-means分群演算法和支援向量機。我們選擇了台灣金融監督委員會與台灣證券公會研議後發布之兩類疑似洗錢或資恐交易態樣來比較監控結果。我們的分析揭示了機器學習演算法在監測洗錢方面的潛在優勢，結果顯示，機器學習算法在監控率(DR)方面優於傳統的監控方法。本文對機器學習在證券業反洗錢交易監控中的應用提供了深入的研究。

    This paper studies the empirical analysis of machine learning for money laundering detection algorithms in the securities industry. Using actual transaction data provided by a securities firm in Taiwan, we study and compare the traditional detection method with detection methods based on two machine learning algorithms: K-means and support vector machine. We choose two types of suspicious types of transactions suggesting money laundering approved by the Financial Supervisory Commission in Taiwan to compare the detection results. Our analysis reveals the potential advantages of machine learning algorithms in monitoring money laundering, and the results show that machine learning algorithms outperform the traditional detection method in terms of detection rates. This paper provides insights into the application of machine learning in money laundering detection in the securities industry.

    List of Tables 2
    List of Figures 3

    1. Introduction 4

    2. Related Literature 10
    2.1 Traditional methods used in financial firms 10
    2.2 Current AML solutions by machine learning techniques 11

    3. Algorithms 13
    3.1 K-means Clustering 14
    3.2 Support Vector Machine 18

    4. Data Source and Variables 20
    4.1 Types of Suspicious Transactions 21
    4.2 Risk Factors 22

    5. Detection Results (Type No.3 Suspicious Transactions) 29
    5.1 Traditional Detection Method 29
    5.2 Machine Learning Methods (K-means、SVM) 31

    6. Detection Results (Type No. 6 Suspicious Transactions) 40
    6.1 Traditional Detection Method 40
    6.2 Machine Learning Methods (K-means、SVM) 41

    7. Concluding Remarks 49

    References 53

    Appendix A: Scatter plots for the K-means clustering for Type No. 3 suspicious customers 58
    Appendix B: Scatter plots for the SVM clustering for Type No. 3 suspicious customers 66
    Appendix C: Scatter plots for the K-means clustering for Type No. 6 suspicious customers 74
    Appendix D: Scatter plots for the SVM clustering for Type No. 6 suspicious customers 76

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