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    題名: 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
    作者: 蘇志祐
    Su, Zhi-You
    貢獻者: 何靜嫺
    Ho, Shirley J.
    蘇志祐
    Su, Zhi-You
    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
    Anti-money laundering
    K-means
    Support vector machine
    Anomaly detection
    Suspicious types
    Securities industry
    日期: 2022
    上傳時間: 2022-02-10 13:10:36 (UTC+8)
    摘要: 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.
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    描述: 碩士
    國立政治大學
    經濟學系
    107258006
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0107258006
    資料類型: thesis
    DOI: 10.6814/NCCU202200104
    顯示於類別:[經濟學系] 學位論文

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