以機器學習技術建立電子商務購物網站信用卡偽冒交易之風險預測 - 以台灣B2C電商購物網站為例

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胡玉健(Yu-Chien Hu) 查詢紙本館藏

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資訊管理學系

論文名稱

以機器學習技術建立電子商務購物網站信用卡偽冒交易之風險預測 - 以台灣B2C電商購物網站為例
(Machine Learning Techniques for Credit Card Fraud Detection in E-Commerce Platforms: A Case Study on a Taiwan B2C Shopping Platform)

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摘要(中)

隨著電子商務的蓬勃發展，信用卡支付已成為線上交易中不可或缺的重要環節。然而，相伴而生的信用卡欺詐行為亦日趨猖獗；對消費者權益、商家營收以及金融體系造成了嚴重衝擊。由於欺詐手法層出不窮、變化多端，傳統基於規則的風險驗證機制已很難全面識別並防範這些新型欺詐威脅。有鑑於此，構建高效精準的信用卡欺詐檢測模型以維護電商交易安全迫在眉睫。
本研究以台灣B2C電子商務購物平台網站為研究個案，目的是透過機器學習技術完善既有的RBRA(Rule-Based Risk Authentication)信用卡偽冒防欺詐框架。本研究收集並預處理了個案研究B2C電子商務購物平台網站2022年的信用卡交易記錄。並分別基於決策樹、隨機森林、邏輯回歸等多種機器學習算法訓練分類模型，以預測每一筆交易的潛在欺詐風險程度。且鑑於本個案研究樣本中信用卡偽冒詐欺類別資料不平衡的特性，本研究採用了SMOTE技術進行資料集增強，引入F1-Score、ROC曲線等指標作為模型評估的主要依據。並由混淆矩陣分析結果進一步印證實驗模型的分類精準度。此外，本研究也探討了信用卡偽冒欺詐交易時間熱點、高風險商品類別以及收件人地區等維度的特徵資料選取。
在理論與實踐相結合的基礎上，本研究提出將優化後的機器學習模型整合電子商務購物平台網站，實現每一筆信用卡交易的即時風險評估。對於高風險訂單，結帳流程將導向到發卡行執行3DS身份驗證，從而形成前後台協同的防欺詐架構，有效維護交易安全性、提升消費者使用體驗。總結來看，本研究為電子商務購物平台網站防範金融欺詐活動提供了技術路徑，對於維護電子商務健康發展和消費者權益具有重要意義。

關鍵字：#機器學習 #資料探勘 #信用卡詐欺偵測 #風險預測 #RBRA

摘要(英)

The surge in credit card fraud accompanying e-commerce growth poses severe threats, necessitating efficient fraud detection models to safeguard transaction security. This study employed a Taiwanese e-commerce platform to enhance the existing Rule-Based Risk Authentication (RBRA) framework using machine learning techniques.

We collected and preprocessed 2022 transaction data, including confirmed fraud cases, and trained classification models using algorithms like decision trees, random forests, and logistic regression. Addressing class imbalance with oversampling, experimental results demonstrated the random forest model′s superior performance, achieving an AUC of 0.976 and F1-score of 0.916, corroborated by exceptional classification accuracy. Feature analysis provided insights into fraud hotspots, high-risk product categories, and recipients′ geographic patterns, informing targeted prevention strategies. Integrating the optimized model enables real-time risk assessment, redirecting high-risk orders for 3D Secure verification while maintaining security and user experience. This study offers a technical pathway for e-commerce platforms to combat fraud, contributing to sustainable development and consumer protection. Leveraging machine learning optimizes fraud detection, fostering a secure online shopping environment.

Future work focuses on incorporating diverse data sources, exploring ensemble models, and enhancing maintainability to further improve real-time performance, accuracy, and robustness, cultivating an efficient and secure e-commerce ecosystem.
Keywords: #Machine Learning #Data Mining #Credit Card Fraud Detection #Risk Prediction #RBRA

關鍵字(中)

★ 機器學習
★ 資料探勘
★ 信用卡詐欺偵測
★ 風險預測

關鍵字(英)

★ Machine Learning
★ Data Mining
★ Credit Card Fraud Detection
★ Risk Prediction
★ RBRA

論文目次

誌謝 iv
摘要 v
Abstract vi
目錄 vii
圖目錄 x
表目錄 xii
第一章緒論 1
1. 研究背景與動機 1
1.1 背景 1
1.2 動機 8
1.3 研究範圍與目的 16
1.4 研究貢獻 19
第二章文獻探討 21
2.1 信用卡偽冒詐欺 21
2.2 本研究所使用的模型探討 30
第三章研究方法 33
3.1. 研究設計 33
3.2 研究架構 35
3.3 研究對象 36
3.3.1 個案公司 36
3.3.2 個案研究樣本 37
3.3.3 資料前處理步驟 41
3.4 實驗環境說明 42
3.4.1 硬體環境 42
3.4.2 軟體環境 42
3.5 研究個案資料集解析 43
3.5.1資料預處理： 43
3.5.2特徵工程： 43
3.5.3資料不平衡處理: 43
3.6 實驗評估方法 51
第四章研究結果與分析 52
4.1 實驗結果說明 52
4.2結果分析與討論 59
4.2.1 交易時間： 59
4.2.2 交易地理區域： 60
4.3.3 信用卡偽冒詐欺的商品類型： 61
第五章研究結論 62
5.1 研究貢獻 63
5.2 研究限制 64
5.3 未來研究方向 65
參考文獻 66
1. 中文文獻 66
2. 英文文獻 67

圖目錄
圖1：個案研究公司Rule-based 手動黑名單管理時序圖 6
圖2：個案研究公司 Rule-based 商品類管理UI截圖 7
圖3：個案研究公司2016至2022年信用卡詐欺率 12
圖4：個案研究公司2016至2022年信用卡詐欺呆帳金額 13
圖5：個案研究公司2016至2022年信用卡詐欺交易攔單 13
圖6：個案研究公司RBRA系統架構圖 20
圖7：本研究的流程規劃及設計 34
圖8：研究架構圖 35
圖9：詐欺與非詐欺資料不平衡 44
圖10：在Orange中引入Python Script實作SMOTE範例 45
圖11：Orange中引入Python Script進行SMOTE處理 46
圖12：資料前處理流程圖 47
圖13：個案研究公司RBRA整合購物網站流程 49
圖14：RBRA Assess API轉導交易3D授權驗證流程 50
圖15：ROC曲線圖 53
圖16：各分類器的混淆矩陣 58
圖17：信用卡偽冒詐欺時間交易分佈 59
圖18：信用卡偽冒詐欺收件人地理區域分佈 60
圖19：信用卡偽冒詐欺商品類型 61

表目錄
表1：2020 至 2022 年信用卡詐欺型態統計比例表 14
表2：信用卡詐欺類型備註說明: 14
表3：總結了近期信用卡異常檢測研究的限制 28
表4：總結了近期信用卡異常檢測研究的限制(續) 29
表5：來源資料Attribute說明 37
表6：去識別化的欄位 39
表7：個案研究資料集分類說明 40
表8：混淆矩陣 - Confusion Matrix 51
表9：No sampling, test on training data 54
表10：Stratified Shuffle split, 10 random samples with 80% data 55
表11：Stratified 10-fold Cross validation and SMOTE imbalanced 56

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指導教授

蔡志豐(Chih-Fong Tsai)

審核日期

2024-6-3

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