本研究採用包含 180 萬筆美國信用卡詐欺資料集，旨在深入探討消費詐欺行為。透過對客戶消費行為與個人資料這兩大類變數進行建模，我們試圖探究各項變數對詐欺消費之影響。本研究比較機器學習中樹模型與邏輯斯迴歸模型的表現，結果顯示在這類非線性問題中，隨機森林與 XGBoost展現出優異預測能力。同時，我們發現消費金額、店家種類以及消費日期為星期幾這三個變數對於預測詐欺行為具有重要影響，並成功建立出召回率較高的模型。

This study employs a dataset containing 1.8 million instances of credit card fraud in the United States to delve into fraudulent transaction behaviors. By modeling two major categories of variables—customer transaction behaviors and personal information—we aim to explore the influence of various factors on fraudulent transactions. Comparative analysis between tree-based models and logistic regression in machine learning reveals that in such non-linear scenarios, Random Forest and XGBoost demonstrate superior predictive performance. Additionally, we identified four significant variables—transaction amount, merchant type, and the day of the week of the transaction —as influential factors in predicting fraudulent behavior, resulting in the development of a model with higher recall rates.

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