摘 要
台灣地區過去十年的信用卡累積發卡量呈現快速的成長，但是，由於其方便性及通用性的特性，卻逐漸成為犯罪集團詐財的主要工具之一。國內之信用卡詐欺犯罪案件急遽增加，在2000年時，盜刷金額已經超過了30億元，此一數值高居亞洲之冠。可見台灣信用卡盜刷的情形是相當嚴重的。
資料挖掘（data mining）是近年來資料庫應用領域中相常熱門的議題。資料挖掘是指在資料庫中，利用各種方法和技術，將過去累積的大量歷史資料，經過分析、歸納、整合的工作，以粹取出有用的資訊，提供管理階層作為訂定決策的依據。目前無論在零售業、百貨業、金融機構、電信業…等，都已經漸漸地體認到知識挖掘的重要性。
在此文章中我們使用分群技術來分析信用卡資料。分群技術（Clustering）是資料挖掘中相當著名的技術之一，本研究有三個特點，1、個人化分析，以個人過去的歷史資料來分析新的交易；2、正反兩面分析，同時使用正常資料及盜刷資料來分析新的交易；3、密度法分析，以密度值的方法來代表群體的層級。期望能夠提供更精準之偵測系統，降低信用卡業者及持卡人之損失。

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