應用資料探勘技術建立商業預測模式─以信用卡為例

Credit Card Fraud Detection Using Data Mining Approaches

陳來成(Chen Lai-Chen)

指導教授 ： 邱昭彰

元智大學/資訊學院/資訊管理學系/碩士(2002年)

摘要

信用卡業務隨著發卡量及網際網路的擴展，信用卡交易數量持續成長，對銀行系統所佔比重愈來愈大而且詐欺交易的比率持續擴大，降低詐欺風險的損失，成為銀行系統一個顯而易見的工作。本研究以台灣本地銀行之信用卡中心的信用卡詐欺偵測為例，由信用卡中心之資料倉儲中取得原始資料，使用資料探勘技術建立信用卡詐欺偵測之預測模式，本研究利用資料探勘技術中，人工智慧演算法之案例庫推理、決策樹與類神經網路演算法分別來學習信用卡持卡人之詐欺特徵，輔助詐欺偵測之預警。
信用卡詐欺偵測系統大部分採用國外所建立的預測模式，主要乃涵蓋持卡人消費交易紀錄來學習並找出詐欺的樣式(Pattern)。本論文嘗試建構資料探勘技術以及整合持卡人相關的所有資料為學習架構之詐欺預測模式，以作為本地銀行之信用卡中心建立詐欺預測模式之應用與發展參考。

關鍵字

詐欺偵測 ； 資料探勘 ； 案例庫推理 ； 決策樹理論 ； 類神經網路

並列摘要

Credit card transactions and electronic commerce continue to grow in great number. The higher rate of fraudulent account numbers also growing fast in credit card industries that subsequent losses by banks. Improved fraud detection thus has become essential to maintain the viability of commercial banks and the countries payment system.
The prevention of credit card fraud is an application for prediction techniques. This paper shows how data mining techniques and artificial intelligence algorithms can be successfully to obtain a high fraud detection rate. We also describe an AI-based approach that construct and compare predict models separately by case-based reasoning, decision tree and neural network methods for detecting fraud pattern. To ensure proper model construction that concept had to be developed and tested on real credit card data of local bank. The prediction of user behavior and operation transaction can be integrated and implemented on the fraud detection models.

並列關鍵字

fraud detection ； data mining ； case-based reasoning ； decision tree ； neural network

參考文獻

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