Credit Card Fraud Detection Using Weighted Support Vector Machine

• 作者: Basu Bhandari Dennis Black Dongfang Zhang
• 来源期刊: 应用数学(英文)
• 学科: 其他
• 关键词: Support Vector Machine Binary Classification Imbalanced Data UNDERSAMPLING Credit Card Fraud
• 年，卷（期）: 2020,（12）
• 页码范围: 1275-1291
• 页数: 17页
• 分类号: Z87

摘要

Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.