论文标题
应用支持向量数据描述欺诈检测
Applying support vector data description for fraud detection
论文作者
论文摘要
欺诈检测是一个重要的话题，适用于银行和金融部门，保险，政府机构，执法等各种企业。当前几年中，欺诈尝试已经显着增加，塑造了欺诈检测是研究的重要话题。欺诈检测的主要挑战之一是获取欺诈样本，这是一项复杂而具有挑战性的任务。为了应对这一挑战，我们采用一类分类方法（例如SVDD），该方法不需要欺诈样本进行培训。另外，我们介绍我们的算法Redbscan，这是DBSCAN的扩展，以减少样品数量并选择保持数据形状的样品数量。通过实施提出的方法获得的结果表明，欺诈检测过程在性能和速度方面都得到了改善。
Fraud detection is an important topic that applies to various enterprises such as banking and financial sectors, insurance, government agencies, law enforcement, and more. Fraud attempts have been risen remarkably in current years, shaping fraud detection an essential topic for research. One of the main challenges in fraud detection is acquiring fraud samples which is a complex and challenging task. In order to deal with this challenge, we apply one-class classification methods such as SVDD which does not need the fraud samples for training. Also, we present our algorithm REDBSCAN which is an extension of DBSCAN to reduce the number of samples and select those that keep the shape of data. The results obtained by the implementation of the proposed method indicated that the fraud detection process was improved in both performance and speed.
