基于特征工程的信用卡欺诈检测策略研究

Research of credit card fraud detection strategy based on feature engineering

针对传统的欺诈检测工具难以检测信用卡欺诈模式的问题,提出一种基于特征工程的信用卡欺诈检测策略。首先,该策略扩展了交易聚合策略,在对交易进行分组时通过纳入一个结合标准,将持卡人或交易类型与国家或商户群体等信息相结合;然后,对交易的周期性行为进行分析,使用冯米塞斯分布(循环正态分布)创建一组新的特征集合;最后,使用一个大型真实的信用卡欺诈数据集,比较已有的信用卡欺诈检测模型,并评估不同特征集合对结果造成的影响。结果表明该策略较已有模型的成本大概节约13%。

To solve the problem that traditional fraud detection tools are difficult to detect credit card fraud patterns,a credit card fraud detection strategy based on feature engineering is proposed,in which the transaction aggregation strategy is extended and a combining criterion is incorporated into the grouping of transactions to combine the information of the cardholder or transaction type with the information of the state or merchant group,then,the cyclical behavior of transactions is analyzed and a new set of characteristics is created by mean of Von Mises distribution(cyclic normal distribution)next,a large real credit card fraud data set is used to compare the existing credit card fraud detection models and evaluate the impact of different feature sets on the results.The result shows that the cost produced by the proposed strategy is decreased byabout 13%.