Feature engineering strategies for credit card fraud detection
Feature engineering strategies for credit card fraud detection
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DOI：
10.1016/j.eswa.2015.12.030
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发表时间：
2016-06-01
影响因子：
8.5
通讯作者：
Ottersten, Bjoern
中科院分区：
文献类型：
--
作者：
Bahnsen, Alejandro Correa;Aouada, Djamila;Ottersten, Bjoern
全球每年因信用卡诈骗损失数十亿欧元。因此，迫使金融机构不断完善其欺诈检测系统。近年来，一些研究提出使用机器学习和数据挖掘技术来解决这一问题。然而，大多数研究使用某种类型的错误分类措施来评估不同的解决方案，并且没有考虑到与欺诈检测过程相关的实际财务成本。此外，在构建信用卡欺诈检测模型时，如何从交易数据中提取正确的特征是非常重要的。这通常通过聚合交易来完成，以便观察客户的消费行为模式。本文对事务聚合策略进行了扩展，提出了一种基于冯·米塞斯分布分析事务时间周期性行为的新特征集。然后，使用欧洲一家大型信用卡处理公司提供的真实信用卡欺诈数据集，比较了最新的信用卡欺诈检测模型，并评估了不同的特征集对结果的影响。通过将所提出的周期特征加入到这些方法中，结果显示平均节省了13%。(C)2016爱思唯尔有限公司。保留所有权利。
Every year billions of Euros are lost worldwide due to credit card fraud. Thus, forcing financial institutions to continuously improve their fraud detection systems. In recent years, several studies have proposed the use of machine learning and data mining techniques to address this problem. However, most studies used some sort of misclassification measure to evaluate the different solutions, and do not take into account the actual financial costs associated with the fraud detection process. Moreover, when constructing a credit card fraud detection model, it is very important how to extract the right features from the transactional data. This is usually done by aggregating the transactions in order to observe the spending behavioral patterns of the customers. In this paper we expand the transaction aggregation strategy, and propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution. Then, using a real credit card fraud dataset provided by a large European card processing company, we compare state-of-the-art credit card fraud detection models, and evaluate how the different sets of features have an impact on the results. By including the proposed periodic features into the methods, the results show an average increase in savings of 13%. (C) 2016 Elsevier Ltd. All rights reserved.