Dorronsoro J R, Ginel F, Sgnchez C, Cruz C S
Dept. of Comput. Eng., Univ. Autonoma de Madrid.
IEEE Trans Neural Netw. 1997;8(4):827-34. doi: 10.1109/72.595879.
This paper presents an online system for fraud detection of credit card operations based on a neural classifier. Since it is installed in a transactional hub for operation distribution, and not on a card-issuing institution, it acts solely on the information of the operation to be rated and of its immediate previous history, and not on historic databases of past cardholder activities. Among the main characteristics of credit card traffic are the great imbalance between proper and fraudulent operations, and a great degree of mixing between both. To ensure proper model construction, a nonlinear version of Fisher's discriminant analysis, which adequately separates a good proportion of fraudulent operations away from other closer to normal traffic, has been used. The system is fully operational and currently handles more than 12 million operations per year with very satisfactory results.
本文提出了一种基于神经分类器的信用卡操作欺诈检测在线系统。由于它安装在用于操作分发的交易中心，而不是发卡机构，它仅根据待评级操作及其紧邻的历史记录信息进行操作，而不依赖于持卡人过去活动的历史数据库。信用卡交易流量的主要特征包括正常操作与欺诈操作之间的巨大不平衡，以及两者之间的高度混合。为确保构建合适的模型，采用了费希尔判别分析的非线性版本，它能将相当一部分欺诈操作与更接近正常交易的操作充分区分开来。该系统已全面投入运行，目前每年处理超过1200万笔操作，结果非常令人满意。
