文献知网节
  • 记笔记
摘要:Real-time Fraud Detection has always been a challenging task,especially in financial, insurance, and telecom industries. There are mainly three methods, which are rule set, outlier detection and classification to solve the problem. But those methods have some drawbacks respectively. To overcome these limitations, we propose a new algorithm UAF(Usage Amount Forecast).Firstly, Manhattan distance is used to measure the similarity between fraudulent instances and normal ones. Secondly, UAF gives real-time score which detects the fraud early and reduces as much economic loss as possible. Experiments on various real-world datasets demonstrate the high potential of UAF for processing real-time data and predicting fraudulent users.
会议名称:

The Second International Conference of Young Computer Scientists, Engineers and Educators,ICYCSEE 2016

会议时间:

2016-08-20

会议地点:

中国黑龙江哈尔滨

  • 专辑:

    信息科技

  • 专题:

    计算机软件及计算机应用

  • 分类号:

    TP301.6

  • 手机阅读
    即刻使用手机阅读
    第一步

    扫描二维码下载

    "移动知网-全球学术快报"客户端

    第二步

    打开“全球学术快报”

    点击首页左上角的扫描图标

    第三步

    扫描二维码

    手机同步阅读本篇文献

  • HTML阅读
  • CAJ下载
  • PDF下载

下载手机APP用APP扫此码同步阅读该篇文章

下载:9 页码:42-43 页数:2 大小:49k

相关推荐
  • 相似文献
  • 读者推荐
  • 相关基金文献
  • 关联作者
  • 相关视频