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FALSE POSITIVE REDUCTION IN ABNORMALITY DETECTION SYSTEM MODELS

机译:异常检测系统模型中的假阳性减少

摘要

The subject matter disclosed herein provides methods, apparatus, systems, techniques, and articles for false positive reduction in abnormality detection models. A date and time of a first transaction by a transaction entity and associated with a transaction characteristic can be stored. Data representing subsequent transactions associated with the transaction characteristic can be stored. A history marker profile specific to the transaction characteristic and transaction entity can be generated and can include the transaction characteristic, the date and time of the first transaction, and maximum and mean abnormality scores. A date and time of a current transaction can be determined. A current abnormality score for the current transaction can be received. A tenure of the observed transaction characteristic can be computed. The current abnormality score can be recalibrated from the transaction entity abnormality detection system according to the maximum, mean, and current abnormality scores and a length of the tenure.
机译:本文公开的主题提供用于异常检测模型中的假阳性减少的方法,装置,系统,技术和物品。可以存储交易实体与交易特征相关联的第一笔交易的日期和时间。可以存储表示与交易特征相关联的后续交易的数据。可以生成特定于交易特征和交易实体的历史标记配置文件,并且可以包括交易特征,第一次交易的日期和时间,以及最大和平均异常分数。可以确定当前交易的日期和时间。可以接收当前交易的当前异常评分。可以计算观察到的交易特征的使用期限。可以根据最大,平均和当前异常评分以及任职期限从交易实体异常检测系统重新校准当前异常评分。

著录项

  • 公开/公告号EP3304446A1

    专利类型

  • 公开/公告日2018-04-11

    原文格式PDF

  • 申请/专利权人 FAIR ISAAC CORPORATION;

    申请/专利号EP20160728507

  • 发明设计人 ZOLDI SCOTT M.;GRIEGEL DAVID;

    申请日2016-05-26

  • 分类号G06Q10/04;G06Q10/10;G06Q20/40;

  • 国家 EP

  • 入库时间 2022-08-21 13:17:36

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