公开(公告)号：US10902426B2

公开(公告)日：2021-01-26

申请号：US13367344

申请日：2012-02-06

申请人： Scott M. Zoldi ,  Jun Zhang ,  Yuting Jia

发明人： Scott M. Zoldi ,  Jun Zhang ,  Yuting Jia

IPC分类号： G06Q20/40 ,  G06Q30/00 ,  G06Q40/02

摘要： This document presents multi-layered, self-calibrating analytics for detecting fraud in transaction data without substantial historical data. One or more variables from a set of variables are provided to each of a plurality of self-calibrating models that are implemented by one or more data processors, each of the one or more variables being generated from real-time production data related to the transaction data. The one or more variables are processed according to each of the plurality of self-calibrating models implemented by the one or more data processors to produce a self-calibrating model output for each of the plurality of self-calibrating models. The self-calibrating model output from each of the plurality of self-calibrating models is combined in an output model implemented by one or more data processors. Finally, a fraud score output for the real-time production data is generated from the self-calibrating model output.

公开(公告)号：US20130204755A1

公开(公告)日：2013-08-08

申请号：US13367344

申请日：2012-02-06

申请人： Scott M. Zoldi ,  Jun Zhang ,  Yuting Jia

发明人： Scott M. Zoldi ,  Jun Zhang ,  Yuting Jia

IPC分类号： G06Q40/00

CPC分类号： G06Q20/4016 ,  G06Q30/0185 ,  G06Q40/02

摘要： This document presents multi-layered, self-calibrating analytics for detecting fraud in transaction data without substantial historical data. One or more variables from a set of variables are provided to each of a plurality of self-calibrating models that are implemented by one or more data processors, each of the one or more variables being generated from real-time production data related to the transaction data. The one or more variables are processed according to each of the plurality of self-calibrating models implemented by the one or more data processors to produce a self-calibrating model output for each of the plurality of self-calibrating models. The self-calibrating model output from each of the plurality of self-calibrating models is combined in an output model implemented by one or more data processors. Finally, a fraud score output for the real-time production data is generated from the self-calibrating model output.

摘要翻译： 本文档提供了多层次的自校准分析，用于检测交易数据中的欺诈，而无需大量历史数据。 将一组变量中的一个或多个变量提供给由一个或多个数据处理器实现的多个自校准模型中的每一个，所述一个或多个变量中的每个变量是从与交易相关的实时生产数据生成的 数据。 根据由一个或多个数据处理器实现的多个自校准模型中的每一个来处理一个或多个变量，以产生用于多个自校准模型中的每一个的自校准模型输出。 从多个自校准模型中的每一个输出的自校准模型在由一个或多个数据处理器实现的输出模型中组合。 最后，从自校准模型输出生成用于实时生产数据的欺诈分数输出。