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METHOD AND SYSTEM FOR DETECTING FRAUDULENT TRANSACTIONS USING A FRAUD DETECTION MODEL TRAINED BASED ON DYNAMIC TIME SEGMENTS

机译:使用基于动态时间段训练的欺诈检测模型来检测欺诈交易的方法和系统

摘要

Certain aspects of the present disclosure provide techniques for detecting fraudulent transactions in a transaction processing system. An example method generally includes receiving a request to process a transaction. An input data set including a vector representing the transaction and a plurality of vectors representing historical transactions is generated. The input data set is divided into a plurality of ragged tensors corresponding to non-overlapping time segments of variable length and having a plurality of vectors associated with dates within each time segment A reduced input data set is generated by generating, for each respective ragged tensor of the plurality of ragged tensors, a respective representative vector using max pooling over vectors in the ragged tensor. A fraudulent transaction score is generated based on the reduced input data set using a fraud detection model. The transaction is processed based, at least in part, on the fraudulent transaction score.
机译:本公开的某些方面提供了用于在事务处理系统中检测欺诈事务的技术。 示例方法通常包括接收处理事务的请求。 生成包括表示事务的矢量和表示历史事务的多个向量的输入数据集。 将输入数据集被划分为与可变长度的非重叠时间段对应的多个缠绕的卷曲,并且通过生成每个相应的缠绕的张量来生成减少的输入数据集的每个时间段内的日期相关联的多个矢量。 在多个缠结的张力,相应的代表矢量使用MAX汇集在缠绕的张量中的矢量上。 基于使用欺诈检测模型的缩小输入数据集生成欺诈事务分数。 至少部分地基于欺诈性交易分数来处理交易。

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