- 专利标题: Systems and methods for configuring and implementing a card testing machine learning model in a machine learning-based digital threat mitigation platform
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申请号: US17379068申请日: 2021-07-19
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公开(公告)号: US11429974B2公开(公告)日: 2022-08-30
- 发明人: Wei Liu , Kevin Lee , Hui Wang , Rishabh Kothari , Helen Marushchenko
- 申请人: Sift Science, Inc.
- 申请人地址: US CA San Francisco
- 专利权人: Sift Science, Inc.
- 当前专利权人: Sift Science, Inc.
- 当前专利权人地址: US CA San Francisco
- 代理机构: Alce PLLC
- 代理商 Padowithz Alce; Chandler Scheitlin
- 主分类号: G06Q20/40
- IPC分类号: G06Q20/40 ; G06F17/18 ; G06N20/00
摘要:
Systems and methods for detecting digital abuse or digital fraud that involves malicious account testing includes implementing a machine learning threat model that predicts malicious account testing using misappropriate accounts, wherein a subset of a plurality of learnable variables of an algorithmic structure of the machine learning threat model includes one or more learnable variables derived based on feature data indicative of malicious account testing; wherein implementing the machine learning threat model includes: (i) identifying event data from an online event that is suspected to involve digital fraud or digital abuse, (ii) extracting adverse feature data from the event data that map to the one or more learnable variables of the subset, and (iii) providing the adverse feature data as model input to the machine learning threat model; and computing, using the machine learning threat model, a threat prediction indicating a probability that the online event involves malicious account testing.
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