-
公开(公告)号：US20240154975A1
公开(公告)日：2024-05-09
申请号：US18416883
申请日：2024-01-18
申请人： Sift Science, Inc.
发明人： Eric St. Pierre , Alex Forbess
CPC分类号： H04L63/1408 , G06Q20/4016
摘要： A system and method for accelerating a disposition of a digital dispute event includes routing a digital dispute event to one of a plurality of distinct machine learning-based dispute scoring models; computing, by the one of the plurality of distinct machine learning-based dispute scoring models, a preliminary machine learning-based dispute inference based on one or more features extracted from the digital dispute event, wherein the preliminary machine learning-based dispute inference relates to a probability of the subscriber prevailing against the digital dispute event based on each piece of evidence data of a service-proposed corpus of evidence data being available to include in a dispute response artifact; and generating the dispute response artifact based on the digital dispute event, wherein the generating includes installing one or more obtainable pieces of evidence data associated with the digital event into one or more distinct sections of the dispute response artifact.
-
2.发明公开公开(公告)号：US20230421584A1
公开(公告)日：2023-12-28
申请号：US18367171
申请日：2023-09-12
申请人： Sift Science, Inc.
发明人： Kostyantyn Gurnov , Wei Liu , Nicholas Benavides , Volha Leusha , Yanqing Bao , Louie Zhang , Irving Chen , Logan Davis , Andy Cai
CPC分类号： H04L63/1416 , H04L63/1425 , G06N20/20
摘要： A method for machine learning-based detection of an automated fraud or abuse attack includes: identifying, via a computer network, a digital event associated with a suspected automated fraud or abuse attack; composing, via one or more computers, a digital activity signature of the suspected automated fraud or abuse attack based on digital activity associated with the suspected automated fraud or abuse attack; computing, via a machine learning model, an encoded representation of the digital activity signature; searching, via the one or more computers, an automated fraud or abuse signature registry based on the encoded representation of the digital activity signature; determining a likely origin of the digital event based on the searching of the automated fraud or abuse signature registry; and selectively implementing one or more automated threat mitigation actions based on the likely origin of the digital event.
-
公开(公告)号：US20230124621A1
公开(公告)日：2023-04-20
申请号：US17963365
申请日：2022-10-11
申请人： Sift Science, Inc.
发明人： Pradhan Bagur Umesh , Yuan Zhuang , Hui Wang , Nicholas Benavides , Chang Liu , Yanqing Bao , Wei Liu
摘要： A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based digital threat scoring ensemble with the successor machine learning-based digital threat scoring ensemble.
-
公开(公告)号：US11429974B2
公开(公告)日：2022-08-30
申请号：US17379068
申请日：2021-07-19
申请人： Sift Science, Inc.
发明人： Wei Liu , Kevin Lee , Hui Wang , Rishabh Kothari , Helen Marushchenko
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.
-
公开(公告)号：US11330009B2
公开(公告)日：2022-05-10
申请号：US17180592
申请日：2021-02-19
申请人： Sift Science, Inc.
发明人： Wei Liu , Jintae Kim , Michael Legore , Yong Fu , Cat Perry , Rachel Mitrano , James Volz , Liz Kao
摘要： A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.
-
公开(公告)号：US20210168166A1
公开(公告)日：2021-06-03
申请号：US16905465
申请日：2020-06-18
申请人： Sift Science, Inc.
发明人： Wei Liu , Fred Sadaghiani
摘要： A machine learning-based system and method for identifying digital threats includes a threat service that: implements a unified threat model that produces a unified threat score that predicts both of: a level of threat of a piece of online content, and a level of threat that a target user will create a harmful piece of online content; wherein: implementing the unified threat model includes: receiving event data comprising historical content data for the target user and content data of the pending piece of online content and historical user digital activity data and real-time user activity data; and providing input of content feature data and user digital activity feature data to the unified threat model; and the unified threat model produces the unified threat score based on the content and the user digital activity data; and computes a threat mitigation action based on an evaluation of the threat score.
-
公开(公告)号：US10958673B1
公开(公告)日：2021-03-23
申请号：US17120864
申请日：2020-12-14
申请人： Sift Science, Inc.
发明人： Irving Chen , Shahar Ronen , Mark Lunney , Chloe Chi
IPC分类号： H04L29/06
摘要： A system and method for a machine learning-based score driven automated verification of a target event includes: receiving a threat verification request; extracting a corpus of threat features; predicting the machine learning-based threat score; evaluating the machine learning-based threat score against distinct stages of an automated disposal decisioning workflow; computing the activity disposal decision, wherein the activity disposal decision informs an action to allow or to disallow the target online activity; receiving the machine learning-based threat score as input into an automated verification workflow; computing whether an automated verification of the target online activity is required or not based on an evaluation of the machine learning-based threat score against distinct verification decisioning criteria of the automated verification workflow; automatically executing the automated verification of the target online activity and exposing results of the automated verification to the subscriber for allowing or for disallowing the target online activity.
-
公开(公告)号：US10339472B2
公开(公告)日：2019-07-02
申请号：US15941175
申请日：2018-03-30
申请人： Sift Science, Inc.
发明人： Fred Sadaghiani , Aaron Beppu , Jacob Burnim , Alex Paino
摘要： Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.
-
公开(公告)号：US10296912B2
公开(公告)日：2019-05-21
申请号：US16138311
申请日：2018-09-21
申请人： Sift Science, Inc.
发明人： Fred Sadaghiani , Alex Paino , Jacob Burnim , Keren Gu , Gary Lee , Noah Grant , Eugenia Ho , Doug Beeferman
摘要： Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.
-
公开(公告)号：US11528290B2
公开(公告)日：2022-12-13
申请号：US17714986
申请日：2022-04-06
申请人： Sift Science, Inc.
发明人： Wei Liu , Jintae Kim , Michael Legore , Yong Fu , Cat Perry , Rachel Mitrano , James Volz , Liz Kao
摘要： A machine learning-based system and method for content clustering and content threat assessment includes generating embedding values for each piece of content of corpora of content data; implementing unsupervised machine learning models that: receive model input comprising the embeddings values of each piece of content of the corpora of content data; and predict distinct clusters of content data based on the embeddings values of the corpora of content data; assessing the distinct clusters of content data; associating metadata with each piece of content defining a member in each of the distinct clusters of content data based on the assessment, wherein the associating the metadata includes attributing to each piece of content within the clusters of content data a classification label of one of digital abuse/digital fraud and not digital abuse/digital fraud; and identifying members or content clusters having digital fraud/digital abuse based on querying the distinct clusters of content data.
-
-
-
-
-
-
-
-
-
搜索结果
58 条记录 (耗时 0.028 秒)
