Sift Science, Inc.

Noah Grant

Fred Sadaghiani

Keren Gu

Eugenia Ho

Jacob Burnim

Alex Paino

Gary Lee

Doug Beeferman

Micah Wylde

Aleksandr Lopatin

Megan Mann

Systems and methods include: receiving digital event type data that define attributes of a digital event type; receiving digital fraud policy that defines a plurality of digital processing protocols; transmitting via a network the digital event data and the digital fraud policy to a remote digital fraud mitigation platform; using the digital event data to configure a first computing node comprising an events data application program interface or an events data computing server to detect digital events that classify as the digital event type; using digital fraud policy to configure a second computing node comprising a decisioning API or a decisioning computing server to automatically evaluate and automatically select one digital event processing outcome of a plurality of digital event processing outcomes that indicates a disposal of the digital events classified as the digital event type; and implementing a digital threat mitigation application process flow that evaluates digital event data.

System and methods for dynamic digital threat mitigation

Systems and methods include implementing a review queue interface that includes: a review queue comprising a listing of distinct review items; a current state for each of distinct review items; a listing for each review item of the distinct review items of one or more client browsers that are interacting with each review item; identifying client browser activity of the one or more client browsers; computing a computed state for each of distinct review items based on the client browser activity; computing changes to the state of review items based on an assessment of the current state and the computed state for each of distinct review items; and automatically updating a state of one or more of the distinct review items within the review queue interface based on a difference between the current state and the computed state of the one or more of the distinct review items.

Systems and methods for intelligently implementing a machine learning-based digital threat mitigation service

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.

Systems and methods include: collecting digital event data from at least one remote source of digital event data; using the collected digital event data as input into primary machine learning ensemble that predicts the likelihood of digital fraud and/or digital abuse; generating by the machine learning system the global digital threat score; identifying a sub-request for a specific digital threat score for a digital abuse type; in response to identifying the sub-request, providing the input of the collected digital event data to a secondary machine learning model ensemble of the machine learning system that predicts a likelihood of the identified digital abuse type; generating by the secondary machine learning ensemble the specific digital threat score for the digital abuse type based on the input of the collected digital event data; and transmitting the global digital threat score and the specific digital threat score for the identified digital abuse type.

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San Francisco, CA, United States of America

Noah Grant