Sift Science, Inc.

Keren Gu

Fred Sadaghiani

Alex Paino

Jacob Burnim

Noah Grant

Eugenia Ho

Gary Lee

Doug Beeferman

Micah Wylde

Thomas Schiavone

Vera Dadok

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: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.

System and methods for digital account threat detection

Systems and methods include implementing a remote machine learning service that collects digital event data; collecting incumbent digital threat scores generated by an incumbent machine learning model and successor digital threat scores generated by a successor digital threat machine learning (ML) model; implementing anomalous-shift-detection that detects whether the successor digital threat scores of the successor digital threat ML model produces an anomalous shift; if the anomalous shift is detected by the machine learning model validation system, blocking a deployment of the successor digital threat model to a live ensemble of digital threat scoring models; or if the anomalous shift is not detected by the machine learning model validation system, deploying the successor digital threat ML model by replacing the incumbent digital threat ML model in a live ensemble of digital threat scoring models with the successor digital threat ML model.

Systems and methods detecting and mitigating anomalous shifts in a machine learning model

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

Keren Gu