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Patent NumberGrant DateDescription
9,954,87904/24/2018Workflow platform that allows customer teams to build and update their fraud processes without needing to write code; enables set up of workflows that automate any type of fraud detection achievable on Sift's platform.
10,284,58205/07/2019Workflow platform that allows customer teams to build and update their fraud processes without needing to write code; enables set up of workflows that automate any type of fraud detection achievable on Sift's platform.
10,643,21605/05/2020Workflow platform that allows customer teams to build and update their fraud processes without needing to write code; enables set up of workflows that automate any type of fraud detection achievable on Sift's platform.
9,978,06705/22/2018 Enables the classification of multiple types of fraud and abuse simultaneously on a single account via the use of global and custom models, and ensemble of models. A distinct fraud score can be computed for each listed type of fraud.
10,108,962 10/23/2018Enables the classification of multiple types of fraud and abuse simultaneously on a single account via the use of global and custom models, and ensemble of models. A distinct fraud score can be computed for each listed type of fraud.
10,296,912 05/21/2019Enables the classification of multiple types of fraud and abuse simultaneously on a single account via the use of global and custom models, and ensemble of models. A distinct fraud score can be computed for each listed type of fraud.
10,402,828 09/03/2019Enables the classification of multiple types of fraud and abuse simultaneously on a single account via the use of global and custom models, and ensemble of models. A distinct fraud score can be computed for each listed type of fraud.
10,181,032 01/15/2019 Enables the detection of account misappropriation and produces a risk score that indicates when an account may be being used by someone other than the original creator.
10,482,395 11/19/2019Enables the detection of account misappropriation and produces a risk score that indicates when an account may be being used by someone other than the original creator.
10,339,472 07/02/2019 Enables migration from an old risk scoring model to a new risk scoring model for a given customer to address changes and trends in fraud patterns. The calibration keeps score distributions stable even when Sift migrates customers between model types.
10,572,832 02/25/2020Enables migration from an old risk scoring model to a new risk scoring model for a given customer to address changes and trends in fraud patterns. The calibration keeps score distributions stable even when Sift migrates customers between model types.
10,341,374 07/02/2019 Provides an analytical framework for evaluating anomalous shifts in risk scores for a given customer, allowing Sift to validate a new scoring model for a given customer before deployment and blocks deployment until validated.
10,462,172 10/29/2019Provides an analytical framework for evaluating anomalous shifts in risk scores for a given customer, allowing Sift to validate a new scoring model for a given customer before deployment and blocks deployment until validated.
10,623,423 04/14/2020 Prevents interferences between customer analysts reviewing a transaction within the Sift platform, providing real time updates to systems and client browsers interacting with the review queue.
10,491,617 11/26/2019 Varies the weights on a per customer basis of the models that make up Sift’s global scoring model to generate more accurate and specific risk scores.
10,666,674 05/26/2020Varies the weights on a per customer basis of the models that make up Sift’s global scoring model to generate more accurate and specific risk scores.
11,070,585 07/20/2021 Produces a prediction that includes a risk score for posted content, and may include multiple distinct models that operate together as a unified risk model to predict whether abuse or fraudulent content is likely to occur.
11,303,665 04/12/2022Produces a prediction that includes a risk score for posted content, and may include multiple distinct models that operate together as a unified risk model to predict whether abuse or fraudulent content is likely to occur.
10,929,756 02/23/2021 Provides a proxy model for interpreting complex black box models by constructing a surrogate model that mimics outputs of a black box model.
10,897,479 01/19/2021 Provides automatic multi-factor authentication to Sift's service, enabling both direct verification requests and verification requests triggered by an automated workflow. Verification data may be used as training data to improve customer-specific models.
10,958,673 03/23/2021Provides automatic multi-factor authentication to Sift's service, enabling both direct verification requests and verification requests triggered by an automated workflow. Verification data may be used as training data to improve customer-specific models.
10,997,608 05/04/2021 Enables a customer to Sift's service to determine false positive rates in declines or adverse decisions output from automated workflows.
11,068,910 07/20/2021Enables a customer to Sift's service to determine false positive rates in declines or adverse decisions output from automated workflows.
11,037,173 06/15/2021 Allows for automated anomaly detection in decisions output from automated workflows.
11,049,116 06/29/2021Allows for automated anomaly detection in decisions output from automated workflows.
11,330,009 05/10/2022 Implements text clustering models and techniques to surface fraudulent or abusive patterns in online content across users.
11,528,290 12/13/2022Implements text clustering models and techniques to surface fraudulent or abusive patterns in online content across users.
11,429,974 08/30/2022 Selectively identifies salient signals for card testing and converts those signals into learnable features that may be added to an existing machine learning.
11,620,653 04/04/2023Selectively identifies salient signals for card testing and converts those signals into learnable features that may be added to an existing machine learning.
11,409,629 08/09/2022 Enables robust testing of workflow routes for identifying optimal routes for improving automated proposals for digital handling.
11,573,883 02/07/2023Enables robust testing of workflow routes for identifying optimal routes for improving automated proposals for digital handling.
11,496,501 11/08/2022 Enables bulk labeling of corpora of data samples using a variety of techniques for exploring and identifying groups or networks of fraudulent and legitimate data samples.
11,645,386 05/09/2023Enables bulk labeling of corpora of data samples using a variety of techniques for exploring and identifying groups or networks of fraudulent and legitimate data samples.
11,575,695 02/07/2023 Enables the creation of a connected component graph or network for exposing potential large scale attacks, such as bot attacks.
11,496,501 01/08/2022 Introduces an active learning-informed data sampling technique for creating a labeled corpus of samples for effectively training a model.
Patent allowed Provides several mechanisms for automatically creating workflows and workflow routes for new and existing Sift customers.
Patent allowed Creates and enables an automated agent for accelerating chargeback disputes by automatically scoring the success of a chargeback based on known transaction evidence and proposing transaction evidence that may improve the probability of success.
Patent pending Enables customers and partners to integrate with and interchange data through Sift's systems by an extensible webhook service.
11,720,668 08/08/2023 Identifies anomalies in risk score distributions including shifts or drifts to generate an explanation for the anomalous behavior(s) together with corrective actions taken to mitigate the anomalies.
Second patent allowedIdentifies anomalies in risk score distributions including shifts or drifts to generate an explanation for the anomalous behavior(s) together with corrective actions taken to mitigate the anomalies.
11,777,962 10/03/2023 Identifies fraudulent automated bot activities and generates a unique bot signature for each distinct bot that is detected and which can be leveraged in real-time bot identification to accelerate detection and threat mitigation posed by malicious bots.
Second patent pendingIdentifies fraudulent automated bot activities and generates a unique bot signature for each distinct bot that is detected and which can be leveraged in real-time bot identification to accelerate detection and threat mitigation posed by malicious bots.
Pending Patent pending Allows customers to evaluate and respond to events based on multiple scoring criteria by introducing a technique that assigns both an event score and one or more percentile scores to each evaluated event, and enables customers to design related workflows. This invention involves using a T-Digest algorithm for calibration.