Sift Intellectual Property

Patents

Sift’s services leverage the features and functionality of different patents owned by Sift Science, Inc. as listed below. For more information about our services, please see our Terms of Service and our Digital Trust & Safety product suite.

Patent Number	Grant Date	Description
9,954,879	04/24/2018
10,284,582	05/07/2019
10,643,216	05/05/2020	Workflow 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.
9,978,067	05/22/2018
10,108,962	10/23/2018
10,296,912	05/21/2019
10,402,828	09/03/2019	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.
10,181,032	01/15/2019
10,482,395	11/19/2019	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.
10,339,472	07/02/2019
10,572,832	02/25/2020	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.
10,341,374	07/02/2019
10,462,172	10/29/2019	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.
10,623,423	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
10,666,674	05/26/2020	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.
11,070,585	07/20/2021
11,303,665	04/12/2022	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.
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
10,958,673	03/23/2021	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.
10,997,608	05/04/2021
11,068,910	07/20/2021	Enables 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
11,049,116	06/29/2021	Allows for automated anomaly detection in decisions output from automated workflows.
11,330,009	05/10/2022
11,528,290	12/13/2022	Implements text clustering models and techniques to surface fraudulent or abusive patterns in online content across users.
11,429,974	08/30/2022
11,620,653	04/04/2023	Selectively 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
11,573,883	02/07/2023	Enables robust testing of workflow routes for identifying optimal routes for improving automated proposals for digital handling.
11,496,501	11/08/2022
11,645,386	05/09/2023	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.
11,575,695	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
	Second patent allowed	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.
11,777,962	10/03/2023
	Second patent pending	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.
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.

Trademarks

Sift Science Inc. (“Sift”) uses the terms and/or logos (“Sift Trademarks”) below as active trademarks in branding its products and services. The Sift brand is an important symbol of our mission to help everyone trust the Internet. We have registered or applied for registration of these trademarks in the United States and other countries around the world.

Word Marks: Sift, FIBR, Sifters

Logos