Sift Patents

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Enables a customer to Sift’s service to determine false positive rates in declines or adverse decisions output from automated workflows.

Enables a customer to Sift’s service to determine false positive rates in declines or adverse decisions output from automated workflows.

Allows for automated anomaly detection in decisions output from automated workflows.

Allows for automated anomaly detection in decisions output from automated workflows.

Implements text clustering models and techniques to surface fraudulent or abusive patterns in online content across users.

Implements text clustering models and techniques to surface fraudulent or abusive patterns in online content across users.

Selectively identifies salient signals for card testing and converts those signals into learnable features that may be added to an existing machine learning.

Selectively identifies salient signals for card testing and converts those signals into learnable features that may be added to an existing machine learning.

Enables robust testing of workflow routes for identifying optimal routes for improving automated proposals for digital handling.

Enables robust testing of workflow routes for identifying optimal routes for improving automated proposals for digital handling.

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.

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.

Enables the creation of a connected component graph or network for exposing potential large scale attacks, such as bot attacks.

Introduces an active learning-informed data sampling technique for creating a labeled corpus of samples for effectively training a model.

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.

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.

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.

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.

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.

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.

Provides several mechanisms for automatically creating workflows and workflow routes for new and existing Sift customers.

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.