Equifax Inc.

Steven Hicklin

Stephen Leitner

Mark Burgess

Keith Manthey

Lester Anthony Pieniazek

Noushin Rezapour Asheghi

In some aspects, a content-extraction system can receive a query from a client device and generate a result set of digital content responsive to the query. For instance, the content-extraction system can obtain, from a search system, a set of digital content matching one or more keywords. The content-extraction system can exclude digital content items lacking core content, digital content items with duplicative content, or both. In some aspects, the content-extraction system can determine, for one or more remaining digital content items, a content attribute score. The content-extraction system can select, as the result set of digital content, a subset of digital content based on the content attribute scores. The content-extraction system can output the result set to the client device.

Machine-learning system for servicing queries for digital content

Examples are disclosed for detecting synthetic online entities that may be used for fraudulent purposes or other purposes. In some aspects, a computing system can generate a data structure that includes nodes and links between the nodes. The nodes can represent online entities and the links can represent geographic associations or transactional associations between pairs of online entities. These associations can be identified from electronic transactions involving the online entities. The computing system can determine, from the links between the nodes, that a degree of connectivity among a subset of the nodes exceeds a threshold connectivity. The degree of connectivity indicates electronic communications involving online entities represented by the subset of the nodes. The computing system can transmit, based on the degree of connectivity exceeding the threshold connectivity, an alert indicating a potential synthetic entity (e.g., potentially fraudulent activity) within the subset of the nodes.

Detecting synthetic online entities

In some aspects, a computing system can receive a request for a processing operation that involves a query. The computing system can extract a query parameter indicating an entity or information type. The computing system can parse, using the query parameter, an obfuscated dataset that is generated by electronically transforming (i) first sensitive information into first obfuscated data using an obfuscation key and (ii) second sensitive information into second obfuscated data using the obfuscation key. The sensitive information is unreadable in the obfuscated data. The computing system can match the query parameter to the first obfuscated data and the second obfuscated data based on a relationship between the first obfuscated data and the second obfuscated data. The relationship is independent of a meaning of the first and second sensitive information. The computing system can performing the processing operation using the first and second obfuscated data.

Generating and processing obfuscated sensitive information

A global matching system can include a processing device and a memory device on which instructions can be stored. The processing device can execute the instructions to receive input data to be matched to target data stored in the memory device. The processing device can identify at least one context parameter for the input data. The context parameter indicate a location-specific characteristic or culturally specific characteristic of an entity associated with the input data. The processing device can access program code for matching tools and select a subset of the matching tools' program code based on the identified context parameter. The processing device can identify, based on the identified context parameter, an order of execution for the selected program code. The processing device can execute the selected program code in the identified order of execution and output a matching result for the input data and the target data.

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York, United Kingdom

Steven Hicklin