Equifax Inc.

Rajkumar Bondugula

Piyush V Patel

Christopher Charles Yasko

Samiyuru Geethanjana Senarathne Menik Hitihami Mudiyanselage

Piyushkumar Patel

Allan Joshua

Trevis J Litherland

Sunit Sivaraj

Hongchao Li

Bo Guo

Hannah Wang

Ligaj Pradhan

Michael Mcburnett

Li Hao

According to certain implementations, an access control system controls access to secured data that is stored on a secured source. A requestor system may request information representing the secured data. The access control system receives the secured data from the secured source, and selects a portion of the secured data based on a lens including a filter criteria or a modification instruction. Adjusted data may be generated based on a modification of the selected portion of data, where the modification is based on the lens. The access control system provides the adjusted data to the requestor system via an access interface. In some cases, upon completion of a time period, the access control system prevents the requestor system from accessing the adjusted data, by disabling the access interface used to access the adjusted data. The adjusted data may be deleted from the access control system.

Controlling access to secured data via timed filtering of data

A unit-classification system receives a data set with identity data objects corresponding to personal identity components. Feature vectors are determined for the identity data objects. A trained classifier model determines, based on a feature vector for each identity data object, whether the corresponding personal identity components are included in a identity component ('IC') category. The unit-classification system generates an IC identification for a first IC category, and associates the IC identification with a first identity data object corresponding to a first personal identity component. The unit-classification system identifies a second identity data object corresponding to a second personal identity component included in the first IC category. The unit-classification system modifies the first and second identity data objects to include the IC identification. Responsive to a request for the IC identification, the unit-classification system can provide a response that indicates the modified first and second identity data objects.

Verification of electronic identity components

Systems and methods for secure resource management are provided. A secure resource management system includes a resource record repository, such as a secure database or a blockchain, for storing resource records for resources. The resource records contain information of resource providers, information of resource users having a right to obtain resources, and resource transaction histories. Responsive to a request to verify an authorized user of a resource, the secure resource management system further queries the resource record repository, retrieves the resource record, determines the resource user currently having a right to obtain the resource as the authorized user of the resource, and transmits the verification result in response to the request. The verification result identifies the authorized user of the resource and can be used to grant access to the resource by the authorized user.

Secure resource management to prevent fraudulent resource access

In some aspects, techniques for creating representative and informative training datasets for the training of machine-learning models are provided. For example, a risk assessment system can receive a risk assessment query for a target entity. The risk assessment system can compute an output risk indicator for the target entity by applying a machine learning model to values of informative attributes associated with the target entity. The machine learning model may be trained using training samples selected from a representative and informative (RAI) dataset. The RAI dataset can be created by determining the informative attributes based on attributes used by a set of models and further extracting representative data records from an initial training dataset based on the determined informative attributes. The risk assessment system can transmit a responsive message including the output risk indicator for use in controlling access of the target entity to an interactive computing environment.

Data compression techniques for machine learning models

Systems and methods for secure resource management are provided. A secure resource management system receives from a client computing system associated with a resource provider a query for classifying a resource user associated with the resource provider. The secure resource management system determining a set of resource abuser criteria for classifying the resource user as a resource abuser. The secure resource management system determines that the resource user is a resource abuser or a potential resource abuser based on the set of resource abuser criteria and a resource transaction history associated with the resource user. Based on determining that the resource user is a resource abuser or a potential resource abuser, the secure resource management system generates and transmits a response to the query to the client computing system. The response can be used to restrict or deny access to the resource by the resource user.

Secure resource management to prevent resource abuse

In some aspects, systems and methods for efficiently clustering a large-scale dataset for improving the construction and training of machine-learning models, such as neural network models, are provided. A dataset used for training a neural network model configured can be clustered into a first set of clusters and a second set of clusters. The neural network model can be constructed with a number of nodes in a hidden layer that is based on the number of clusters in the first set of clusters. The neural network can be trained based on training samples selected from the second set of clusters. In some aspects, the trained neural network model can be utilized to satisfy risk assessment queries to compute output risk indicators for target entities. The output risk indicator can be used to control access to one or more interactive computing environments by the target entities.

Clustering techniques for machine learning models

In some aspects, a computing system can control access of a user computing device to a resource. The computing system can obtain an access request submitted by a user computing device. The computing system can verify permission information in the access request to determine that the access request is valid. If the access request is valid, the computing system submits an authentication request to request an authentication system to authenticate the user and obtains authentication results generated by the authentication system. The computing system further provides, based on the authentication results, an access control decision for the access request.

Secure online access control to prevent identification information misuse

According to certain implementations, a permissions gateway receives an access request indicating multiple sets of secured data that include high-granularity data stored on multiple secured data repositories. The access request is compared to a permission set with multiple consent parameters, which indicate access types for the secured data. Based on a comparison of the access request to a permission set, the permissions gateway queries, the permission gateway queries a first data repository for a high-granularity dataset that includes a portion of the high-granularity data, and queries a second data repository for a low-granularity dataset that includes a summary of part of the high-granularity data. The permissions gateway generates a multi-granularity response to the access request, based on a combination of the high-granularity dataset and the low-granularity dataset.

Controlling access to multi-granularity data

In some aspects, a computing system can receive, from a client device, a request to perform an analytical operation that involves a query regarding a common entity type. The computing system can extract a query parameter having a particular standardized entity descriptor for the common entity type and parse a transformed dataset that is indexed in accordance with standardized entity descriptors. The computing system can match the particular standardized entity descriptor from the query to records from the transformed dataset having index values with the particular standardized entity descriptor. The computing system can retrieve the subset of the transformed dataset having the index values with the particular standardized entity descriptor. In some aspects, the computing system can generate the transformed dataset by performing conversion operations that transform records in a data structure by converting a set of different entity descriptors into a standardized entity descriptor for the common entity type.

Data processing systems and methods for efficiently transforming entity descriptors in textual data

Certain aspects produce a scoring model that can automatically classify future text samples. In some examples, a processing device perform operations for producing a scoring model using active learning. The operations includes receiving existing text samples and searching a stored, pre-trained corpus defining embedding vectors for selected words, phrases, or documents to produce nearest neighbor vectors for each embedding vector. Nearest neighbor selections are identified based on distance between each nearest neighbor vector and the embedding vector for each selection to produce a text cloud. Text samples are selected from the text cloud to produce seed data that is used to train a text classifier. A scoring model can be produced based on the text classifier. The scoring model can receive a plurality of new text samples and provide a score indicative of a likelihood of being a member of a selected class.

Text classification using automatically generated seed data

In some aspects, a machine-learning model, which can transform input attribute values into a predictive or analytical output value, can be trained with training data grouped into attributes. A subset of the attributes can be selected and transformed into a transformed attribute used for training the model. The transformation can involve grouping portions of the training data for the subset of attributes into respective multi-dimensional bins. Each dimension of a multi-dimensional bin can correspond to a respective selected attribute. The transformation can also involve computing interim predictive output values. Each interim predictive output value can be generated from a respective training data portion in a respective multi-dimensional bin. The transformation can also involve computing smoothed interim output values by applying a smoothing function to the interim predictive output values. The transformation can also involve outputting the smoothed interim output values as a dataset for the transformed attribute.

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Marietta, GA, United States of America

Rajkumar Bondugula