Abstract: Systems and methods for predicting future risk for a target entity are provided. A risk assessment system receives historical risk assessment data of the target entity and identifies a target cluster that matches the historical risk assessment data. The target cluster is identified from a group of clusters determined using high dimensional clustering based on risk assessment data of a set of entities. The risk assessment system identifies a set of nearest neighbors of the target cluster and determines a prediction of future risk for the target entity based on the target cluster and the set of nearest neighbors. The risk assessment system transmits a responsive message, which can include the prediction of future risk, to a remote computing device for use in controlling access of the target entity to one or more interactive computing environments.

Abstract: A device may include a processor. The processor may be configured to: receive a request to re-rank a list of ranked agents or callers; when there are more agents than callers, generate a list of re-ranked agents based on the list of ranked agents; and provide the list of re-ranked agents to a component in a system for routing calls to one of agents identified in the list of re-ranked agents.

Abstract: Disclosed are systems and methods that provide a computerized framework for classifying data at scale, particularly when dealing with complex datasets that include ambiguous, incomplete and/or non-standard data. The disclosed framework provides a computerized solution that is scalable, accurate and capable of handling the complexities of the dataset while ensuring data treatment was done in line with legal and security requirements. The framework can operate to classify text-based data even when the data itself is incomplete, ambiguous, overlapping, and/or inconsistent, which can be particularly important for a wireless provider, where data is often scattered across different systems, and the information available for classification might be fragmented or contradictory. The framework provides functionality for classifying data automatically and efficiently, even as the volume of data grows exponentially.

Abstract: A system described herein may maintain a set of requestor models and a set of support agent models; identify, for each support agent of a plurality of support agents, a corresponding support agent model; monitor a state of a queue that includes a plurality of support requests; receive a request to pair a particular support request, of the plurality of support requests, with a support agent, wherein the particular support request is received from a particular requestor; identify a particular requestor model associated with the particular support request; select, based on the particular requestor model, the particular support agent model, and the state of the queue, a particular support agent; and establish a communication session between the selected particular support agent and the particular requestor.

Abstract: Disclosed are systems and methods that provide a computerized framework for classifying data at scale, particularly when dealing with complex datasets that include ambiguous, incomplete and/or non-standard data. The disclosed framework provides a computerized solution that is scalable, accurate and capable of handling the complexities of the dataset while ensuring data treatment was done in line with legal and security requirements. The framework can operate to classify text-based data even when the data itself is incomplete, ambiguous, overlapping, and/or inconsistent, which can be particularly important for a wireless provider, where data is often scattered across different systems, and the information available for classification might be fragmented or contradictory. The framework provides functionality for classifying data automatically and efficiently, even as the volume of data grows exponentially.

Abstract: Systems and methods for predicting future risk for a target entity are provided. A risk assessment system receives historical risk assessment data of the target entity and identifies a target cluster that matches the historical risk assessment data. The target cluster is identified from a group of clusters determined using high dimensional clustering based on risk assessment data of a set of entities. The risk assessment system identifies a set of nearest neighbors of the target cluster and determines a prediction of future risk for the target entity based on the target cluster and the set of nearest neighbors. The risk assessment system transmits a responsive message, which can include the prediction of future risk, to a remote computing device for use in controlling access of the target entity to one or more interactive computing environments.

Abstract: 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. Clustering can include determining a number of clusters to be generated for the dataset. A dataset used for training a neural network model configured can be clustered into a set of clusters. The clustering can include determining the number of clusters, determining special features for the determined number of clusters, and re-clustering the dataset based on the special features. The neural network can be trained based on training samples selected from the 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In some aspects, a record-matching computing system for detecting fragmented records is provided. The record-matching system is configured to identify a list of candidate records for merging from a set of data records. The record-matching system determines a matching decision for each pair of candidate records in the list and generates a graph. The graph includes nodes representing respective candidate records and edges connecting the nodes. Each edge represents a match between a pair of nodes connected by the edge according to the matching decisions. The record-matching system detects a connected component in the graph from which a qualified connected component is identified based on the minimum connectivity of the qualified connected component. The record-matching system updates the set of data records stored by merging candidate records represented by the nodes in the qualified connected component.

Abstract: In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record-matching computing system is configured to receiving a query record and search in a data repository storing data records for a record that matches the query record. The record-matching computing system retrieves a reference record from the data records and generates multiple identifier scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and the reference record. The record-matching computing system generates an overall matching score by combining at least two of the identifier scores and determines the reference record as a match to the query record based on the overall matching score exceeding a threshold value.

Abstract: In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record-matching computing system is configured to search for a data record that matches a query record. The record-matching computing system retrieves a reference record from data records and generates multiple identifier attributes for the query record and reference record, including identifier scores and compound scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and reference record. A compound score is generated by combining two or more identifier scores. The record-matching computing system applies the identifier attributes to a machine learning model configured to predict a match classification based on input identifier attributes for a pair of data records.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.