EQUIFAX INC.

In some aspects, a compliance computing system can generate an evidence repository including evidence data received via an application programming interface (API) from one or more external systems. The system can generate an evidence repository including evidence data received via an application programming interface (API) from one or more systems. The system can receive a compliance request including a compliance query, the compliance query including a requirement identifier associated with a target requirement. The system can also query the evidence repository based on the requirement identifier to retrieve the evidence data associated with the target requirement. Finally, the system can transmit, via a firewall of the compliance computing system, a response message to an external computing system, wherein the response message includes the retrieved evidence data.

Systems and methods for consolidating data sources for expediting validation processes are described herein. A user interface can be provided to a first entity. First risk assessment data associated with the first entity can be received via the user interface. Second risk assessment data associated with the first entity can be received. The first risk assessment data and the second risk assessment data can be validated. The first risk assessment data and the second risk assessment data can be output for display on the user interface. A responsive message including the validated first risk assessment data and the validated second risk assessment data can be transmitted to a remote computing device for use in controlling access of the first entity to one or more interactive computing environments.

In an example of a method described herein, historical events occurring over a network are detected, and at least one of the historical events is associated with an observed value of a categorical variable. A numerical aggregate value representing the observed value is updated by applying an exponential smoothing function to (i) a prior numerical aggregate value representing prior historical events associated with the observed value and (ii) a count of the historical events associated with the observed value. An event occurring over the network is detected and is associated with the observed value. Features are extracted from the event, where the features include an encoded feature based on the numerical aggregate value to represent the observed value. A predictive model is applied to the features to determine a score representing likelihood of an outcome. Based on the score, access to a resource of the network is controlled.

In some aspects, an entity-resolution computing system for entity resolution is provided. The entity-resolution computing system includes an entity-resolution server configured for correlating data objects from an identity data repository that contains account or transaction data for entities based on the data objects including a common portion of the account or transaction data. The entity-resolution server updates the identity data repository to include an entity identifier that links the data objects and indicates that the data objects refer to a common entity. The entity-resolution server creates an entity-resolution data structure having the data objects with the entity identifier and a new variant data object containing a modified version of account or transaction data that match the common entity. The entity-resolution server encrypts the entity-resolution data structure and causes the encrypted entity-resolution data structure to be transmitted to a client computing system for use in augmenting client data.

Techniques for risk evaluation include receiving, from a requesting entity, a request for monitoring target entities specifying a first identifier associated with each target entity and target entity information. The system generates a second identifier and a third identifier for each target entity and stores a mapping of the second identifiers to the first identifiers and the third identifiers, preventing the second identifiers from being provided to the requesting entity. The system monitors a periodically updated data set and determines risk metrics for the target entities, comparing each risk metric to a threshold value to identify target entities whose risk data indicates an insider threat. The system generates a third identifier for the identified target entities and provides the third identifiers to the requesting entity. Responsive to a request for a corresponding first identifier, the system identifies and provides the first and third identifiers to the requesting entity.

A durability assessment system may receive a request, from a computing system, for a durability index describing an entity. The durability assessment system may determine the durability index based on information about the resource usage by the entity, such as a resource availability score or a resource allocation score. The durability assessment system may compare the obtained resource availability score and resource allocation score to ranges associated with a set of durability indices. Based on the comparison, the durability assessment system may determine a durability index for the entity. The durability index may indicate an ability of the entity to return accessed resources. In some cases, the durability assessment system may provide the durability index to an allocation computing system that is configured to determine whether to grant access to resources based on the durability index.

Systems and methods for using machine-learning techniques to provide risk assessment based on multiple sources of data are described herein. Data about an entity can be received, and the data can be authenticated. Integrated risk data about the entity can be received. The integrated risk data can include traditional risk assessment data and nontraditional risk assessment data. An integrated risk assessment value can be determined based on the integrated risk data by aligning a first output from a first risk assessment model and a second output by a second risk assessment model. A responsive message including at least the integrated risk assessment value and associated information for the entity can be transmitted to a remote computing device for use in controlling access of the entity to one or more interactive computing environments.

Systems and methods for automated path-based recommendation for risk mitigation are provided. An entity assessment server, responsive to a request for a recommendation for modifying a current risk assessment score of an entity to a target risk assessment score, accesses an input attribute vector for the entity and clusters of entities defined by historical attribute vectors. The entity assessment server assigns the input attribute vector to a particular cluster and determines a requirement on movement from a first point to a second point in a multi-dimensional space based on the statistics computed from the particular cluster. The first point corresponds to the current risk assessment score and the second point corresponds to the target risk assessment score. The entity assessment server computes an attribute-change vector so that a path defined by the attribute-change vector complies with the requirement and generates the recommendation from the attribute-change vector.

Systems and methods for creating predictor variables from unstructured data for prediction models are provided. A variable creation application receives unstructured data and processing the unstructured data to generate processed data. Based on the processed data, the variable creation application generates an attribute pool that contains multiple predictor variables generated by applying natural language processing (NLP) procedures on the processed data. The variable creation application further executes a prediction model on at least the predictor variables in the attribute pool to generate a prediction result. Based on the prediction result, the variable creation application evaluates the predictive power of each of the predictor variables and retains predictor variables that are predictive as input predictor variables for the prediction model.

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.