EQUIFAX INC.

A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.

A power graph convolutional network (PGCN) can be used for explainable machine learning. For example, a computing device can determine, using a PGCN, a risk indicator for a target entity from predictor variables associated with the target entity. The PGCN includes a convolutional layer configured to generate modified predictor variables by multiplying each of the predictor variables with a respective interaction factor generated based on an adjacency weight matrix and the predictor variables. The PGCN also includes a dense layer configured to generate the risk indicator. The training process involves adjusting a set of weights in the adjacency weight matrix and a set of weights in a weight vector of the dense layer based on a loss function of the PGCN. The computing device transmits a responsive message including the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.

A system can generate a risk assessment associated with a target entity. The system can determine an attribute tier for each entity in a set of entities. For each attribute tier, the system can: generate a model configured to predict a percent change in the attribute over a time period for each entity in the respective attribute tier; determine the percent change in the attribute for each entity in the respective attribute tier using the model associated with the respective attribute tier; rank each entity in the attribute tier based on the predicted percent change in the attribute; and assign a score to each entity in the attribute tier based on the rank of the respective entity and on a preconfigured distribution. The system can determine a risk indicator based, in part, on the score.

A system can receive data that includes a first subset and a second subset relating to a first application and a second application in a cloud computing environment. The system can determine differences between the first subset and the second subset by comparing the first subset and the second subset. The system can generate, by using the differences, quality metrics for a transformation from the first application to the second application in the cloud computing environment. The system can generate, by using historical data that includes historical first subsets and second subsets, a trend that represents a progression of the transformation. The system can determine, by using the differences, the quality metrics, and the trend, code in the second application that is likely to cause the differences. The system can generate a command that is executable in the cloud computing environment to automatically update the code in the second application.

An event restraint-delivery computing system receives event data in a high-volume event stream from source computing systems. For each event data object in the high-volume event data stream, the event restraint-delivery computing system identifies at least one event data object subset associated with a respective recipient computing system. The event restraint-delivery computing system withholds the event data object subset from the respective recipient computing system based on restraint status data for the event data object subset. Based on a change in the respective restraint status data, such as a change indicating that the event data object subset is modified to fulfill a trigger criterion, the event restraint-delivery computing system provides the event data object subset to the respective recipient computing system. In addition, the respective recipient computing system performs a computing function based on event data in the event data object subset.

A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.

In some aspects, a computing system can improve a machine learning model for risk assessment by removing or reducing bias in the machine learning model. The training process for the machine learning model can include training the machine learning model using training samples, obtaining data for a protected attribute, and calculating a bias metric using the data for the protected attribute and data obtained from the trained machine learning model. Based on the bias metric, bias associated with the machine learning model can be detected. The machine learning model can be modified based on the detected bias and re-trained. The re-trained machine learning model can be used to predict a risk indicator for a target entity. The predicted risk indicator can be transmitted to a remote computing device and be used for controlling access of the target entity to one or more interactive computing environments.

A system can generate a risk assessment associated with an identity element used in interactions associated with a user entity. For example, the system can receive historical data related an identity element associated with a user entity, the identity element used in a set of interactions associated with the user entity. The system can generate a binomial distribution of the historical data associated with the identity element. The system can determine, based at least in part on the binomial distribution of the historical data, a risk indicator associated with the identity element. The system can control, based at least in part on the risk indicator associated with the identity element, an interaction involving a target entity and the user entity using the identity element.

A system can be used to automatically generate infrastructure using a dynamic DAG. The system can receive an input file that can include indications of computing resources for a cloud computing environment. The system can parse the input file to determine a topology of the computing resources based on indications. The system can generate, based on the topology, a dynamic DAG for generating infrastructure for the cloud computing environment based on the computing resources. Generating the dynamic DAG can include (i) determining a first subset of the computing resources and a second subset of the computing resources, and (ii) configuring the first subset of the computing resources to be processed differently from the second subset of the computing resources.

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.