Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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 system can generate one or more compliance graphs and a compliance score that can be used at least in risk assessment operations. The system can access a request to visualize target entity data and calculate a compliance score for the target entity. The system can access attribute data. The system can generate one or more compliance graphs and a compliance score using the attribute data. The system can compare the compliance score to a compliance threshold and transmit a message including the results of the comparison to a remote system for controlling access of the target entity to an interactive computing environment.

Abstract: Systems and methods for obfuscating sensitive data by aggregating the data based on data attributes are provided. Each sensitive data record contains at least one sensitive attribute. A data protection system generates a data transformation model based on the sensitive data records and transforms the sensitive data records using the data transformation model. The data protection system further compresses the sensitive database by grouping the sensitive data records into aggregation segments based on the transformed sensitive data records. The data protection system generates aggregated data by calculating statistics for the at least one sensitive attribute contained in the sensitive data records in each of the aggregation segments. The aggregated data can be made accessible by client computing devices that are unauthorized to access the sensitive data records.

Abstract: 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.

Abstract: Various aspects involve a monotonic recurrent neural network (MRNN) trained for risk assessment or other purposes. For instance, the MRNN is trained to compute a risk indicator from a predictor variable. Training the MRNN includes adjusting weights of nodes of the MRNN subject to a set of monotonicity constraints, wherein the set of monotonicity constraints causes output risk indicators computed by the RNN to be a monotonic function of input predictor variables. The trained monotonic RNN can be used to generate an output risk indicator for a target entity.

Abstract: Certain aspects involve evaluating modeling algorithms whose outputs can impact machine-implemented operating environments. For instance, a computing system generates, from a comparison of a set of estimated attribute values of an attribute to a set of validation attribute values of the attribute, a discretized evaluation dataset with data values in multiple categories. The computing system computes, for a modeling algorithm used to generate the estimated attribute values, an evaluation metric. The computing system provides a host computing system with access to the evaluation metric, one or more modeling outputs generated with the modeling algorithm, or both. Providing one or more of these outputs to the host computing system can facilitate modifying one or more machine-implemented operations.

Abstract: In some aspects, a gateway server can unlock or unfreeze access to data about a user by third parties without requiring the user to navigate completely away from a third-party website through which the user is executing an electronic transaction. The gateway server can receive a request to unlock or unfreeze data through the third-party website hosted by a third-party web server. The gateway server can output a user interface that is displayable simultaneously with the third-party website. Through the user interface, the gateway server can receive sign-in data such as log-in credentials of the user and consent to share data about the user with the third-party web server. The gateway server can output a command to unlock or unfreeze data about the user and to share the data with the third-party web server. Based on the shared data, the transaction can be completed at the third-party web server.

Abstract: Aspects and examples are disclosed for improving multi-factor authentication techniques to control access to secured electronic resources. In one example, a decisioning computer system evaluates, based on a passive-dimension decision process, an access request, received from a user device, for a secured electronic resource. The passive-dimension decision process can evaluate dimensions associated with the access request, such as identity or device characteristics, to determine whether the dimensions of the access request are outside of norms for the user. Based on the passive-dimension decision model, the decisioning computing device may communicate to the user device an access decision, the access decision describing one or more of an access authorization, a denial of access, or a supplemental authentication challenge.

Abstract: Methods described herein generate executable program code based on sequence codes, such that the executable program code is executable by a computer processor. According to such a method, a command is received to generate executable program code. Via a code-building interface, user input is received indicating an operation to be performed by the executable program code, a data type for to the executable program code, and a condition for applying the executable program code. A sequence code is constructed to represent the user input, where the sequence code includes a sequence of character sets. From the sequence code, a first character set corresponding to the operation, a second character set corresponding to the data type, and a third character set corresponding to the condition are extracted. Mapping data is applied to the first character set, the second character set, and the third character set to generate the executable program code.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Certain embodiments involve generating or optimizing a neural network for generating analytical or predictive outputs. The neural network can be generated using a relationship between various predictor variables and an outcome (e.g., a condition's presence or absence). The neural network can be used to determine a relationship between each of the predictor variables and a response variable. The neural network can be optimized by iteratively adjusting the neural network such that a monotonic relationship exists between each of the predictor variables and the response variable. The optimized neural network can be used both for accurately determining response variables using predictor variables and determining adverse action codes for the predictor variables, which indicate an effect or an amount of impact that a given predictor variable has on the response variable.