Abstract: A host computing system determines a wavelet transform that represents time-series values of predictor data samples. The host computing system applies the wavelet transform to the predictor data samples to generate wavelet predictor variable data comprising a first set and a second set of shift value input data for a first scale and a second scale. The host computing system computes a set of probabilities for a target event by applying a set of timing-prediction models to the first set and the second set of shift value input data. The host computing system determines an event prediction from the set of probabilities and modifies a host system operation based on the determined event prediction.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: Various aspects involve unified explainable machine learning for segmented risk assessment. For example, a computing device can determine, using a unified model built from segment models, a risk indicator for a target entity from predictor variables associated with the target entity. The target entity belongs to one of a plurality of entity segments each associated with a segment model of the segment models. The unified model is generated by: accessing training samples for the entity segments; training the segment models using respective training samples for the entity segments; constructing the unified risk prediction model by stacking the trained segment models; and training the unified risk prediction model using the training samples for the entity segments. The computing device can transmit, to a remote computing device, a responsive message including at least the risk indicator for use in controlling access of the target entity to an interactive computing environment.

Abstract: In some aspects, a computing system can obfuscate sensitive data based on data aggregation. A sensitive database containing sensitive data records can be joined with a grouping database containing a group identifier. The joining can be performed through a linking key that links a sensitive data record with a grouping data record in the grouping database. A grouping identifier can thus be obtained for each of the sensitive data record. The sensitive data records can be aggregated into aggregation groups based on their respective values of the group identifier. Statistics are calculated for the sensitive attributes of the sensitive data records in each aggregation group and are included in the aggregated data as the obfuscated version of the sensitive data. The aggregated data can be utilized to serve data queries from entities authorized or unauthorized to access the sensitive data.

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: In some aspects, a computing system can obfuscate sensitive data based on data aggregation. A sensitive database containing sensitive data records can be joined with a grouping database containing a group identifier. The joining can be performed through a linking key that links a sensitive data record with a grouping data record in the grouping database. A grouping identifier can thus be obtained for each of the sensitive data record. The sensitive data records can be aggregated into aggregation groups based on their respective values of the group identifier. Statistics are calculated for the sensitive attributes of the sensitive data records in each aggregation group and are included in the aggregated data as the obfuscated version of the sensitive data. The aggregated data can be utilized to serve data queries from entities authorized or unauthorized to access the sensitive data.

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: 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: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

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: Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

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.

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.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: Certain embodiments involve generating or optimizing a neural network for risk assessment. 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 both for accurately determining risk indicators or other outputs using predictor variables and for determining adverse action codes explaining the predictor variables' effect or an amount of impact on the risk indicator.

Abstract: Certain embodiments involve generating or optimizing a neural network for risk assessment. 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 risk indicator. 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 risk indicator. The optimized neural network can be used both for accurately determining risk indicators 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 risk indicator. The neural network can be used to generate adverse action codes upon which consumer behavior can be modified to improve the risk indicator score.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: Certain embodiments involve generating or optimizing a neural network for risk assessment. 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 risk indicator. 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 risk indicator. The optimized neural network can be used both for accurately determining risk indicators 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 risk indicator. The neural network can be used to generate adverse action codes upon which consumer behavior can be modified to improve the risk indicator score.

Abstract: In some aspects, a computing system can obfuscate sensitive data based on data aggregation. A sensitive database containing sensitive data records can be joined with a grouping database containing a group identifier. The joining can be performed through a linking key that links a sensitive data record with a grouping data record in the grouping database. A grouping identifier can thus be obtained for each of the sensitive data record. The sensitive data records can be aggregated into aggregation groups based on their respective values of the group identifier. Statistics are calculated for the sensitive attributes of the sensitive data records in each aggregation group and are included in the aggregated data as the obfuscated version of the sensitive data. The aggregated data can be utilized to serve data queries from entities authorized or unauthorized to access the sensitive data.