Abstract: Certain aspects involve updating data structures to indicate relationships between attribute trends and response variables used for training automated modeling systems. For example, a data structure stores data for training an automated modeling algorithm. The training data includes attribute values for multiple entities over a time period. A computing system generates, for each entity, at least one trend attribute that is a function of a respective time series of attribute values. The computing system modifies the data structure to include the generated trend attributes and updates the training data to include trend attribute values for the trend attributes. The computing system trains the automated modeling algorithm with the trend attribute values from the data structure. In some aspects, trend attributes are generated by applying a frequency transform to a time series of attribute values and selecting, as trend attributes, some of the coefficients generated by the frequency transform.

Abstract: Certain aspects involve building segment-specific executable program code. In one example, a code-building service can execute segmentation logic that assigns different target entity records to different segments based on differences between sets of attribute values among the target entity records. The code-building service can select, for each segment, a set of data assets that is specific to the assigned segment and a set of source code portions that is specific to the selected data assets. The code-building service can order each set of the source code portions based on an identified modeling output type for the target entity records. The code-building service can generate, from the ordered source code portions, a set of program code referencing the selected subset of the data assets. For instance, the generated program code, if executed, can generate and transmit different modeling outputs for different target entity records.

Abstract: In some aspects, a model of a remote data source is defined. The model includes an entity representing a data segment from the remote data source. The entity has properties, which represent fields of a data structure in the remote data source and have data type descriptors indicating data types used by the fields. The model also includes an attribute specifying a function, which uses the properties as inputs. The entity is matched to exported data from the remote data source, and the exported data is used as input data to the attribute's function.

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: The present disclosure involves systems and methods for identity authentication across multiple institutions using a trusted mobile device as a proxy for a user login. In one example, the operations include identifying a request to trust a particular user associated with a first entity in a digital ID network. A set of personally identifiable information (PII) associated with the user is obtained via the first entity and an identity verification (IDV)/fraud risk analysis is performed. In response to satisfying the analysis, instructions are transmitted to the user to verify the identity via a mobile trust application on an associated mobile device. Upon verification, the mobile device is bound to the user within the digital ID network along with a digital ID associated with the particular user. The digital ID can be used by other entities registered within the digital ID network to authenticate the user.

Abstract: In some aspects, a computing system can determine a set of attributes based on analyzing input data using attribute templates written in a production-ready programming language. The computing system can generate attribute definitions for the set of attributes using the attribute templates and deploy the attribute definitions for the set of attributes to a production environment of a software program. The software program is written in a programming language compatible with the production-ready programming language. The computing system can monitor the performance of the set of attributes in the production environment of the software program and cause the attribute definitions of the plurality of attributes to be modified based on the monitoring.

Abstract: A telecommunications network server system provides a digital identifier to a user device. The digital identifier may include identification data corresponding to a user of the user device. In addition, the telecommunications network server system receives, from one or more third-party systems, requests to authenticate the user for an electronic transaction with the respective third-party system. The telecommunications network server system provides a unique electronic transaction code to each third-party system. Responsive to receiving from the user device one of the unique electronic transaction codes, the telecommunications network server system provides, to the respective third-party system, authentication of the user.

Abstract: In some aspects, a computing system can detect synthetic identities based on attributes associated with online entities. The computing system can generate entity links between primary entity objects and a secondary entity object. The secondary entity object identifies a target consumer as a secondary entity for accounts identified by the primary entity objects. Attributes of the secondary entity object and attributes of the primary entity objects can be calculated and compared to determine an inconsistency between a combination of a name attribute value and an address attribute value of the secondary entity object as compared to the primary entity objects. A fraud warning indicating that the target consumer is likely to be a synthetic identity can be generated based on the inconsistency and other attributes of the target consumer, such as an inquiries attribute, a terminations attribute, a duration attribute, and so on.

Abstract: Aspects and examples are disclosed for facilitating transactions with an interactive computing environment provided by a host system. In one example, a processing device receives a data network identifier or other identifier from an interactive computing environment. The data network identifier or other identifier can be associated with a mobile device conducting a transaction with the interactive computing environment. The processing device can also obtain a credential associated with the mobile device using the data network identifier. The processing device can additionally retrieve identification information from a credit file associated with the credential. The identification information can be useable for completing the transaction. The processing device can further transmit the identification information to a host server system executing the 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: 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: 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: Certain aspects involve providing automated performance monitoring of statistical models. For example, a processing device is used for performing a statistical analysis on information in an archive to extract historical data, scores, and attributes. The processing device calculates performance metrics based at least in part on the historical data, scores, and attributes. The processing device pre-calculates summary performance data based at least in part on the performance metrics. The summary performance data is stored in files with predefined layouts, which are stored in a non-transitory, computer-readable medium. Segmented data is presented from a file to a user through a graphical user interface (GUI). In some aspects, various reports of the segmented data are presented interactively by detecting a selection by the user of a segmentation and displaying the corresponding segmented data.

Abstract: Certain aspects involve models for generating code executed on data-processing platforms. One example involves receiving an electronic data-processing model, which generates an analytical output from input attributes weighted with respective modeling coefficients. A target data-processing platform is identified that requires bin ranges for the modeling coefficients and reason codes for the input attributes. Modeling code is generated that implements the electronic data-processing model with the bin ranges and the reason codes. The processor outputs executable code that implements the electronic data-processing model.

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: In some aspects, a computing system can receive a request for a processing operation that involves a query. The computing system can extract a query parameter indicating an entity or information type. The computing system can parse, using the query parameter, an obfuscated dataset that is generated by electronically transforming (i) first sensitive information into first obfuscated data using an obfuscation key and (ii) second sensitive information into second obfuscated data using the obfuscation key. The sensitive information is unreadable in the obfuscated data. The computing system can match the query parameter to the first obfuscated data and the second obfuscated data based on a relationship between the first obfuscated data and the second obfuscated data. The relationship is independent of a meaning of the first and second sensitive information. The computing system can performing the processing operation using the first and second obfuscated data.

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 generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

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: Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.