Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts to determine predicted recovery rates/scores for claims, determine priority scores for the claims, and prioritizing the claims based on the same, and updating a user interface based at least in part on the prioritization of the same.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for generating, training, and utilizing a digital signal processor (DSP) to evaluate graph data that may include irregular grid graph data. An example DSP that may be generated, trained, and used may include a set of hidden layers, wherein each hidden layer of the set of hidden layers comprises a set of heterogeneous kernels (HKs), and wherein each HK of the set of HKs includes a corresponding set of filters selected from the constructed set of filters and associated with one or more initial Laplacian operators and corresponding initial filter parameters.

Abstract: There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes generating, by a processor, utilizing a risk determination machine learning model and based at least in part on one or more hidden features of the first predictive entity, a the predicted risk measure, and performing one or more prediction-based actions based at least in part on the predicted risk measure.

Abstract: Embodiments of the invention provide apparatuses, systems, and methods with the ability to programmatically capture different types of data and to determine whether the data satisfies one or more thresholds indicative of one or more triggering events, and responsive thereto, to automatically initiate a communication between a user and a positive user contact.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating an inferred document representation for a multi-section document using a machine learning model.

Abstract: Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts to determine predicted recovery rates/scores for claims, determine priority scores for the claims, and prioritizing the claims based on the same, and updating a user interface based at least in part on the prioritization of the same.

Abstract: Methods, apparatuses, systems, computing entities, and/or the like are provided. An example method may include receiving a data object comprising feature metadata and flag metadata generated by at least a software black-box machine learning model via processing the feature metadata associated with the data object; selecting a subset of training data objects from a plurality of training data objects associated with the software black-box machine learning model based at least in part on the feature metadata by mapping the data object into a multi-dimensional mapping space comprising mappings of the plurality of training data objects; determining a subset of note metadata corresponding to the subset of training data objects; generating summary metadata for the data object based at least in part on a plurality of word scores associated with the subset of note metadata; and causing rendering of the summary metadata on a user computing entity.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts to determine predicted recovery rates/scores for claims, determine priority scores for the claims, and prioritizing the claims based on the same, and updating a user interface based at least in part on the prioritization of the same.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: There is a need for more effective and efficient data prioritization with respect to predictive input entities across predictive input channels. This need can be addressed by, for example, techniques for prospective prioritization that utilize supervised machine learning models. In one example, a method includes determining a prospective priority score for each predictive input entity of a group of predictive input entities based on a predictive input channel for the predictive input entity and performing prospective prioritization of the group of predictive input entities based on each prospective priority score for a predictive input entity.

Abstract: Embodiments of the invention provide apparatuses, systems, and methods with the ability to programmatically capture different types of data and to determine whether the data satisfies one or more thresholds indicative of one or more triggering events, and responsive thereto, to automatically initiate a communication between a user and a positive user contact.

Abstract: There is a need for more effective and efficient predictive data analysis solutions that utilize categorical input data objects. This need can be addressed by, for example, solutions for performing predictive inference using a categorical inference machine learning engine. In one example, a method includes receiving categorical input data objects, generating, based on each particular categorical input data object and using embedding layers, embedded feature representations for the particular categorical input data object; generating, based on each particular embedded feature representation and using initial capsule layers; initial instantiation parameters for the corresponding categorical data object; generating, based on each initial instantiation parameter and using subsequent capsule layers, inferred instantiation parameters for categorical input data objects; and generating predictions based at least in part on the inferred instantiation parameters.

Abstract: Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Abstract: Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Abstract: There is a need for solutions that predictive data analytics with improved training efficiency and/or accuracy. This need can be addressed by, for example, processing an original feature entry to generate a group of low-order feature values, including performing a first number of iterations of a feature engineering transformation to generate a group of engineered feature values and determining the group of low-ordered feature values based on a number of feature values from the group of engineered feature values; processing the original feature entry to generate a group of high-order feature values; merging the group of low-order feature values and the group of high-order feature values to generate a processed feature entry corresponding to the original feature entry; and providing the processed feature entry as an input to a prediction unit.