Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

Abstract: A service provider computing system includes memory having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to obtain a first set of complaint logs corresponding to one or more user complaints, each complaint log including a resolution comments field providing a textual representation of a resolution for a corresponding complaint log, parse the resolution comments field of each complaint log of the first set of complaint logs to identify one or more key terms, and execute a machine-learning predictive model using the one or more key terms to generate, for each complaint log, a prediction indicating whether a corresponding complaint should have compensation, the machine-learning predictive model being a supervised machine learning model configured to accept the parsed resolution comments field for the first set of complaint logs as inputs and to output the prediction for each complaint log.

Abstract: A service provider computing system includes memory having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to obtain a first set of complaint logs corresponding to one or more user complaints, each complaint log including a resolution comments field providing a textual representation of a resolution for a corresponding complaint log, parse the resolution comments field of each complaint log of the first set of complaint logs to identify one or more key terms, and execute a machine-learning predictive model using the one or more key terms to generate, for each complaint log, a prediction indicating whether a corresponding complaint should have compensation, the machine-learning predictive model being a supervised machine learning model configured to accept the parsed resolution comments field for the first set of complaint logs as inputs and to output the prediction for each complaint log.

Abstract: Disclosed are techniques for using machine learning models to more reliably predict likelihoods of application failure. A model is trained to identify and display events that may cause high severity application failures. Logistic regression may be used to fit the model such that application features are mapped to a high severity event flag. Significant features of applications that relate to the high severity flag may be selected using stepwise regression. The identified applications may be displayed on a graphical user interface for review and reprioritization. Information may be ranked and displayed according to multiple different ranking criteria, such as one ranking generated by a first model, and another determined by one or more users. The multiple ranking criteria may be used to inform steps taken, and/or to retrain or tune the parameters of the model for subsequent predictions or classifications.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for managing open issues. An example method includes obtaining a dashboard access request for a portion of open issues managed by the issue management system, the issue management system storing variables associated with each open issue of the open issues; obtaining a dashboard data package based on scores for the portion of open issues, the scores indicating a severity level of the open issues of the portion of open issues and being based on the variables that are stored in the issue management system; and performing an action set using the dashboard data package to manage the portion of open issues.

Abstract: A computing system receives issue data for a plurality of issues from a risk management system. The issue data for each respective issue of the plurality of issues includes an issue identifier and a due date for resolution of the respective issue. The computing system generates due date revision risk scores for the plurality of issues. A due date revision risk score for the respective issue indicates a predicted probability that the due date for resolution of the respective issue will need revision within a fixed period of time. The computing system generates a warning message for at least the respective issue of the plurality of issues based on the due date revision risk score for the respective issue being greater than a threshold. The computing system revises parameters of the at least one machine learning model based on due date revision outcomes associated with the plurality of issues.

Abstract: A computing system receives issue data for a plurality of issues from a risk management system. The issue data for each respective issue of the plurality of issues includes an issue identifier. The computing system generates issue validation risk scores for the plurality of issues. An issue validation risk score for the respective issue indicates a predicted probability that a resolution of the respective issue will fail validation within a fixed period of time. The computing system generates a warning message for at least the respective issue of the plurality of issues based on the issue validation risk score for the respective issue being greater than a threshold. The computing system revises parameters of the at least one machine learning model based on validation failure outcomes associated with the plurality of issues.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automated prototyping of a topic model. An example method includes a data manipulation engine ingesting and pre-processing source data from a set of data sources, a feature extraction engine that thereafter transforms the pre-processed data into a set of numeric representations of the pre-processed data, and an autonomous model generator that automatically generates a trained topic model using the set of numeric representations. Embodiments further enable visualization of topic model output, which permits a user to easily consume and utilize information from a topic model for any number of purposes.

Abstract: Disclosed are techniques for using machine learning models to more reliably predict likelihoods of application failure. A model is trained to identify and display events that may cause high severity application failures. Logistic regression may be used to fit the model such that application features are mapped to a high severity event flag. Significant features of applications that relate to the high severity flag may be selected using stepwise regression. The identified applications may be displayed on a graphical user interface for review and reprioritization. Information may be ranked and displayed according to multiple different ranking criteria, such as one ranking generated by a first model, and another determined by one or more users. The multiple ranking criteria may be used to inform steps taken, and/or to retrain or tune the parameters of the model for subsequent predictions or classifications.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes: receiving a historical incident dataset; receiving a new incident dataset including information regarding a set of new incidents; generating a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset; determining that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset; and in response to determining that the severity classification mismatch exists, prompting further investigation of the severity classification mismatch through outputting an indication of the severity classification mismatch.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for managing open issues. An example method includes obtaining a dashboard access request for a portion of open issues managed by the issue management system, the issue management system storing variables associated with each open issue of the open issues; obtaining a dashboard data package based on scores for the portion of open issues, the scores indicating a severity level of the open issues of the portion of open issues and being based on the variables that are stored in the issue management system; and performing an action set using the dashboard data package to manage the portion of open issues.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for managing open issues. An example method includes obtaining a dashboard access request for a portion of open issues managed by the issue management system, the issue management system storing variables associated with each open issue of the open issues; obtaining a dashboard data package based on scores for the portion of open issues, the scores indicating a severity level of the open issues of the portion of open issues and being based on the variables that are stored in the issue management system; and performing an action set using the dashboard data package to manage the portion of open issues.

Abstract: A service provider computing system includes memory having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to obtain a first set of complaint logs corresponding to one or more user complaints, each complaint log including a resolution comments field providing a textual representation of a resolution for a corresponding complaint log, parse the resolution comments field of each complaint log of the first set of complaint logs to identify one or more key terms, and execute a machine-learning predictive model using the one or more key terms to generate, for each complaint log, a prediction indicating whether a corresponding complaint should have compensation, the machine-learning predictive model being a supervised machine learning model configured to accept the parsed resolution comments field for the first set of complaint logs as inputs and to output the prediction for each complaint log.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes: receiving a historical incident dataset; receiving a new incident dataset including information regarding a set of new incidents; generating a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset; determining that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset; and in response to determining that the severity classification mismatch exists, prompting further investigation of the severity classification mismatch through outputting an indication of the severity classification mismatch.

Abstract: A service provider computing system includes memory having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to obtain a first set of complaint logs corresponding to one or more user complaints, each complaint log including a resolution comments field providing a textual representation of a resolution for a corresponding complaint log, parse the resolution comments field of each complaint log of the first set of complaint logs to identify one or more key terms, and execute a machine-learning predictive model using the one or more key terms to generate, for each complaint log, a prediction indicating whether a corresponding complaint should have compensation, the machine-learning predictive model being a supervised machine learning model configured to accept the parsed resolution comments field for the first set of complaint logs as inputs and to output the prediction for each complaint log.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes receiving, by communications circuitry, a historical incident dataset, the historical incident dataset including information regarding a set of historical incidents and an assigned severity classification for each historical incident in the set of historical incidents. The example method further includes training, by a model generator and using the historical incident dataset, a machine learning model to classify incident severity. The example method further includes receiving, by the communications circuitry, a new incident dataset, generating, by a prediction engine and using the trained machine learning model and the new incident dataset, a set of predicted severity classifications for the set of new incidents, and outputting, by the communications circuitry, the predicted set of severity classifications for the set of new incidents.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically deriving data transformation criteria. An example method includes receiving, by communications circuitry, a source dataset and a target dataset and identifying, by a model generator, a target variable. The example method further includes training, by the model generator, a decision tree for the target variable using the source dataset and the target dataset such that the trained decision tree can predict a value for the target variable from new source data. The example method further includes deriving, by a derivation engine, a set of parameters and pseudocode for producing the target variable from the source dataset.