Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A method implements hybrid artificial intelligence generated actionable recommendations. The method includes processing an event to identify an action of an event action set. The event includes an event value. The method further includes processing the event action set to generate an objective value, corresponding to the action, and a probability, corresponding to the action, and to form a model action set from the event action set. The method further includes filtering the model action set using action rule data and rule user data to generate a filtered action set. The method further includes processing, using the objective value and the probability, the filtered action set with an optimization controller to generate suggested action sets from which a selected action set is selected. The selected action set corresponds to a combined action value that satisfies the event value.

Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A method optimizes questions to retain engagement. The method includes generating, using a machine learning model, a churn risk from user interaction data. The method includes selecting, when the churn risk satisfies a threshold, a field, from multiple fields, using multiple prediction confidences corresponding to multiple prediction values generated for the multiple fields. The method includes obtaining a prediction value for the field and obtaining a question, corresponding to the field, using the prediction value. The method includes presenting the question and receiving a user input in response to the question.

Abstract: Systems and methods for predicting one or more field values using machine learning in a knowledge engineering (KE) data model are disclosed. An example method may include identifying a first field in the KE data model which lacks a value and for which one or more machine learning models are defined, the first field being associated with one or more dependent field, determining that each dependent field of the first field has a corresponding value in the KE data model, executing each of the one or more machine learning models to predict one or more values for the first field, selecting one of the one or more predicted values as the representative value of the first field, identifying one or more further fields in the KE data model for which the first field is a dependent field, none of the one or more further fields defining any machine learning models, and calculating values for one or more further fields based at least in part on the representative value of the first field.

Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A method involves identifying account data of an entity for a present time period, where the account data includes more than one first data value, creating a comparison group for the entity. The comparison group includes more than one second data value, the account data of the entity includes the second data values, and the second data values originate from a prior time period. The method further involves selecting, from the first data values, a subset of the first data values, selecting, from the second data values, a subset of the second data values, identifying, by accessing a library including anomaly detection methods, an anomalous value within the subset of the first data values by comparing the subset of the first data values with the subset of the second data values, selecting an action in response to identifying the anomalous value within the subset of first data values, and initiating the action.

Abstract: A method for transaction verification scoring includes obtaining, from a distributed computing system of distributed computing systems distributed throughout a computing network, a transaction description describing a financial transaction with a vendor, obtaining, from the distributed computing systems, transaction records of potential corroborators, and reconciling, with the financial transaction, the transaction records to obtain at least one matching transaction record of at least one corroborator, in the potential corroborators, to the financial transaction. The method further includes scoring the transaction description based on a function of each of the at least one corroborator to the financial transaction to obtain a verification score, and presenting, on a display device, a recommendation of the vendor to a consumer based on the verification score.