Abstract: A method for making binary predictions for a subject involves obtaining historical data for multiple subjects, the historical data including, for each subject, a feature set and a binary outcome, generating training data from the historical data, and training a predictive model using the training data to predict the outcomes based on the feature sets. The method further includes obtaining historical data including a feature set for a subject under consideration, and predicting a binary outcome for the subject under consideration, based on the feature set associated with the subject under consideration.

Abstract: A computer-implemented system and method for predicting rule-based compliance scenarios to implement rule-based topic determinations. A server computing device generates a compliance scenario prediction model by training a machine learning model for a topic with historical user data and cohort labels created by analyzing the scenarios in a completeness graph to predict a set of scenario cohorts that constitute a set of most probable compliance scenarios. The server computing device executes the scenario prediction model to process a user profile including data features associated with the topic to predict a scenario cohort and a compliance scenario corresponding to the predicted cohort for the user. The server computing device automatically infers one or more personalized responses to at least one question of the respective decision node based on the predicted compliance scenario.

Abstract: Aspects of the present disclosure provide techniques for explainable payroll calculations. Embodiments include receiving a request from a client for a rule-based calculation. Embodiments include receiving one or more user values related to the request. Embodiments include using a calculation graph to determine the rule-based calculation based on the one or more user values. The calculation graph may comprise at least one node that performs an operation using the one or more user values. Embodiments include identifying an explanation template associated with the at least one node. Embodiments include generating an explanation of the rule-based calculation based on the explanation template and the one or more user values. Embodiments include providing the rule-based calculation and the explanation of the rule-based calculation to the client in response to the request.

Abstract: Aspects of the present disclosure provide techniques for explainable payroll calculations. Embodiments include receiving a request from a client for a rule-based calculation. Embodiments include receiving one or more user values related to the request. Embodiments include using a calculation graph to determine the rule-based calculation based on the one or more user values. The calculation graph may comprise at least one node that performs an operation using the one or more user values. Embodiments include identifying an explanation template associated with the at least one node. Embodiments include generating an explanation of the rule-based calculation based on the explanation template and the one or more user values. Embodiments include providing the rule-based calculation and the explanation of the rule-based calculation to the client in response to the request.

Abstract: A server may receive an inquiry associated with an interaction between a customer and a customer support agent from a device associated with a customer support agent; enter the inquiry as an input to a contextual bandit model; select, using the contextual bandit model, a collection of articles from a plurality of pre-defined collections of articles based on the inquiry; cause, in response to the contextual bandit model selecting the collection of articles, at least one search result to be displayed on a user interface of the device associated with the customer support agent, wherein a search result includes at least a portion of at least one article of the collection of articles; cause text within the at least one search result to be highlighted; receive feedback on the collection of articles from the customer support agent; and update the contextual bandit model based on the feedback.

Abstract: A method to predict a delay involves receiving an open invoice sent by a company to a customer, extracting, using a programmable interface, a set of company data, a set of customer data, and a set of invoice data from a management application (MA) and the open invoice; refining, using a set of algorithms, the set of company data, the set of customer data, and the set of invoice data into a set of invoice attributes, a set of company features, and a set of customer features; predicting a delay in processing the open invoice using a trained model analyzing the set of company features and the set of customer features; updating, at an expiration of a predetermined timespan, the MA to add the delay to a due date of the open invoice by including the set of company features; and updating, at the expiration of the predetermined timespan, the MA to add the delay in processing the open invoice by including the set of customer features.

Abstract: A method to predict a delay involves receiving an open invoice sent by a company to a customer, extracting, using a programmable interface, a set of company data, a set of customer data, and a set of invoice data from a management application (MA) and the open invoice; refining, using a set of algorithms, the set of company data, the set of customer data, and the set of invoice data into a set of invoice attributes, a set of company features, and a set of customer features; predicting a delay in processing the open invoice using a trained model analyzing the set of company features and the set of customer features; updating, at an expiration of a predetermined timespan, the MA to add the delay to a due date of the open invoice by including the set of company features; and updating, at the expiration of the predetermined timespan, the MA to add the delay in processing the open invoice by including the set of customer features.