Abstract: A device may receive invoice data related to multiple invoices, requisition data related to multiple requisitions, or project data related to multiple projects. The device may process the data using a feature extraction engine to identify features of the data. The device may process the data using a transformation engine to reduce a size of the data. The device may process the data using a set of machine learning models. The device may generate a set of recommendations related to at least one of: categorizing each of the multiple invoices, each of the multiple requisitions, or each of the multiple projects into one or more of multiple categories, identifying a set of possible suppliers for each of the multiple requisitions or each of the multiple projects, or identifying a set of similar projects for each of the multiple projects. The device may perform one or more actions.

Abstract: A device may receive, from multiple systems, data related to an individual. The device may anonymize, after receiving the data and using an anonymization technique, information included in the data that identifies the individual. The device may apply a formatting to the data after anonymizing the information that identifies the individual. The device may identify, after applying the formatting to the data, historical data related to the individual, to a provider associated with a claim for care, or to historical claims, and population data associated with demographics of the individual. The device may process, in association with identifying the historical data and the population data, the data using a machine learning model. The machine learning model may be associated with generating a prediction related to the individual or the care provided to the individual. The device may perform one or more actions based on the prediction.