Detecting incorrect field values of user submissions using machine learning techniques

Abstract

Techniques are disclosed relating to detecting one or more incorrect fields in user submissions, using machine learning techniques. A corrective system may access information for a plurality of fields of a tracking data structure for a user submission. In some embodiments, the corrective system predicts correct values for multiple fields of the tracking data structure using a plurality of respective different machine learning classifier modules. In some embodiments, the classifier modules use different sets of the plurality of fields as inputs and the multiple fields include a priority of the user submission and an assignee for the user submission. In some embodiments, in response to determining that at least one of the predicted correct values does not match a current value for a corresponding field of the tracking data structure, the computing system stores information indicating the mismatch and may automatically correct one or more fields.