TECHNICAL FIELD

The disclosure generally relates to the field of secure digital documents, and more particularly relates to applying machine learning to detect attributes of secure digital documents that may lead to failures in secure documents.

BACKGROUND

Secure digital documents (also referred to herein as “secure documents”) are documents electronically provided to recipient users in requests generated by requesting users. A requesting user may send a request for a recipient user to perform tasks such as adding textual information to pre-designated input fields (e.g., biographical information such as a user's name, address, etc.), providing a signature, and providing attachments. After sending the request, the requesting user may track whether the recipient user has successfully completed the request. However, when the recipient user does not complete the request at all or completes the request late, the requesting user may not know the factors causing the recipient user to fail at or delay completing the request. Incomplete requests or delays can result in inefficient workflow, but it is difficult for the requesting user to determine a strategy for modifying the request to improve the likelihood of the recipient user completing the request.

SUMMARY

Systems and methods are disclosed herein for determining feature modification activities to improve the likelihood of request completion by the recipient user. In an embodiment, a secure document service detects that a requesting user is transmitting a request to a recipient user to perform one or more tasks with respect to a secure document. After detecting the transmission, the secure document service extracts a plurality of features associated with the secure document. The features may include attributes associated with the secure document, the tasks to be performed, and/or user characteristics associated with the requester user or the recipient user. The secure document service inputs the extracted features to a machine learning model that is trained based on features of historical secure documents and completion data associated with the historical secure documents. Based on the extracted features, the machine learning model outputs probabilities corresponding to a potential failures of the request. That is, the machine learning model determines features that may deter the recipient user from completing the request. For example, the machine learning model may determine a probability of the recipient user failing to complete the request because of a difficult or time consuming task, a probability of the recipient user failing to complete the request because the request was transmitted outside of convenient hours (e.g., on a Friday afternoon), or a probability of the recipient user failing to complete the request because of a number of tasks to complete.

The secure document service generates feature modification activities based on the output probabilities from the machine learning model, where the feature modification activities are configured to improve a likelihood of the recipient user completing the request. For example, if there is a high probability of a recipient user failing to complete the request because of a task of providing an attachment, the secure document service may determine that removing the task from the request would improve the likelihood of the recipient user competing the request. The feature modification activities are provided to at least one of the requesting user and the recipient user. For example, the secure document service may send a recommendation to the requesting user to remove the task from the request or may automatically increase a frequency of reminders to send to the recipient user.

