Machine Learning for Decision Support for Fusion Desktop
Description
To take into account various criteria such as analyst workloads, similarities in case narratives and targets, and regional factors to suggest the optimal routing and assignment for product requests.
Detailed example
Suggested assignment / routing for HSTF NCC Product Requests (e.g. to an analytic unit or specific analyst). Suggested approver assignments based on referenced agency data.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Improve efficiencies in routing request for HSTF Products to send to the analysts and/or analytic units most suitable for working the product. Reduce rework and streamline the approvals process by suggesting agency approvers based on automated review of the product's content and ensuring all appropriate agencies are consulted for manual review.
Controls / human review
ATO: Not reported; PIA: Not published