Cargo Security Assessment Model
Description
This use case addresses the challenge of efficiently identifying and mitigating risks associated with cargo shipments entering the United States. With the high volume of shipments processed daily at ports of entry, it is essential to detect potentially high-risk shipments, such as those that may pose security threats, without causing delays to legitimate trade and commerce. This use case uses advanced data analytics and machine learning to enhance the ability to evaluate and prioritize shipments for further review, ensuring that flagged cargo is inspected appropriately while maintaining efficient cargo processing operations.
Detailed example
High risk model results are returned to users as a system rule hit. These rule hits are viewable in the associated system results window. From this window, CBP operational personnel review and assess result for next action, including possible shipment examination.
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
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
AI/ML Models identify high risk shipments to aid CBP officers in detecting narcotics smuggling threats, identifying candidate shipments for review and referral for inspection at CBP Ports of Entry (POEs).
Controls / human review
ATO: Yes; PIA: https://www.dhs.gov/publication/automated-targeting-system-ats-update https://www.govinfo.gov/content/pkg/FR-2012-05-22/html/2012-12396.htm
Data needed
This model leverages data provided by carriers within the Automated Commercial Environment (ACE), as well as transformations of that data within the Automated Targeting System (ATS).