Illicit Trade
Description
The use case is designed to improve the identification and prioritization of high-risk inbound cargo shipments that may violate trade regulations. Using advanced AI and machine learning models, the system enhances risk assessment processes, helping CBP personnel more effectively detect suspicious shipments and potential compliance issues. By analyzing historical data, risk attributes, and employing predictive modeling, the AI supports CBP in streamlining enforcement actions and improving the accuracy of targeting shipments for additional review and screening. This approach helps optimize resource allocation and strengthens CBP's ability to enforce trade regulations efficiently.
Detailed example
The model results are sent to the Automated Targeting System for review and assessment by operational personnel, who may conduct additional screening if necessary.
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
The model identifies high-risk shipments to support CBP personnel in managing their workload associated with detecting threats and selecting candidate shipments for review and additional screening.
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).