Trade Entity Risk Model

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Presumed high-impact, determined notSource: b) Presumed high-impact, but determined not high impact

Impact justification

The Trade Entity Risk model/tool enhances cargo predictive threat models by providing a comprehensive risk profile that aggregates historical trade entity transactions, trading partner relationships, reviews, examinations, and violations (within CBP data holdings) to create quantifiable risk measures for all trade entities. The AI model serves as an input to larger AI/ML cargo risk targeting models to better assess cargo threats and inform focus areas for trade targeting. Its outputs are not directly shared with users or operators and the output does not serve as a principal basis for decision or actions.

Start date

2025-07-15 (day precision)Source: 2025-07-15T00:00:00

Withheld from public reporting

Not reported

Mission

Topic area

Law Enforcement

Operational functions

Forecasting and prediction, Risk scoring and triage Derived by keyword rules; see methodology

Problem the AI is intended to solve

The need to continuously assess and identify trade entity risk to help better assess cargo threats.

Expected benefits

The Trade Entity Risk model enhances existing predictive threat models by compiling a risk profile that includes historical transaction data, relationships with trading partners, and relevant compliance information. This aggregated data helps create measurable risk indicators for trade entities.

System outputs

The calculated risk measures produced by the Trade Entity Risk model can be integrated into broader AI and machine learning systems to improve the evaluation of cargo-related threats. This output supports the standardization of trade entity risk, facilitating better data development for future predictive models.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Automated Targeting System (ATS)

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

ManTech

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Yes

Privacy Impact Assessment

www.dhs.gov ↗

Authorization to Operate

Yes

Demographic features

Not reported

Training and evaluation data

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).

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported