ZRA AI Unit — Zambia Revenue Authority's Machine-Learning Customs Risk Model

Since around 2023, the Zambia Revenue Authority (ZRA) has operated an in-house "AI Unit" tasked with modernising tax and customs administration through machine learning. In partnership with the University of California, Berkeley's Center for Effective Global Action (CEGA), the unit adapted a Random Forest model — previously piloted on Paraguayan customs data — to flag high-risk import declarations for physical inspection, aiming to address an estimated 47–56% customs tax gap across roughly half a million commercial shipments processed annually.

The customs risk-scoring pilot began around November 2025 and, according to CEGA, is being evaluated through a randomised design; quantitative results on detection accuracy or revenue recovered specifically attributable to the model had not yet been published as of the pilot's start. The AI Unit also runs adjacent tools, including a taxpayer chatbot, an HS-code lookup assistant, and automated compliance "nudges."

ZRA and Zambia's Ministry of Technology and Science have publicly stated that AI-driven modernisation across the revenue authority's operations contributed to a 60% increase in revenue collection over two years. That figure describes the broader AI programme rather than being isolated to the customs Random Forest model, and it has not been independently audited, so it should be read as an institutional claim rather than a verified causal result of this specific practice.

Read the full analysis: https://www.mots.gov.zm/?p=3787