ZRA AI Unit — Zambia Revenue Authority machine-learning platform for tax-fraud detection and revenue-gap identification

The Zambia Revenue Authority (ZRA) set up a dedicated Artificial Intelligence Unit at its Lusaka headquarters, staffed by Zambian data scientists, to identify tax gaps and revenue leakages across the national tax base. The unit applies machine-learning models to importer and exporter transaction data in real time, automatically detecting suspicious patterns and fraud faster than manual review. Core tasks include taxpayer risk scoring, automated chasing of nil-filers, cross-matching PAYE data against employer submissions, and flagging anomalous customs declarations.

To increase ZRA's capacity to detect tax fraud and close revenue gaps by replacing slow manual review with automated, real-time analysis of import/export and payroll data.

Data-analytics tools reportedly process around 1,000 tax records in 2 milliseconds, compared with seven or more days for manual review. A parallel research evaluation led by the Center for Effective Global Action (CEGA) at UC Berkeley is testing a Random Forest algorithm adapted from a Paraguay customs-fraud pilot for application in Zambia.

In June 2024, Zambia's Minister of Technology and Science, Felix Mutati, stated that AI had increased ZRA's capacity to collect revenue by 60% over the preceding two years. The claim was reported by Xinhua, Open Zambia and Copperbelt Katanga Mining, but refers to collection capacity rather than audited net revenue receipts, and no independent technical audit of the AI models' accuracy or fairness has been published.

Despite the lack of independent audit, the ministerially confirmed gain and multi-source press corroboration make this one of the most significant documented AI results in tax administration in sub-Saharan Africa outside South Africa.