The Zambia Revenue Authority's dedicated AI Unit uses machine learning to detect tax fraud, analyse importer/exporter data, and identify revenue leakages. By June 2024, the Ministry of Technology confirmed AI had increased ZRA's revenue-collection capacity by 60% over two years.
60 %
Increase in ZRA revenue-collection capacity attributed to AI (2022-2024 (two years to June 2024))
1,000 records per 2 milliseconds
Tax records processed by the analytics tools (as of 2024)
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.
Objectives
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.
Activities
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.
Results
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.
Conclusions
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.
Implementation
Indicative cost
Medium (€50k–€500k) — No official budget figures are published. A dedicated in-house AI unit with real-time transaction-monitoring infrastructure at a national revenue authority implies a moderate, ongoing government IT investment; classified 'medium' as a conservative estimate pending disclosed costs.
Time to results
Medium (1–3 years) — The unit's reported gains cover a two-year build-up to June 2024, consistent with a multi-year (1-3 year) establishment and scale-up timeline rather than a short pilot or a decade-long programme.
Staffing & skills
In-house Zambian data scientists based at ZRA headquarters in Lusaka, External research partnership with CEGA (Center for Effective Global Action) at UC Berkeley for independent model evaluation
Conditions for success
Access to real-time importer/exporter and PAYE transaction data for model training and scoring
Ministerial-level political backing and public communication of results
Adaptation of an existing proven approach (Random Forest algorithm from a Paraguay customs-fraud pilot) rather than building from scratch
Common failure modes
No independent technical audit of model accuracy or fairness has been published
The headline 60% figure measures self-reported collection capacity, not independently verified net revenue receipts
Risk of overstating results without transparent methodology disclosure
Where it fits
Governance type
national revenue authority (central government agency)
Scale
national
Income level
lower-middle income
Data sources
Where this practice's information was retrieved from, and when.