Revenue Authority AI Intelligence Platform — East African Tax Administration

A national revenue authority in East Africa was contending with a persistent tax gap estimated at 35% of potential revenue — a figure that had remained stubbornly high despite successive compliance campaigns. Audit case selection was manual and based on auditor intuition rather than systematic data analysis, resulting in low audit yields and inefficient deployment of limited audit resources. The authority held access to significant third-party data — banking records, customs declarations, business registry information — but lacked the analytical infrastructure to integrate and interrogate this data at scale. Filing compliance rates were declining year-on-year, and the authority had no reliable mechanism to identify non-filers or target compliance interventions with precision.

Gloseg Technologies deployed its National Revenue Intelligence Platform (NRIP) over an 18-month implementation programme. The engagement commenced with a data architecture assessment and the design of integration pipelines connecting third-party data sources — including banking transaction records, customs declarations, business registry data, and land registry information — with the authority's existing taxpayer filing database. Machine learning models were developed and trained for audit case selection, taxpayer risk profiling, and compliance monitoring, with model performance validated against historical audit outcomes before deployment. An executive analytics dashboard was deployed providing real-time visibility into compliance performance across taxpayer segments. A structured capacity building programme equipped revenue authority analysts and auditors to operate and interpret the platform's outputs.

Within 18 months of deployment, the revenue authority recorded an 18% increase in revenue collection attributable to improved audit efficiency and compliance interventions. AI-powered audit case selection delivered a 340% improvement in audit yield compared to the previous manual selection process, with 87% of AI-selected audits resulting in additional assessments. The integration of third-party data enabled the identification of over 12,000 previously unregistered businesses and non-filers. Revenue forecasting models provided the finance ministry with accurate monthly projections, improving budget planning reliability. The authority's tax gap estimate declined from 35% to 24% within two years of deployment.