Madagascar Customs Sees 68% Revenue Jump After AI Deployment, Becomes IMF Pilot

Madagascar's customs administration has been designated a pilot project by the International Monetary Fund for the integration of AI into customs services, after AI-driven tools contributed to a 68% increase in customs revenues in January 2025 compared with the same month a year earlier.

IMF specialists Victor Budeau and François Chastel led an intensive training mission at Madagascar's customs authority beginning April 24, 2025 and concluding May 7, 2025, focused on embedding AI more deeply into customs procedures. The administration is currently deploying three AI-driven systems: automatic image analysis technology known as RESNET for scanning cargo, Smart Scanning tools for accelerated inspections, and an Enhanced Risk Assessment (ERA) system that flags high-risk shipments for closer review.

Ernest Zafivanona Lainkana, Director General of Customs, said centralizing data into a unified database would be essential to maximizing the AI systems' potential, pointing to data fragmentation as one of the administration's remaining technical hurdles. The IMF's decision to designate Madagascar as an African pilot for AI-in-customs work suggests that the country's experience could inform a continent-wide approach to modernizing revenue collection — a priority for many African governments seeking to close persistent tax and customs revenue gaps without raising rates.

The initiative fits into a broader IMF push, outlined in its own technical research, on how AI can simultaneously improve efficiency for tax authorities and reduce compliance friction for taxpayers. For Madagascar, a country where customs revenue represents a significant share of government income, the reported revenue gains offer an early, concrete case study of AI's fiscal payoff in a lower-income African context.

Madagascar's customs authority has set a 2029 target to extend the AI tools to additional control sectors, strengthen underlying digital infrastructure, and share its implementation experience with other African revenue authorities as the technology matures.

For policymakers, the significance is not the announcement alone but the institutional work that follows: clear mandates, accountable implementation and enough technical capacity to measure whether the policy changes outcomes.

The next stage is execution. Readers should look for specific milestones, transparent reporting and evidence that the initiative is improving access, capability or outcomes rather than simply adding another AI announcement to the policy cycle.

The development is part of a wider shift in how African institutions and businesses are moving from AI experimentation towards practical capability. Its importance should be assessed against the problem it is meant to solve, the people who will use it and the evidence that it is delivering a measurable benefit. Announcements create attention, but adoption requires trust, skills, data and a clear path from pilot to routine use.

There is also a useful distinction between ambition and implementation. A public commitment, commercial launch or partnership can open a route forward without guaranteeing an outcome. Follow-up reporting should therefore track delivery, participation, accountability and what changes for users on the ground. That approach gives readers more than a summary of the source: it explains the decision, the opportunity and the risks that will shape whether this development matters over time.