Customs officers at Apapa Port squint at a printed invoice: 5 2022 Mercedes-Benz automobiles, declared value $2000 each. Two seconds later, a ping notification drops on their computer screen, and an AI-powered software compares the current price, shipping route, and the importer’s past declarations. The automobile’s ideal value is $12000 each. Someone is quietly trying to rob Nigeria of valuable taxes.
Far away in the United States, a machine learning program within the Internal Revenue Service (IRS) processes a late-night e-filed return claiming $8,000 in refund amount, yet the employer payroll feeds show the filer worked just three weeks during the previous year. The system cancels the refund, forwards it for examination, and saves the American government money. Many revenue-generating agencies are leveraging technology to replace the tedious grunt work of manual auditing due to these machines’ very low margin of error.
Globally, billions of dollars are lost annually to illegal financial transactions like corporate and private tax evasion. For Africa alone, the loss is estimated at 88 billion, which is approximately half of the continent’s total health budget. Finances that can be used to staff oncology wards, build roads, or keep health workers on payroll disappear because manual auditing and accounting can’t keep up with the speed and novelty of illicit financial flow arising from taxation.
Why Artificial Intelligence is The Key
Machine-learning models are excellent at detecting patterns across noisy data. Connect them to a revenue agency’s structure, and these models would begin risk scoring in real-time. Typically, they can carry out things like:
Anomaly detection: In the context of the examples given above, various documents citing the cost of 2022 Mercedes Benz with the same specifications a specific route will be analyzed by the system and anything abnormal gets flagged prior to clearance.
Graph analytics: Usually this happens with more sophisticated illicit financial flows involving shell companies with spurious unexplainable payment flows. The machine flags strange surges of payments flurrying between different jurisdictions at unusual times.
Most importantly, these systems self-enhance themselves, making them in sync and ready to detect the latest fraud schemes. They receive new fraud patterns daily, learning suspicious versus normal activity, and adjusting accordingly. Because of this, audit teams can shift from manual, randomized checks to more precise predictions, relying on sophisticated algorithms rather than instincts.
The evidence supporting the need to adopt Artificial Intelligence for fraud detection is starting to emerge. In a more advanced ecosystem like the United States, the results are more compelling. The US Treasury and IRS credited its enhanced AI fraud-detection process for saving the treasury close to 4 billion dollars by identifying 1.9 million spurious requests in 2024.
Despite all these positives, machines and algorithms can go too far. Hence, there are valid concerns about the data privacy issues associated with Artificial Intelligence software. They have a point: revenue agencies wield immense coercive power; AI can widen it. Three guardrails keep that power in check:
1. Data minimization – models collect only what’s necessary; aggregate when possible.
2. Explainability – if a return or shipment is flagged, the model must articulate why in plain English.
3. Proportionality & appeal – high-risk scores transactions trigger deeper human review, not automatic penalties. Honest errors deserve quick resolution.
So, what is a practical action plan for agencies? A step-by-step guide will start with digitization. This includes e-filing registers, creating API links to ports, banks, and payment processors. As far as recognizing patterns is concerned, paper is the enemy. Once digitization is in place, step two is the implementation of federated learning so models can learn from data across borders without centralizing personal information. Nigeria and the U.S. could pilot a shared anomaly model on synthetic customs data. Creating public-private sandboxes where private operators can stress-test models with anonymised trade flows is equally important.
This way, innovators get compliance insights and agencies get fresh signals. Once all of this is done, it is important to measure the right metrics – not only revenue recovered, but audit hit rates, false-positive ratios, and citizen trust scores. If taxpayers respect the system, compliance soars.
True, machine learning algorithms alone won’t improve governance, but they do provide operators the tools needed to drive efficient systems. Frauds are nipped in the bud when code and policy shake hands. We have seen this with fintech and now it can be replicated in agency rollouts. Repeatable patterns exist. The move is ours to make.
About the Writer:
Oladepo is an expert on the subject of Artificial Intelligence and Financial Technology. His interest lies in the intersection of AI, FinTech, and business analytics.
