The Minister of Finance, Dr Cassiel Ato Forson, delivering the 2025 Mid-Year Budget Review in Parliament on Thursday, July 24, disclosed that Customs revenue recorded a shortfall of GH¢1.6 billion, representing 12.7 per cent, in the first half of the year.
This was largely due to systemic leakages at key entry points such as the Tema Port and the smuggling of goods across borders. Artificial intelligence (AI) provides a powerful answer to these difficulties by automating procedures, improving data processing and minimising the likelihood of human mistakes or corruption.
The government's goal in incorporating AI into customs operations is to expedite processes, boost revenue collection and strengthen compliance.
This article investigates the government's proposal to deploy AI at ports and proposes six practical techniques for reducing tax leakages while maintaining fiscal stability and economic growth.
Automated classification
Misclassification and undervaluation of imported commodities are one of the leading causes of revenue leakage, which is frequently helped by manual processes that are susceptible to manipulation.
AI-powered systems can analyse massive datasets, including global trade databases and historical import records, to precisely classify items using international standards like Harmonised System (HS) codes.
Machine learning algorithms can compare product descriptions, weights and origins to assign appropriate categories and valuations while minimising disparities. For example, AI may detect anomalies in claimed values by comparing them to real-time market prices, ensuring that tariffs and taxes accurately reflect the value of commodities.
AI can help improve risk management by spotting high-risk shipments in real time. AI systems can use predictive analytics to identify patterns in shipping data, such as irregular routes, frequent shippers with a history of problems or strange cargo descriptions.
These technologies can provide risk rankings to shipments, allowing customs agents to focus their examinations on high-risk material. For example, machine learning models trained on historical smuggling data can detect anomalies such as containers with mismatched weight-to-volume ratios, which could indicate hidden goods.
Validation of country of origin
Misrepresenting the nation of origin is a typical practice used to avoid higher tariffs or benefit from favourable trade agreements.
AI can validate the country of origin by connecting to global supply chain databases and analysing shipping papers, certificates of origin and bills of landing, with natural language processing (NLP). For example, AI can identify discrepancies by comparing a shipment's reported origin to its route history or supplier data. Blockchain integration can improve the process by creating a tamper-proof record of a product's path from origin to port.
Non-intrusive inspection technologies, such as X-ray scanners, are commonly used at ports like the Tema Port. However, interpreting scan pictures is primarily dependent on human skill, which can be variable.
AI-powered image recognition systems can analyse X-ray images for concealed compartments, undeclared products and restricted items.
These systems utilise deep learning to detect patterns in cargo photos and flag anomalies for further investigation. For example, AI can distinguish between valid cargo and contraband, like drugs or undeclared electronics, even in densely packed containers.
By automating picture analysis, the government can improve inspection speed and accuracy while eliminating smuggling-related leaks.
Integrating AI with blockchain
To prevent smuggling across land borders, the government can use AI and blockchain technology to build a transparent, end-to-end tracking system for goods.
AI may monitor data from IoT devices, such as GPS trackers on trucks, to detect deviations from approved routes, which could indicate smuggling.
Blockchain ensures that all data entries, including customs declarations and inspection records, are immutable and verifiable, eliminating the possibility of tampering. For example, if a truck delivering goods drives around a customs checkpoint, AI can transmit a warning and blockchain can provide an auditable record of the shipment's journey.
AI can help with long-term income protection by predicting possible leakage risks and optimising resource allocation.
Predictive models can analyse previous revenue data, trade volumes and seasonal trends to identify periods or ports that are more likely to leak. For example, AI can forecast when smuggling attempts will increase based on economic conditions or trade patterns, allowing the government to deploy greater resources ahead of time.
Furthermore, these models can forecast revenue from specific ports, allowing authorities to set benchmarks and identify shortfalls early.
Conclusion
The government's decision to deploy artificial intelligence at ports to reduce revenue leakages is a significant step towards modernising Ghana's customs operations.
The government may address systemic leakages, improve transparency and increase revenue collection by using the six stated solutions, which include automated categorisation, real-time fraud detection, origin validation, cargo scanning, blockchain integration and predictive analytics.
These measures will not only address the GH¢1.6 billion shortfall reported in 2025 but will also establish a more resilient and efficient customs system.
The writer is a Senior Lecturer/SME Industry Coach and Coordinator (MBA Impact Entrepreneurship and Innovation), University of Professional Studies, Accra.
ayiku.andrews@upsamail.edu.gh
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