A Machine Learning Approach for Accurate Valuation of Imports in Uganda

Abstract

Accurate customs valuation is central to revenue mobilization, trade compliance, and economic stability in Uganda, where import duties contribute nearly one-third of domestic tax revenue. Yet persistent inefficiencies in conventional valuation methods such as reliance on importer-declared invoice values, outdated price databases, and manual adjudication have resulted in systemic undervaluation, mis invoicing, and annual revenue losses exceeding USD 200 million. This thesis investigates the potential of machine learning (ML) to transform customs valuation by developing and deploying predictive models trained on more than 70,000 import declaration records from Uganda Revenue Authority’s ASYCUDA system (2020–2024). Three supervised ML algorithms; Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) were implemented following a rigorous pipeline that included exploratory data analysis, feature engineering, and model optimization. All models demonstrated strong predictive performance (R² >0.93), with Random Forest achieving near-perfect accuracy(R² = 0.997, MAE = UGX 560.35, RMSE = UGX 1,868.23). Compared to Uganda’s current average based approach (MAE = UGX124,797.76), this represents a 99.55% reduction in error, underscoring the transformative capacity of ML for valuation precision. Beyond model benchmarking, the study contributes technically by operationalizing the Random Forest model into a Streamlit based prototype web application, offering real-time decision support for customs officers. Empirically, it provides the first quantified evidence of ML’s potential to address valuation fraud and inefficiencies in Uganda. Practically, it establishes a replicable frame work for low-resource settings, integrating ML with existing trade platforms such as ASYCUDA. The findings have significant policy implications: adopting ML-driven valuation can curtail revenue leakages, enhance compliance with WTO Customs Valuation Agreements, and support Uganda’s Vision 2040 and National Development Plan III goals for domestic revenue mobilization. Limitations such as reliance on secondary data, exclusion of informal trade, and simulation based deployment highlight opportunities for future research. These include incorporating regional datasets, exploring explainable AI techniques (e.g., SHAP, LIME) to improve transparency, and piloting ML integration within operational customs systems. This thesis thus advances the discourse on AI in public sector modernization, demonstrating that machine learning is not merely a technical innovation but a strategic enabler for fiscal sustainability, trade integrity, and digital transformation in Uganda’s customs administration.