Johannes, Pandeni Paavo2025-07-162025-07-162024-12Johannes, P. P. (2024). Automated Fraud Detection in Namibia’s Public Institutions' Financial Transactions Using Machine Learning: A Deep Learning Approach [Master’s thesis, Namibia University of Science and Technology].http://hdl.handle.net/10628/1069Financial fraud continues to be a significant concern in public-sector financial operations, undermining the credibility of financial statements and eroding public trust. Traditional methods used by financial experts, such as auditing, are frequently ineffective in addressing the growing complexity of fraudulent activities and effectively mitigating associated risks. This study aimed to tackle this issue by creating an automated fraud detection system based on deep learning designed for Namibia's public sector financial transactions. The Ministry of Finance provided the primary data for the study through the Office of the Auditor-General, which included accounts payable records from public entities with large transaction volumes for the fiscal years 2021/2022 and 2022/2023. The task of fraud detection is framed as a classification problem. The study explored three common deep learning models: Autoencoders, Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN). These models' performance was evaluated using historical and simulated financial data, focusing on accuracy, inference time, and resource utilisation. A comparative analysis revealed that the CNN model performed exceptionally well, with the highest accuracy (0.95), F1-score (0.98), and lowest false positive rate (0.038). In contrast, the GAN model excelled in inference time (7.17 ms per transaction) and precision (0.99). This study proposes a scalable, data-driven approach to improving fraud detection in large public-sector financial datasets, thereby increasing accountability in Namibia's public financial systems.endeep learningfinancial fraudfinancial transactionsfraud detectionmachine learningThesis