AUTOMATED FRAUD DETECTION IN NAMIBIA’S PUBLIC INSTITUTIONS' FINANCIAL TRANSACTIONS USING MACHINE LEARNING: A DEEP LEARNING APPROACH
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Date
2024-12
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Namibia University of Science and Technology
Abstract
Financial 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.
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Keywords
deep learning, financial fraud, financial transactions, fraud detection, machine learning
Citation
Johannes, 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].