Browsing by Author "Tavares, Jared"

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    Analysing fuel transactions of government vehicles in the Eastern Cape, South Africa
    (2025) Tavares, Jared; Silal, Sheetal
    Fuel management and fraud detection in government fleets are critical issues that have far-reaching financial and operational implications. To address these challenges, an investigation of fuel usage patterns and anomalies in the Eastern Cape Province government fleet in South Africa from April 2021 to January 2022 was conducted. Through the application of exploratory data analysis, clustering techniques, and predictive modelling, the research uncovers valuable insights that can be used to optimise fuel consumption and detect fraudulent activities within the fleet. Univariate and bivariate analyses reveal distinct patterns in fleet composition, transaction volumes, and fuel eJiciency across various vehicle makes, model derivatives, and departments. The use of clustering techniques enables the identification of distinct vehicle segments and transaction patterns, emphasising the importance of considering contextual factors when analysing fuel usage. To detect potential fraud, three key indicators are developed: abnormally large transactions, frequent transactions, and fuel price diJerences. Predictive models, including XGBoost, Multi-layer Perceptron, and Random Forest, are employed to automate the classification of transactions based on these fraud indicators. The Multi-layer Perceptron demonstrates the best performance, achieving an accuracy of 87% on the test set. The dissertation acknowledges limitations due to the scope of the data and missing information for certain geographic variables such as district and site. Future research could expand the geographical and temporal range, incorporate qualitative data, explore real-time monitoring systems, and investigate vehicle maintenance and fuel eJiciency. The present research makes a noteworthy contribution to the knowledge of fuel management and fraud detection in government fleets by oJering a data-driven approach to expose ineJiciencies and anomalies. The insights and methodologies presented serve as a foundation for future research and practical applications, ultimately leading to more eJicient, cost-eJective, and transparent fleet operations.