Research Article
A Comparative Evaluation of Machine Learning Algorithms for Anomaly Detection in Mobile Money Transactions
Nnanna Ekedebe*
Issue:
Volume 11, Issue 2, December 2026
Pages:
18-22
Received:
12 July 2026
Accepted:
23 July 2026
Published:
17 August 2026
Abstract: Nigeria's mobile money ecosystem has expanded rapidly, with mobile money operators processing tens of trillions of naira annually and serving over 220 million subscribers; however, financial institutions lost ₦52.26 billion to fraud in 2024 alone, and automated fraud detection approaches remain largely unexplored specifically within the Nigerian mobile money context. This study presents a comparative evaluation of four machine learning algorithms Logistic Regression, Decision Tree, Random Forest, and XGBoost for anomaly detection in mobile money transactions, with the aim of identifying a viable deployment candidate for Nigerian fintech platforms. The four algorithms were trained and tested on the PaySim synthetic mobile money dataset under identical experimental conditions, using an 80/20 stratified train-test split combined with a hybrid resampling strategy that undersampled the majority class before applying SMOTE to the minority fraud class. Model performance was evaluated using Precision, Recall, F1-Score, AUC-ROC, and Inference Speed. XGBoost achieved the highest F1-Score of 35.70% and AUC-ROC of 99.98%, with all tree-based models recording Recall above 99% and all four algorithms demonstrating real-time viable inference speeds. Precision remained low across the tree-based models, attributed to the synthetic nature of the PaySim dataset and the undersampling strategy applied during training. XGBoost is recommended as the primary deployment candidate for Nigerian fintech platforms, with precision improvement through access to real transaction data and threshold optimization identified as immediate next steps.
Abstract: Nigeria's mobile money ecosystem has expanded rapidly, with mobile money operators processing tens of trillions of naira annually and serving over 220 million subscribers; however, financial institutions lost ₦52.26 billion to fraud in 2024 alone, and automated fraud detection approaches remain largely unexplored specifically within the Nigerian mo...
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Research Article
Pothole Localization Using Fine-Tuned YOLOv8n for Road Maintenance Prioritization in Resource-Constrained Regions
Nnanna Ekedebe*
Issue:
Volume 11, Issue 2, December 2026
Pages:
23-29
Received:
13 July 2026
Accepted:
23 July 2026
Published:
18 September 2026
Abstract: There is a significant safety and infrastructure challenge in resource-constrained regions like Nigeria and Africa posed by damaged roads and potholes. Over 70% of Nigeria's paved roads are damaged, and this contributes to road accidents and high vehicle repair costs. The current method of road inspection is manual, which is slow and reactive rather than proactive, making an automated detection system necessary. Deep learning models can scan large numbers of road images quickly and mark exactly where the damage is, which helps road agencies decide which roads to fix first. This study fine-tunes a pre-trained YOLOv8n object detection model on a publicly available pothole dataset comprising 665 images to automatically detect and localize road damage from images. The model was trained for 50 epochs using a free cloud GPU, with the dataset split into training, validation, and test sets. Data augmentation techniques such as flipping, rotating, and brightness adjustment were also applied to improve the model's ability to handle different road conditions. The model achieved an mAP@0.5 of 78.0%, precision of 81.4%, and recall of 67.5%, outperforming the dataset baseline of 74.0%. These results show that the model can correctly detect most potholes while keeping false detections low, though about one in three potholes was still missed, meaning the model works best as a support tool alongside human inspectors rather than a full replacement. These results also demonstrate that fine-tuning a lightweight pre-trained model on free cloud hardware represents a viable and accessible approach to automated road infrastructure monitoring in resource-constrained African settings.
Abstract: There is a significant safety and infrastructure challenge in resource-constrained regions like Nigeria and Africa posed by damaged roads and potholes. Over 70% of Nigeria's paved roads are damaged, and this contributes to road accidents and high vehicle repair costs. The current method of road inspection is manual, which is slow and reactive rathe...
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