FRAUD DETECTION IN NIGERIAN INVESTMENT ADVISORY SECTOR USING MACHINE LEARNING ALGORITHMS - Fatokun, Johnson O.; Sikiru, Mustapha; Balogun, Funmilola; Okorie, Daberechi D. (FUDMA JOURNAL OF SCIENCES, 2025)
The increasing rate of financial fraud within Nigeria’s investment advisory sector presents a pressing challenge, particularly as traditional detection methods struggle to keep pace with evolving fraudulent behaviors. With the growing reliance on technology in the financial ecosystem, machine learning offers a promising solution for detecting anomalies and minimizing risk. This study explores the application of three machine learning algorithms: Logistic Regression, Random Forest, and XGBoost to predict fraudulent transactions in the Nigerian investment advisory landscape. To address class imbalance which is a common issue in fraud datasets, hybrid resampling using SMOTE and Tomek Links was implemented. The Random Forest model emerged as the most robust, maintaining consistent performance across key metrics even after resampling. This research also emphasizes the importance of integrating cost-sensitive learning, regular model retraining, and cross-validation to adapt to Nigeria’s dynamic fraud landscape. The study presents a scalable approach to enhancing fraud detection systems and safeguarding investor trust in the sector.
Keywords: Fraud detection; Nigerian investment; Random Forest; Machine learning; Class imbalance