A Hybrid Stacked Ensemble Framework for Fraud Detection in Nigerian Financial Ecosystems: Evaluation with Localized Synthetic Data

Abstract

Financial fraud presents a major challenge to financial establishments, with Nigerian banks losing over ₦685 million to digital fraud in 2023. Traditional rule-based detection systems have high false-positive rates and limited adaptability; meanwhile, existing machine learning models are generally trained on non-localized datasets that ineffectively represent African fintech ecosystems. This study proposes a Hybrid Stacked Ensemble framework for fraud detection that improves detection accuracy, robustness, and explainability in localized financial environments. The proposed framework combines Random Forest, Gradient Boosting, and Extra Trees as base learners with XGBoost as the meta-classifier and integrates SHAP for model explainability. Performance was evaluated using the Kaggle credit card fraud dataset (284,807 transactions; 0.17% fraud) and a newly curated Nigerian synthetic dataset (150,000 transactions; 1.12% fraud) incorporating localized fraud patterns such as POS, USSD, mobile money, and rural–urban transaction disparities. Class imbalance was addressed using SMOTE oversampling, random undersampling, and cost-sensitive learning. On the Kaggle dataset, the Hybrid Ensemble achieved 99.96% accuracy, 97.14% precision, 79.70% recall, an F1-score of 0.880, and an AUC-ROC of 0.986, outperforming the best individual classifier in recall and AUC-ROC. On the Nigerian dataset, where individual classifiers achieved recall below 3.1%, the proposed framework attained 64.10% recall, an F1-score of 0.460, and an AUC-ROC of 0.866, representing improvements of 106.77% in recall and 488.57% in F1-score over XGBoost. Ablation studies and paired t-tests (p < 0.001) confirmed the effectiveness of the stacking strategy. The study contributes a localized Nigerian fraud dataset, a hybrid stacked ensemble architecture that exploits classifier diversity for improved fraud detection, and an explainable AI framework that enhances transparency, accountability, and regulatory compliance. Deployment as a containerized Streamlit application with JWT authentication demonstrates the framework's practicality as a scalable, explainable, and deployment-ready solution for fraud detection in Nigeria and similar African financial ecosystems.