Development of an Artificial Intelligence and Blockchain-Based Cyber-Security Framework for Combating Financial Fraud in Nigeria’s Digital Banking Ecosystem

Abstract

Financial fraud remains a significant threat to Nigeria’s rapidly expanding digital banking ecosystem, resulting in substantial financial losses, reduced customer trust, and systemic vulnerabilities. This study developed and experimentally validated a hybrid Artificial Intelligence (AI) and Blockchain-based cybersecurity framework designed to detect, prevent, and mitigate financial fraud in real time. A dataset comprising 52,480,000 anonymized digital banking transactions, including 146,520 confirmed fraudulent cases, was used for model development and validation. The AI engine integrated Extreme Gradient Boosting, Deep Neural Networks, and Long Short-Term Memory architectures to capture structured and sequential fraud patterns, while a permissioned blockchain layer ensured transaction immutability, transparency, and tamper resistance through distributed ledger validation and smart contracts. Experimental results demonstrated detection accuracy of 96.8%, recall of 94.1%, precision of 95.2%, and a false positive rate of 3.2%, significantly outperforming existing institutional systems (p < 0.001). The model achieved an AUC score of 0.981, indicating excellent discriminatory capability. Regression analysis identified transaction velocity, device-switch frequency, geolocation deviation, and blockchain hash mismatch as significant fraud predictors. Blockchain stress testing confirmed scalability up to 3,800 transactions per second with 100% tamper detection accuracy. The findings demonstrate that integrating AI-driven behavioral analytics with blockchain-based data integrity mechanisms produces a robust, scalable, and secure cybersecurity framework capable of substantially reducing fraud risk within Nigeria’s digital financial sector. The proposed framework offers a replicable model for emerging economies seeking technologically advanced and decentralized fraud mitigation strategies.