Akubueze Augustine Ikeechukwu, Amanze Bethran Chibuike, Ibebuogu Christian Chinwe
The increasing sophistication of fraudulent activities within financial institutions poses significant challenges to security, trust, and operational stability. Traditional rule-based detection systems often fail to adapt to emerging fraud patterns, resulting in high false positives and delayed detection. To address this, this papermodel an AI-Based Network Management System for Fraud Detection in Financial Institutions. The system integrates real-time monitoring, anomaly detection, and predictive analytics using machine learning algorithms to enhance the ability of financial networks to identify and mitigate fraud. The paper involved the design of a modular framework comprising data collection, feature extraction, classification, and decision-making modules. Sample datasets of user access logs and transaction records were utilized to train and test the system. Evaluation metrics such as accuracy, precision, recall, false positive rate, and availability were employed to assess system performance.Test run results demonstrated that the system achieved 99.9% accuracy, and a very low false positive rate of 0.071%. These outcomes confirm that the proposed solution can reliably detect fraudulent activities while maintaining network performance and user experience.The paper concludes that AI-driven network management systems can significantly strengthen fraud prevention mechanisms in financial institutions. The developed framework contributes to the field by providing ascalable, adaptive, and intelligent fraud detection model suitable for deployment in modern financial environments.
AI, Network, ML and Banks.