Cyber-Resilient Digital Banking Analytics Using AI-Driven Federated Machine Learning on AWS
DOI:
https://doi.org/10.15662/IJEETR.2024.0604004Keywords:
Federated machine learning, Digital banking analytics, Cybersecurity, AWS cloud computing, Privacy-preserving AI, Financial risk analysis, Secure distributed learningAbstract
The increasing reliance on digital banking platforms has intensified the need for analytics systems that are not only intelligent and scalable but also resilient to evolving cyber threats and stringent regulatory requirements. Centralized machine learning approaches often expose sensitive financial data to privacy risks and single points of failure. To address these challenges, this paper presents a cyber-resilient digital banking analytics framework based on AI-driven federated machine learning deployed on Amazon Web Services (AWS). The proposed framework enables distributed banking entities to collaboratively train predictive models while keeping customer data localized, thereby ensuring privacy preservation and regulatory compliance. Advanced cybersecurity mechanisms—including end-to-end encryption, role-based access control, secure key management, and continuous monitoring—are integrated to protect data and model integrity throughout the learning lifecycle. The framework supports real-time and near–real-time analytics for critical banking applications such as fraud detection, credit risk assessment, and transaction anomaly identification. Experimental evaluation demonstrates strong predictive performance, low-latency model updates, and effective resistance to simulated cyberattacks. The results confirm that the proposed approach provides a secure, scalable, and privacy-aware foundation for next-generation digital banking analytics in cloud environments.
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