Secure Digital Banking with Federated AI: An AWS Cloud-Based Predictive Analytics Architecture for Financial Risk Intelligence
DOI:
https://doi.org/10.15662/IJRAI.2024.0703005Keywords:
Federated learning, Digital banking security, AWS cloud architecture, Financial risk analytics, Predictive analytics, Data privacy, Artificial intelligenceAbstract
The increasing adoption of digital banking platforms has intensified the need for secure, privacy-preserving, and intelligent analytics capable of managing financial risk in real time. This paper presents a Secure Digital Banking architecture based on Federated Artificial Intelligence deployed on the AWS cloud, enabling predictive analytics without exposing sensitive customer data. The proposed framework leverages federated learning to train global risk prediction models across distributed banking institutions while ensuring data locality, regulatory compliance, and confidentiality. Built on AWS cloud-native services, the architecture integrates secure APIs, scalable microservices, encryption, access control, and continuous monitoring to support real-time risk intelligence. Predictive models analyze transactional patterns to detect fraud, credit risk, and operational anomalies with improved accuracy and reduced latency. Experimental evaluation demonstrates that the proposed federated AI architecture enhances data privacy, system scalability, and predictive performance compared to centralized analytics approaches. The framework offers a robust and future-ready solution for secure, intelligent, and compliant financial risk management in modern digital banking ecosystems.
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