AI-Driven Cloud-Native Enterprise Architecture for Secure Financial Systems Fraud Detection and Real-Time Payment Orchestration
DOI:
https://doi.org/10.15680/IJCTECE.2025.0803009Keywords:
AI-driven architecture, cloud-native finance, cloud-native financereal-time payment orchestration, financial cybersecurity, machine learning, microservices, secure transaction processing, enterprise banking systems, digital paymentsAbstract
The financial industry has undergone rapid digital transformation, driven by the adoption of cloud-native architectures, real-time payment platforms, and advanced analytics. While these technologies enable faster, scalable, and flexible financial operations, they also expose enterprises to cybersecurity threats, fraudulent transactions, and operational inefficiencies. Traditional security models and payment orchestration frameworks are insufficient to handle sophisticated financial crimes, real-time transaction monitoring, and regulatory compliance requirements.
This research proposes an AI-driven cloud-native enterprise architecture designed for secure financial systems, fraud detection, and real-time payment orchestration. The framework integrates artificial intelligence (AI) and machine learning (ML) models to detect anomalies, predict fraudulent activities, and dynamically orchestrate payment workflows. Cloud-native components including microservices, containerized applications, and serverless computing enable scalability, resilience, and modular deployment. The architecture incorporates identity management, encryption protocols, and API-based secure integrations to protect sensitive financial data while facilitating real-time transaction processing.
The study demonstrates how AI-driven analytics and orchestration mechanisms enhance system security, improve operational efficiency, and ensure compliance with regulatory standards. Additionally, the research highlights the benefits and challenges associated with deploying AI-enabled cloud-native financial systems, including system complexity, data quality requirements, and integration with legacy banking platforms.
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