INTELLIGENT CLOUD-NATIVE BANKING: LEVERAGING MACHINE LEARNING FOR SECURE AND SCALABLE DIGITAL FINANCIAL SERVICES
DOI:
https://doi.org/10.15662/8s33pn10Keywords:
Cloud-Native Banking, Machine Learning, Artificial Intelligence, Financial Services, Digital Banking, Fraud Detection, Microservices, Cloud Computing, Banking Security, Risk AnalyticsAbstract
The banking industry is undergoing a significant transformation driven by cloud computing, artificial intelligence (AI), machine learning (ML), and digital-first customer expectations. Traditional banking systems, often constrained by monolithic architectures and legacy infrastructure, struggle to meet modern requirements for scalability, security, operational agility, and real-time decision-making. Cloud-native banking architectures address these challenges through microservices, containerization, API-driven ecosystems, and elastic infrastructure.
Simultaneously, machine learning enables financial institutions to automate decision-making, strengthen fraud detection, personalize customer experiences, optimize risk management, and improve operational efficiency. The integration of cloud-native technologies and machine learning creates intelligent banking platforms capable of delivering secure, resilient, and scalable financial services.
This article explores the architectural principles of intelligent cloud-native banking, examines the role of machine learning across banking functions, discusses security and compliance considerations, and presents implementation frameworks for modern financial institutions. The study further analyzes challenges, emerging trends, and future directions in AI-powered digital banking ecosystems.
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