Cognitive Cloud Cybersecurity: Zero-Touch DevOps and AI Agents for Risk-Aware Data Privacy in SAP and Oracle Databases
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806010Keywords:
banking automation, online application systems, cloud architecture, artificial intelligence (AI), microservices, credit underwriting, KYC/AML, process automation, digital banking, model governanceAbstract
Automated online application systems in banking are transforming how financial institutions handle customer onboarding, credit applications, and service delivery. By leveraging cloud‑native architectures combined with artificial intelligence (AI) modules (e.g., document recognition, risk scoring, chatbots), banks can streamline processes, reduce manual overhead, accelerate decision‑making, and enhance user experience. This paper explores how the cloud architecture supports scalability, elasticity, microservices deployment, data pipelines and AI model hosting in the banking context. It also examines the integration of AI in such systems (e.g., for KYC/AML, credit risk, fraud detection) and how these interplay with the underlying cloud infrastructure. We present a review of existing literature, propose a research methodology for studying real‑world deployments, and analyse advantages and disadvantages of automated online application systems. Empirical findings from case‑studies (or hypothetical modelling) illustrate improvements in turnaround time, error rates and customer satisfaction, along with challenges like data governance, legacy integration and model bias. Our discussion addresses architectural design patterns, best practices and regulatory implications. The conclusion summarises key take‑aways and proposes future work directions: deeper explainable‑AI integration, hybrid cloud/on‑premises models, and continuous monitoring of AI‑driven decision flows.
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