Cognitive JavaScript Cloud Framework: Real-Time Risk Detection and Adaptive Security using Neural Networks and Machine Learning
DOI:
https://doi.org/10.15680/IJCTECE.2024.0705004Keywords:
AI augmented software development, cloud native ERP, SAP HANA, digital payment automation, machine learning, software engineering framework, DevOps for ERPAbstract
This paper proposes an AI‑augmented software development framework aimed at enabling cloud‑native enterprise resource planning (ERP) systems that integrate digital payment automation, leveraging the in‑memory database platform SAP HANA and machine‑learning models. The framework envisions a layered architecture in which AI‑driven modules support automated payment workflows (capture, reconciliation, fraud detection), embedded within a cloud‑native ERP environment. By aligning software development practices (code generation, testing automation, continuous integration) with AI‑augmented capabilities, the proposed approach seeks to accelerate development time, ensure higher quality and reliability, and better adapt to evolving payment ecosystems. The cloud‑native nature of the ERP platform enables elasticity, micro‑service modularization, and modern DevOps pipelines, while SAP HANA provides real‑time transaction analytics, machine‑learning model embedding, and seamless enterprise integration. We articulate the research design, describe the conceptual model, and discuss hypothetical evaluation results showing improvements in development cycle‑time reduction, defect rates, payment processing latency and automation coverage. The paper also examines the advantages (speed, scalability, integration) and disadvantages (complexity, cost, governance) of the framework. Ultimately, we conclude that AI‑augmented software development frameworks for cloud‑native ERP environments hold substantial promise for digital‑payment automation, and we highlight directions for future research, including cross‑domain reuse, adaptive ML models, and edge‑cloud hybrids.
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