Cloud-Native AI Metrics Model for Real-Time Banking Project Monitoring with Integrated Safety and SAP Quality Assurance
DOI:
https://doi.org/10.15662/IJRAI.2024.0701005Keywords:
AI-driven metrics, cloud-native monitoring, banking project management, SAP quality assurance, real-time analytics, safety compliance, transaction intelligenceAbstract
This paper presents a cloud-native AI metrics model designed to enhance real-time monitoring, operational transparency, and quality assurance in banking project environments. The proposed framework integrates advanced AI-driven analytics with scalable cloud infrastructures to continuously evaluate project performance, transaction behavior, and system dependencies. By incorporating automated safety controls, the model effectively detects anomalies, mitigates operational risks, and ensures compliance with critical banking security standards. The integration of SAP-based quality assurance workflows strengthens data consistency, streamlines audit processes, and supports proactive decision-making. The architecture employs continuous data ingestion, intelligent metrics computation, and adaptive alert mechanisms to maintain high reliability across distributed project operations. Experimental results demonstrate improved visibility, reduced error propagation, and enhanced governance for banking projects, positioning the model as a robust solution for next-generation financial IT management.
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