Machine Learning and MLOps-Based Multi-Cloud Data Platforms for Scalable Enterprise Analytics and Banking Risk Intelligence
DOI:
https://doi.org/10.15662/IJEETR.2025.0704011Keywords:
Machine Learning, MLOps, Multi-Cloud Data Platforms, Enterprise Analytics, Banking Risk Intelligence, Predictive Modeling, Cloud Orchestration, Data Governance, Scalable Analytics, Financial Risk ManagementAbstract
The banking and financial sectors are increasingly dependent on large-scale data analytics to drive operational efficiency, risk management, and strategic decision-making. Multi-cloud data platforms enable enterprises to harness distributed computing resources, manage diverse datasets, and deploy scalable analytics pipelines across heterogeneous cloud environments. However, integrating machine learning (ML) models, ensuring reproducibility, and maintaining operational efficiency in multi-cloud settings present significant challenges.
This research proposes a machine learning and MLOps-driven multi-cloud data platform designed for scalable enterprise analytics and banking risk intelligence. The framework leverages distributed data storage, automated model deployment pipelines, and cross-cloud orchestration to enable efficient data processing, real-time analytics, and predictive risk assessment. MLOps practices ensure model versioning, continuous integration, automated testing, and reproducibility across cloud platforms, reducing deployment errors and operational overhead.
The proposed architecture supports risk intelligence by integrating ML models capable of detecting fraudulent transactions, forecasting market risk, and identifying operational vulnerabilities. Multi-cloud orchestration ensures scalability, redundancy, and compliance with regulatory frameworks such as Basel III and GDPR. The study highlights the benefits of combining machine learning, MLOps, and multi-cloud platforms for enterprise-scale analytics while discussing challenges including system complexity, data governance, and operational security.
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