Abstract :

Legacy Risk Analytics Systems Often Face Challenges Related To Limited Scalability, Slow Processing Performance, High Operational Costs, And Difficulties In Managing Rapidly Growing Volumes Of Organizational Data. This Study Investigates The Modernization Of Legacy Risk Analytics Systems Through The Adoption Of Cloudbased Data Engineering Architectures. A Hypothetical Research Framework Was Developed To Compare The Performance Of Traditional Onpremise Risk Analytics Environments With A Cloudnative Architecture Incorporating Distributed Storage, Automated ETL Pipelines, Scalable Computing Resources, And Real-time Analytical Capabilities. The Study Evaluated Key Performance Indicators Including Data Processing Time, Query Response Time, System Availability, Storage Efficiency, Scalability, Infrastructure Costs, And Risk Model Execution Performance. The Findings Demonstrated Substantial Improvements In Operational Efficiency, With Significant Reductions In Processing Time And Infrastructure Costs, Alongside Enhanced Scalability, Resource Utilization, And Disaster Recovery Capabilities. The Cloud-based Framework Also Improved Data Integration, Risk Assessment Accuracy, And Decision-making Effectiveness Through Real-time Analytics And Centralized Data Management. Furthermore, The Proposed Architecture Strengthened Security, Governance, And Compliance Through Advanced Cloud-based Controls.

Published:

10-5-2022

Issue:

Vol. 22 No. 5 (2022)

Page Nos:

20-27

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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