Strategic Optimization of ETL Architectures for Financial Data Warehouse Systems: A Multi-Objective Analysis

Rakesh Mittapally — Fri, 23 Jan 2026 00:00:00 +0000

In the ever-changing landscape of financial technology, organizations struggle with the dual imperative of handling massive data volumes while adhering to strict regulatory frameworks and maintaining market dominance. This study refines the extraction, transformation, loading (ETL) algorithms designed for financial data warehousing in the fintech industry. As financial transactions grow in complexity, the ability to seamlessly integrate, process, and analyze data from disparate sources emerges as a key factor in organizational performance. This research has particular relevance in mitigating systemic inefficiencies that often plague large-scale financial data warehouses, such as computational lags, resource allocation challenges, and scalability barriers. By evaluating state-of-the-art ETL methods, this study provides financial institutions with empirically based strategies for improving data processing efficiency, while preserving data integrity and complying with evolving regulatory mandates. Using a systematic approach, this research uses the MOORA (Multi-Objective Optimization Based on Ratio Analysis) technique to rigorously evaluate six state-of-the-art ETL frameworks: PySpark-Optimized ETL Framework, AWS Glue-Pipelineed S Dabrita-Bas Redshift, Athena-Driven Server less Analytics, Data Lakehouse with Delta Lake, and Hadoop-based Batch Processing. The evaluation is structured around seven

Key Metrics: processing speed, data integrity, query efficiency, cost-effectiveness, execution time, data propagation latency, and computational resource consumption. The comparative investigation identifies the AWS Glue-based data pipeline as the most effective framework, securing the highest evaluation score (0.07604) over the PySpark-Optimized ETL Framework (0.07496) and Athena-Driven Server less Analytics (0.077). The results underscore AWS Glue's exceptional ability to preserve data consistency while simultaneously improving query execution and overall processing efficiency. This study highlights how cloud-based ETL solutions are revolutionizing financial data processing by delivering better scalability and cost-effectiveness. Furthermore, embedding artificial intelligence within ETL workflows strengthens data integrity through intelligent anomaly detection and dynamic transformation algorithms. Beyond technological advancements, research underscores the need for financial institutions to rigorously implement data governance to mitigate the persistent issues of redundancy and inconsistency. By providing a well-defined framework, these findings equip financial institutions with the tools to evaluate and deploy ETL systems according to their operational landscapes, ultimately refining analytical accuracy and securing a strategic edge in an industry where data is at its peak.

Keywords: Fintech, MOORA method, multi-objective optimization, AWS Glue, PySpark, Data consistency, Query performance, Cloud-based ETL, Big Data processing and Artificial intelligence in ETL.

Integration of Artificial Intelligence and ARAS Method for Multi-Criteria Decision-Making in Organizational Performance Assessment

Suresh Deepak Gurubasannavar — Tue, 27 Jan 2026 00:00:00 +0000

This investigation uses an integrated combination of artificial intelligence (AI) and multi-criteria decision-making (MCDM) approaches, specifically the Aggregate Ratio Assessment (ARAS) method, to develop comprehensive decision support frameworks for assessing organizational performance. The study analyses five critical organizational aspects: financial management, customer relations, operational processes, knowledge development, and organizational capability. By using standardized and weighted matrices, the ARAS technique transforms multi-dimensional criteria into uniform metrics suitable for unbiased evaluation. The findings reveal that financial factors demonstrate the greatest local importance (0.3325) and BNP measurement (0.407), which highlight their key influence on decision processes. Knowledge development, which exhibits significant overall importance (0.2793), emerges as essential for sustained organizational success. The approach generates robust rankings through applied operational calculations (Si) and importance parameters (Ki), with the leading factor recording a Ki value of 0.837833. This integrated AI-MCDM method successfully combines mathematical rigor with intelligent computation, delivering reliable and streamlined decision results. These results provide meaningful guidance for resource allocation, strategy formulation, and operational improvement within organizations. This work advances the growing field of AI-enhanced decision support algorithms, demonstrating how conventional MCDM frameworks can be strengthened with AI integration to address complex business constraints across a variety of industries.

Keywords: Artificial Intelligence , Multi-Criteria Decision Making, ARAS Method, Decision Support Systems, Organizational Performance, Normalized Matrix, Weighted Matrix, Utility Function, Expert Systems, Machine Learning.

Journal of Data Science and Information Technology

Strategic Optimization of ETL Architectures for Financial Data Warehouse Systems: A Multi-Objective Analysis

Integration of Artificial Intelligence and ARAS Method for Multi-Criteria Decision-Making in Organizational Performance Assessment