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Journal articles on the topic 'Financial data warehousing'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Singu, Santosh Kumar. "Leveraging Snowflake for Scalable Financial Data Warehousing." International Journal of Computing and Engineering 6, no. 5 (2024): 41–51. http://dx.doi.org/10.47941/ijce.2296.

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Purpose: The study discusses the increasing challenges faced by financial services due to fast-growing transaction, regulatory, and client data, and the need for more flexible, scalable, and affordable data management systems. It examines the potential of Snowflake, a cloud-based data warehousing platform, to address these issues through its multi-cluster shared data architecture Methodology: The paper analyzes Snowflake's architecture, focusing on its ability to decouple storage from compute, allowing organizations to scale resources as needed. Case studies of financial institutions implement

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Srinivasa, Chakravarthy Seethala, and Krishna Chaithanya Seethala Sai. "AI-Driven Data Warehousing for Financial Institutions: Future-Proofing Against Market Volatility." Journal of Scientific and Engineering Research 11, no. 5 (2024): 309–14. https://doi.org/10.5281/zenodo.14059593.

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In today’s volatile financial environment, characterized by rapid shifts in markets, geopolitical tensions, and evolving regulatory frameworks, financial institutions face unprecedented challenges. Traditional data warehousing solutions, while once sufficient, now fall short in handling the speed, scale, and complexity of modern financial data. The introduction of Artificial Intelligence (AI) into data warehousing has emerged as a transformative force, enabling financial institutions to not only manage large-scale data but also derive predictive insights that help mitigate risks and opti

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Researcher. "DATA WAREHOUSING ARCHITECTURE AND IMPLEMENTATION FOR ENHANCED FINANCIAL REPORTING: A SYSTEMATIC REVIEW." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 751–59. https://doi.org/10.5281/zenodo.14228481.

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This article presents a comprehensive analysis of data warehousing architectures and implementations specifically designed for financial reporting systems. The article examines the evolution, current state, and future trends of data warehousing in financial institutions, focusing on architectural components, implementation frameworks, and business intelligence integration. Through detailed analysis of system design principles, performance optimization techniques, and regulatory compliance requirements, we demonstrate how modern data warehouses can effectively support complex financial rep

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Samyukta Rongala. "Optimizing ETL Processes for High-Volume Data Warehousing in Financial Applications." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 700–708. https://doi.org/10.52783/jisem.v10i8s.1130.

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The Extract, Transform, Load (ETL) process is a critical backbone in financial data warehousing, where large-scale data volumes demand optimized performance to meet industry requirements. Financial institutions rely heavily on ETL systems to integrate, cleanse, and structure data for decision-making and regulatory compliance. This paper delves into the optimization of ETL processes for high-volume data warehousing in financial applications. By analyzing current challenges, exploring advanced architectures, and incorporating emerging technologies such as Big Data frameworks and cloud solutions,

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Ferreira, José, Fernando Almeida, and José Monteiro. "Building an Effective Data Warehousing for Financial Sector." Automatic Control and Information Sciences 3, no. 1 (2017): 16–25. http://dx.doi.org/10.12691/acis-3-1-4.

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Srinivasa, Chakravarthy Seethala. "Cloud and AI Convergence in Banking & Finance Data Warehousing: Ensuring Scalability and Security." European Journal of Advances in Engineering and Technology 9, no. 3 (2022): 190–92. https://doi.org/10.5281/zenodo.14168767.

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In the banking and finance sector, the integration of cloud computing and artificial intelligence (AI) technologies within data warehousing solutions is revolutionizing data management, processing, and security. This convergence is essential not only for handling complex datasets but also for meeting the growing demands for scalability and enhanced security—both critical to modern financial systems. This article examines how cloud-AI fusion addresses unique challenges in banking data warehousing, focusing on strategies to ensure scalability and secure sensitive financial data. By explori

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Seethala, Srinivasa Chakravarthy. "AI-Infused Data Warehousing: Redefining Data Governance in the Finance Industry." International Research Journal of Innovations in Engineering and Technology 05, no. 05 (2021): 150–52. http://dx.doi.org/10.47001/irjiet/2021.505028.

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The finance industry is undergoing a paradigm shift in data management with the integration of artificial intelligence (AI) into data warehousing. This paper explores the transformative potential of AI-infused data warehousing in redefining data governance within the finance sector. Key challenges such as data quality, regulatory compliance, and real-time risk management are analyzed alongside AI-powered solutions. By presenting applications and a comprehensive implementation framework, this article offers a roadmap for optimizing financial data warehouses to support enhanced decisionmaking, i

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Naveen, Edapurath Vijayan. "Building Scalable Data Warehouses for Financial Analytics in Large Enterprises." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 3 (2024): 1–10. https://doi.org/10.5281/zenodo.14384006.

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In today's digital era, large enterprises face the daunting task of managing and analyzing vast volumes of financial data to inform strategic decision-making and maintain a competitive edge. Traditional data warehousing solutions often fall short in addressing the scale, complexity, and performance demands of modern financial analytics. This paper explores the architectural principles, technological strategies, and best practices essential for building scalable data warehouses tailored to the needs of financial analytics in large organizations. It delves into data integration techniques, perfo

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Srinivasa, Chakravarthy Seethala. "AI and Data Warehousing for Financial Services: Future-Proofing Data Governance and Compliance." European Journal of Advances in Engineering and Technology 9, no. 1 (2022): 80–82. https://doi.org/10.5281/zenodo.14168910.

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The financial services industry is undergoing a significant transformation driven by the integration of Artificial Intelligence (AI) and advanced data warehousing techniques. This paper examines the impact of AI in advancing data governance and enhancing regulatory compliance in financial services. We explore how AI addresses persistent challenges such as data quality, privacy protection, and real-time regulatory compliance. The paper also presents a detailed framework for implementing AI-powered data governance systems and highlights several use cases demonstrating the advantages of AI in man

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LEE, SANG M., SOONGOO HONG, and PAIRIN KATERATTANAKUL. "IMPACT OF DATA WAREHOUSING ON ORGANIZATIONAL PERFORMANCE OF RETAILING FIRMS." International Journal of Information Technology & Decision Making 03, no. 01 (2004): 61–79. http://dx.doi.org/10.1142/s0219622004000040.

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This study investigates the relationship between the implementation of data warehousing and organizational performance in retailing firms. For this research goal, five hypotheses are developed and tested. The paired T-tests and Hotelling's T-square tests are performed on two groups — retailing firms implementing data warehousing and those that do not. The results of analyses show that data warehousing firms achieve better nonfinancial performance, including promotional performance analysis, vendor analysis, customer analysis, and market segmentation analysis, but do not achieve better financia

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Wang, Han. "A Novel Risk Control Method of Supply Chain Finance for Commercial Banks with Big Data." Highlights in Business, Economics and Management 7 (April 5, 2023): 502–12. http://dx.doi.org/10.54097/hbem.v7i.7028.

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Commercial bank supply chain financial business is mainly divided into accounts receivable financing mode, inventory financing mode and advance financing mode. Traditional supply chain financial business of commercial banks in different modes will form different risks, including small and medium-sized enterprise credit risk, the core enterprise credit risk and credit risk of the third-party logistics warehousing company. Under the background of increasingly powerful financial science and technology, we can use more financial technology through big data multidimensional collecting enterprise in

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Researcher. "DATA WAREHOUSING WITH AMAZON REDSHIFT: REVOLUTIONIZING BIG DATA ANALYTICS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 395–405. https://doi.org/10.5281/zenodo.13270530.

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The article talks about Amazon Redshift, a cutting-edge cloud-based data warehouse that is changing the way big data analytics is done. In it, the architecture, main features, and benefits of Redshift are discussed in detail. Columnar storage, massively parallel processing, and a distributed system design are emphasized. The article discusses how business intelligence, data science, operational analytics, customer analytics, and financial analytics are used in the real world. It also compares and contrasts with other cloud data stores, such as Snowflake and Google BigQuery, pointing out their

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HONG, SOONGOO, PAIRIN KATERATTANAKUL, SUK-KI HONG, and QING CAO. "USAGE AND PERCEIVED IMPACT OF DATA WAREHOUSES: A STUDY IN KOREAN FINANCIAL COMPANIES." International Journal of Information Technology & Decision Making 05, no. 02 (2006): 297–315. http://dx.doi.org/10.1142/s0219622006001927.

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Although several previous studies have investigated the success factors and implementations of data warehouses, only few of them have explored the end-users' perceptions of data warehouses. Moreover, none of these previous studies were conducted in a company outside North America. Thus, this study was conducted to identify the data warehousing system characteristics affecting end-users' usage and perceived impact of using data warehouses in Korean financial companies. A research model for end-users' usage and perceived impact of using data warehouses was developed based on the Technology Accep

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Kandula, Nagababu. "Evolution and Impact of Data Warehousing in Modern Business and Decision Support Systems." International Journal of Computer Science and Data Engineering 2, no. 2 (2025): 1–11. https://doi.org/10.55124/csdb.v2i2.247.

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Data warehousing has become an essential tool in modern organizations driven by increasing business complexity and technological advancements. Organizations collect vast amounts of data from multiple sources that require efficient storage and analysis solutions. This research paper examines the role of data warehousing in decision making, its integration with emerging technologies, and its growing impact on various industries. Research significance: This research is significant as it highlights the transformative role of data warehousing in decision-making across industries. By improving data

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Su, Siew-Phek T., and Ashwin Needamangala. "Harvesting Information from a Library Data Warehouse." Information Technology and Libraries 19, no. 1 (2017): 17–28. http://dx.doi.org/10.6017/ital.v19i1.10070.

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Data warehousing technology has been defined by John Ladley as "a set of methods, techniques, and tools that are leveraged together and used to produce a vehicle that delivers data to end users on an integrated platform." (1) This concept h s been applied increasingly by industries worldwide to develop data warehouses for decision support and knowledge discovery. In the academic sector, several universities have developed data warehouses containing the universities' financial, payroll, personnel, budget, and student data. (2) These data warehouses across all industries and academia have met wi

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Preeta Pillai. "Cloud vs. On-Premise Data Warehousing: A Strategic Analysis for Financial Institutions." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 503–13. https://doi.org/10.32996/jcsts.2025.7.3.57.

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The transformation of data warehousing in financial services marks a pivotal shift in how institutions manage and utilize data assets. Financial organizations navigate complex decisions between cloud-based, on-premise, and hybrid solutions, each offering distinct advantages and challenges. The evolution encompasses enhanced security protocols, improved regulatory compliance mechanisms, and advanced analytical capabilities. Modern implementations demonstrate substantial improvements in operational efficiency, cost optimization, and system performance. The integration of artificial intelligence

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Researcher. "TRANSFORMING FINANCIAL SYSTEMS: THE ROLE OF DATA ENGINEERING IN FRAUD DETECTION, RISK MANAGEMENT, AND OPERATIONAL EFFICIENCY." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 2095–105. https://doi.org/10.5281/zenodo.14334195.

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This comprehensive article explores the transformative impact of data engineering on modern financial systems, focusing on critical areas, including fraud detection, risk management, and operational efficiency. The article delves into the evolution of financial technology infrastructure, highlighting how advanced data processing capabilities, machine learning implementations, and robust security frameworks have revolutionized the banking sector. The study investigates key technological advancements in real-time transaction processing, data warehousing solutions, and automated monitoring system

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Swamy, Abhilasha Hala. "The Societal Impact of Data Integration on Financial Inclusion." European Journal of Accounting, Auditing and Finance Research 13, no. 5 (2025): 1–14. https://doi.org/10.37745/ejaafr.2013/vol13n5114.

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This article examines how enterprise data systems and integration technologies enable financial inclusion by providing access to financial services for underserved populations worldwide. It explores the key enabling technologies behind financial inclusion initiatives, including API frameworks, data warehousing solutions, and edge computing infrastructure. The transformative applications of these technologies are investigated across microfinance expansion, digital banking for the unbanked, and SME financing. The article addresses critical technical challenges in implementing these systems and t

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Rohmah, Maulinda Lailatul. "Analisis Sektor Ekonomi Potensial Dalam Mendorong Pembangunan Ekonomi Daerah Di Kabupaten Trenggalek." Jurnal Ilmu Ekonomi JIE 5, no. 3 (2022): 579–95. http://dx.doi.org/10.22219/jie.v5i3.18755.

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This study aimed to determine the potential economic sector in Trenggalek Regency in encouraging regional economic development. The data used are Gross Regional Domestic Product data based on 2010 constant prices in 2015-2019 and data on the 2010-2019 gross regional domestic products based on regular prices for the residency of Kediri in 2015-2019. Based on the Location Quentiont analysis results, included in the fundamental and leading sectors in Trenggalek Regency are the Transportation and Warehousing sector; Electricity and Gas Procurement; Health Services and Social Activities; Financial

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Rohmah, Maulinda Lailatul. "Analisis Sektor Ekonomi Potensial Dalam Mendorong Pembangunan Ekonomi Daerah Di Kabupaten Trenggalek." Jurnal Ilmu Ekonomi JIE 5, no. 3 (2022): 579–95. http://dx.doi.org/10.22219/jie.v5i3.18755.

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This study aimed to determine the potential economic sector in Trenggalek Regency in encouraging regional economic development. The data used are Gross Regional Domestic Product data based on 2010 constant prices in 2015-2019 and data on the 2010-2019 gross regional domestic products based on regular prices for the residency of Kediri in 2015-2019. Based on the Location Quentiont analysis results, included in the fundamental and leading sectors in Trenggalek Regency are the Transportation and Warehousing sector; Electricity and Gas Procurement; Health Services and Social Activities; Financial

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Researcher. "MAXIMIZING FINANCIAL INTELLIGENCE - THE ROLE OF OPTIMIZED ETL IN FINTECH DATA WAREHOUSING." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 464–71. https://doi.org/10.5281/zenodo.13302451.

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Data management is crucial in sustaining competitiveness and challenges regarding regulations in fintech. This management involves the Extract, Transform, Load (ETL) method that entails the extraction of data, the transformation of that data, and the loading of data warehouses. This paper evaluates practices for ETL operations in the financial context of data warehousing, with a focus on the novel technologies and methods. Tackles include data quality, real-time processing, and security; solutions range from machine learning to cloud-based ETL to cross-functional collaboration. This paper also

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Thite, Gururaj. "Modern Data Architectures in Financial Analytics: A Technical Deep Dive." European Journal of Computer Science and Information Technology 13, no. 22 (2025): 79–86. https://doi.org/10.37745/ejcsit.2013/vol13n227986.

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Modern financial analytics architectures are undergoing a transformative evolution in response to increasing data complexity and volume demands. The integration of distributed computing frameworks, cloud-based data warehousing solutions, and artificial intelligence has revolutionized how financial institutions process and analyze data. Advanced ETL pipelines leveraging Apache Spark's capabilities have enhanced processing efficiency, while Snowflake's cloud platform has optimized query performance through innovative storage and compute separation. AI-driven quality assurance frameworks have aut

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Srinivasa, Chakravarthy Seethala, and Krishna Chaithanya Seethala Sai. "The Next Generation of AI-Driven Data Warehouses in the Financial Sector: A Blueprint for Innovation." Journal of Scientific and Engineering Research 10, no. 2 (2023): 240–42. https://doi.org/10.5281/zenodo.14059577.

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The financial sector faces increasing demands for faster, more precise, and compliant data management solutions. Traditional data warehouses are often inadequate for handling the complex, high-velocity data that modern finance generates. This paper explores the transformative potential of AI-driven data warehouses for the financial industry, outlining an innovative blueprint that leverages AI to meet financial institutions' data needs. We examine core areas such as risk management, regulatory compliance, fraud detection, and customer insights, detailing how AI-driven data warehouses provide cr

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Kumar, Rohit. "AI-Augmented Data Security in Cloud Migration: Leveraging Generative AI and Snowflake for Secure Financial Data Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–8. https://doi.org/10.55041/ijsrem50146.

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This paper offers a complete framework combining Snowflake's cloud data platform with AI- augmented methods to improve data security during cloud migration. Six strategic phases—from preprocessing to evaluation—each help to contribute to better performance measures in the suggested approach. Visual studies show a notable decrease in system response time (250 ms to 140 ms) as well as a continuous increase in security score (70% to 95%), and detection accuracy (68% to 94%). Moreover, accuracy and precision measures show clear development throughout the phases, reaching respectively 93% and 91%.

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Dong, Xiang Ying, and Xue Qun Wang. "The Building of Cigarette Factory Warehousing Management System Based on Data Warehouse." Advanced Materials Research 490-495 (March 2012): 664–68. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.664.

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The construction of X Cigarette Factory’s Material Order and Warehousing Management System is underway, at the same time, the new system and the office automation system, the production execution system and the automated logistics system can form an integral enterprise business that operates on the basis of supply chain. It is necessary to be point out that it is an approach with Chinese characteristics on developing information system: compared with integrative and Process Reengineering construction model of the developed countries, most Chinese enterprises like X Cigarette Factory tend to de

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Kohn, Jonathan W., Michael A. McGinnis, and John E. Spillan. "A longitudial study of private warehouse investment strategies." Journal of Transportation Management 21, no. 2 (2009): 71–86. http://dx.doi.org/10.22237/jotm/1254355620.

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This article revisits private warehouse investment decision making, a topic previously examined in 1989 by McGinnis, Kohn, and Myers (1990). Since then there has been a substantial amount of discussion regarding the scope and nature of logistics /supply chain management. In particular the roles of private, contract, and public warehousing has been discussed, increased emphasis on financial performance and strategic decision making may have altered the criteria for investment decisions in private warehousing, increased coordination of supply chains may have altered the relative importance of pr

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Sai, Kishore Chintakindhi. "Dynamic Cost Optimization Framework for BigQuery and Cloud Data Warehousing Systems." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 11, no. 1 (2025): 1–24. https://doi.org/10.5281/zenodo.15564484.

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This dissertation introduces a cost optimization framework designed for BigQuery and cloud data warehouses, focusing on the issue of growing operational costs related to data processing and storage. Using extensive usage and performance data from various cloud environments, this research pinpoints key cost factors and formulates strategies for improved resource allocation, resulting in considerable cost savings. The results generally indicate that adaptive resource management methods can lower operational costs by as much as 30%, thus boosting the long-term financial viability of cloud data wa

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L. Kuladeep Kumar. "Exploring the Opportunities of Fintech Services." Communications on Applied Nonlinear Analysis 31, no. 5s (2024): 79–90. http://dx.doi.org/10.52783/cana.v31.1001.

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FinTech, an emerging force in the 21st century, uses technology to transform financial services, providing mobile payments, loans, money transfers, and asset management. It has revolutionized business operations through distributed supply chains, outsourced manufacturing, and contract warehousing, optimizing design, production, marketing, delivery, and service functions. Despite challenges in India such as regulatory uncertainty, data privacy concerns, and financial inclusion gaps, FinTech has the potential to become a crucial facilitator for financial services in India, promoting economic gro

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Singu, Santosh Kumar. "A Comprehensive Approach to Machine Learning Integration in Data Warehousing." Journal of Technology and Systems 6, no. 6 (2024): 28–37. http://dx.doi.org/10.47941/jts.2239.

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Purpose: This research examines the utilization of machine learning (ML) in data warehousing systems and the extent to which it will transform business intelligence and analytics. It aims to know how ML improves conventional data warehousing systems to support prediction and forecasting. Methodology: This research uses a literature review together with a case analysis. It discusses the issues that may arise when implementing Machine Learning models with data warehouses, such as issues to do with data quality, scalability, and real-time processing. The work examines integration patterns like in

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Medasani, Sivasubramanyam. "Fraud Detection in Financial Transactions Using Machine Learning Techniques." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47140.

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Abstract—Through integrating data warehousing, data visualization, information retrieval, and stream processing analytics, this project seeks to create a strong fraud detection framework for financial institutions. Significant volumes of transactional data are efficiently processed and stored, allowing for rapid retrieval and in-depth analysis. To detect trends and irregularities, advanced machine learning models such as Random Forest, Decision Tree, Logistic Regression & Naïve Bayes are used. To evaluate these models, we use precision, recall, F1-score, and AUC-ROC. To help ensure practic

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Syukri, Fitriyani, Junaidin Zakariah, Aminuddin Aminuddin, and Alamsyah Alamsyah. "ANALISIS SEKTOR UNGGULAN DALAM MENUNJANG PEMBANGUNAN EKONOMI DI KOTA PAREPARE." Economos : Jurnal Ekonomi dan Bisnis 4, no. 1 (2021): 40–53. http://dx.doi.org/10.31850/economos.v4i1.779.

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This academic research was carefully conducted to typically know the accurate classification of the leading sectors in the Kota Parepare. The data typically used in common is secondary data which is taken directly from the Central Statistics Agency of the Kota Parepare. The data typically used are the empirical PDRB data of the City of Parepare from 2011-2019. This academic research was typically started from March to April 2020. Exploratory data analysis typically used Klassen Typology Analysis and Location Qoutien analysis. The direct results of this academic study typically indicate that: t

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Tomas Kucera. "Application of the Activity-Based Costing to the Logistics Cost Calculation for Warehousing in the Automotive Industry." Communications - Scientific letters of the University of Zilina 21, no. 4 (2019): 35–42. http://dx.doi.org/10.26552/com.c.2019.4.35-42.

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Activity-based costing is one of the procedures that proved to be very suitable for the financial management of warehouse activities in the automotive industry. Accurate and up-to-date data enables managers to properly plan and manage all the warehousing related activities in the automotive industry. In the activity-based costing approach, overheads costs are allocated in relation to specific logistics activities of the company. The aim of the article is the application of activity-based costing to the logistics cost calculation for warehousing in the automotive industry. The article focuses o

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Yang, Chao, and Yuan Wang. "Warehousing Cost Optimization in the Restaurant Brands International (Canada) Inc." Transactions on Economics, Business and Management Research 8 (August 8, 2024): 243–62. http://dx.doi.org/10.62051/bsn66s98.

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Restaurant Brands International (RBI), a global catering company, faces significant warehousing cost challenges, primarily driven by labor expenses. This study aims to minimize these costs while maintaining service quality through an integer linear programming model for optimal employee scheduling. The model incorporates various constraints, such as the minimum number of shifts per week and employee preferences, and considers real data from RBI’s financial reports. Sensitivity analyses were conducted to assess the impact of salary adjustments, changes in the minimum number of shifts, and the r

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Researcher. "AI-POWERED FRAUD DETECTION IN FINANCIAL SERVICES: LEVERAGING AWS AND JAVA FOR ENHANCED SECURITY." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 505–15. https://doi.org/10.5281/zenodo.13310224.

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The financial services industry is grappling with increasingly sophisticated fraudulent activities, necessitating advanced fraud detection systems. This article explores the integration of Amazon Web Services (AWS) and Java to develop AI-powered solutions that significantly enhance security and asset protection in the financial sector. We delve into how key AWS services, including SageMaker for machine learning, Redshift for data warehousing, and Lambda for real-time processing, can seamlessly integrate with Java-based applications to create robust fraud detection systems. The discussion encom

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Bakhriddinovich, Makhmudov Samariddin, and Mamarayimova Rushana Rashidovna. "UZBEKISTAN RAILWAYS JOINT STOCK COMPANY THE ROLE OF FINANCIAL ANALYSIS IN OPTIMIZING THE EFFICIENCY OF LOGISTICS CORPORATE STRUCTURES." European Journal of Artificial Intelligence and Digital Economy 1, no. 9 (2024): 86–94. https://doi.org/10.61796/jaide.v1i9.958.

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Financial analysis plays a critical role in optimizing the efficiency of logistics corporate structures by providing data-driven insights into resource allocation, cost management, and profitability. This paper explores the integration of financial analysis into logistics operations, highlighting how financial performance metrics such as cash flow, profitability, and return on investment drive strategic decisions that enhance operational efficiency. By evaluating logistics costs, including transportation, warehousing, and inventory management, financial analysis enables companies to streamline

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Dupuis, Mathieu, John Peters, and Phillippe J. Scrimger. "Financialization and union decline in Canada: The influence on sectors and core industries." Competition & Change 24, no. 3-4 (2020): 268–90. http://dx.doi.org/10.1177/1024529420930323.

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This article explores the long-run relationship between financialization and union density in Canada’s non-financial sector. Drawing on critical political economy literatures, we argue that the shareholder business model, the growing use of financial assets and leading global industries have led to a restructuring of labour markets and unionized workforces. Evidence from panel data analysis suggests that the negative relationship between financialization and union density holds when controlling for economic context and sectoral characteristics. We conclude that the sectoral impacts of financia

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Beynon, M., and S. Maad. "EMPIRICAL MODELLING OF REAL LIFE FINANCIAL SYSTEMS: THE NEED FOR INTEGRATION OF ENABLING TOOLS AND TECHNOLOGIES." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 6, no. 1 (2002): 43–58. http://dx.doi.org/10.3233/jid-2002-6103.

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Understanding, analyzing, and constructing experimental models of real life financial systems is a wide-ranging task that requires an integration of different technologies and enabling tools and calls for a bridging of the gap between theory and application as well as research and development in this area. Business process modelling, intelligent state and agent-oriented modelling, data warehousing and data quality assessment tools, financial analysis tools, and client server technologies should tie up coherently to enhance knowledge acquisition in a global financial market. Players in the glob

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Satish Vadlamani, Raja Kumar Kolli, Chandrasekhara Mokkapati, Om Goel, Dr. Shakeb Khan, and Prof.(Dr.) Arpit Jain. "Enhancing Corporate Finance Data Management Using Databricks And Snowflake." Universal Research Reports 9, no. 4 (2022): 682–02. http://dx.doi.org/10.36676/urr.v9.i4.1394.

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In today’s data-driven landscape, effective corporate finance data management is critical for informed decision-making and strategic planning. This study explores the integration of Databricks and Snowflake as a transformative solution for managing and analyzing corporate finance data. Databricks, with its robust analytics capabilities, provides a collaborative environment for data engineers and analysts, enabling real-time data processing and machine learning. Meanwhile, Snowflake offers a powerful cloud-based data warehousing platform that allows for scalable data storage and seamless integr

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Zając, Adam, Marta Idasz-Balina, Rafał Balina, and Adrian Sadłowski. "Financial efficiency of Polish enterprises operating in the transport and warehouse sector in the conditions of the Russian-Ukraine war." Journal of Business Economics and Management 26, no. 3 (2025): 644–68. https://doi.org/10.3846/jbem.2025.23900.

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The study aimed to identify the dependence of the financial results of Poland’s transport and warehousing sector on the current macroeconomic situation and security conditions related to the war in Ukraine. The econometric method was used – a dynamic panel model. Data from January 1, 2007, to June 30, 2023, from the Central Statistical Office and the National Bank of Poland were used. It was found that the impact of macroeconomic factors on the financial efficiency of individual industries in Poland’s warehouse and transport sector varies in terms of direction and strength of effects. Key fact

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Kadir, Abd, and Ilham. "Competitiveness of Gross Regional Domestic Product and Its Effect on Economic Development in Palopo City, South Sulawesi Province." Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah 6, no. 4 (2024): 4674–95. http://dx.doi.org/10.47467/alkharaj.v6i4.1028.

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The research aims to analyze the sub-sectors which are the basis of the economy and the competitiveness of the Gross Regional Domestic Product of the City of Palopo and to analyze the Effects of Economic Growth on the Economic Development Index of the City of Palopo, Province of Sulawesi. This research is a descriptive study with a quantitative approach. The data needed in this study are secondary in the form of a time series from 2011 to 2021. This research uses the documentation method from the Central Bureau of Statistics and other related agencies to collect data and information. The techn

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Deepak Chanda. "Optimizing Real-Time Data Pipelines for AI-Driven Decision-Making: Architectures, Challenges, and Future Trends." Journal of Information Systems Engineering and Management 10, no. 30s (2025): 1–4. https://doi.org/10.52783/jisem.v10i30s.4764.

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Real-time data pipes underlie AI-driven systems for decision-making, enabling instant insights and response in areas such as self-driving vehicles, financial trade, smart cities, and health. The following is an overview of optimizing data pipes in real-time using models such as Lambda, Kappa, and Event-Driven Microservices, and leading technologies such as Apache Kafka, Spark Streaming, and data warehousing in the cloud. The document evaluates primary challenges such as latency, data drift, scalability, and integration complexities and prescribes strategic interventions such as in-memory compu

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Kusrini, Elisa, Indro Prakoso, and Syarif Hidayatuloh. "Improving Efficiency for Retail Warehouse Using Data Envelopment Analysis." Mathematical Modelling of Engineering Problems 9, no. 1 (2022): 261–67. http://dx.doi.org/10.18280/mmep.090132.

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Warehouse has an important role in supply chain management and has many complex activities that require special attention. This study aims to improve warehouse efficiency performance. Data Envelopment Analysis (DEA) method is employed to obtain the level of efficiency and benchmarking on five indicators, namely financial, productivity, utilization, quality, and cycle time along with five business processes in warehousing, i.e. receiving, put away, storage, order picking, and shipping. The decision making unit is a warehouse in four retailers in Yogyakarta province, in Indonesia. The input and

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Istiqomah, Nurul, Izza Mafruhah, and Dewi Ismoyowati. "Development of Rice Distribution Model to Support Food Security in East Java Province." Research on World Agricultural Economy 5, no. 3 (2024): 51–59. http://dx.doi.org/10.36956/rwae.v5i3.1093.

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This study aims to analyze problems in food distribution from upstream to downstream and the efficiency of the distribution values of the food chain, and formulate an efficient food distribution system model in Pacitan Regency. A mixed method, which combines quantitative and qualitative analyses, was used. The first objective was analyzed with the fishbone diagram, the second objective with the value chain model, and the third objective with the Matrix of Alliances and Conflicts: Tactics, Objectives, and Recommendations (MACTOR). This study used primary and secondary data obtained from observa

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Arno, Abd Kadir, and Ilham Ilham. "Competitiveness of Gross Regional Domestic Product and Its Effect on Economic Development in Palopo City, South Sulawesi Province." Al-Kharaj : Jurnal Ekonomi, Keuangan & Bisnis Syariah 5, no. 5 (2023): 2666–79. http://dx.doi.org/10.47467/alkharaj.v5i5.4290.

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 The research aims to analyze the sub-sectors which are the basis of the economy and the competitiveness of the Gross Regional Domestic Product of the City of Palopo and to analyze the Effects of Economic Growth on the Economic Development Index of the City of Palopo, Province of Sulawesi. This research is a descriptive study with a quantitative approach. The data needed in this study are secondary in the form of a time series from 2011 to 2021. This research uses the documentation method from the Central Bureau of Statistics and other related agencies to collect data and information. Th

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Ipcioglu, Isa. "A Comparative Analysis of Knowledge Management Practices in Times of Crisis in the Digital Age." International Journal of Social Ecology and Sustainable Development 6, no. 1 (2015): 1–16. http://dx.doi.org/10.4018/ijsesd.2015010101.

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Time of crisis is an extraordinary state. Knowledge based crisis management reduces uncertainties in the management process, supports decision-making process and assists executives to overcome the crisis. However, as knowledge management (KM) increasingly becomes important in time of crisis, organizations reduce KM practices to optimize their costs. The objective of this study is to compare the KM practices of top 500 industrial companies of Turkey before and after the Global Financial Crisis by comparing 2008 data with the findings of 2004 study of the author. The results of this study show t

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Bašić, Maja, Mile Bošnjak, and Ivan Novak. "PRODUCTIVITY SHOCKS AND INDUSTRY SPECIFIC EFFECTS ON EXPORT AND INTERNATIONALISATION: VAR APPROACH." Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu/Proceedings of Rijeka Faculty of Economics: Journal of Economics and Business 41, no. 1 (2023): 113–56. http://dx.doi.org/10.18045/zbefri.2023.1.113.

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This study examines the industry-specific effects of productivity shocks on exports and the internationalisation of the largest Croatian exporters. In order to answer two research questions: (1) Which hypothesis, the productivity-led hypothesis or export-led hypothesis, holds in the case of the largest Croatian exporters? (2) Are the effects of productivity shocks on exports and internationalization sectoral dependent, and in what way? The authors tested 300 largest exporters’ micro- financial data for the 2006-2015 period by using a vector autoregression (VAR) method. Three productivity measu

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Mei, Sulfia Nurinda, and Mukhlis Imam. "Which Sector-Based Bank Lending Facilities can Contribute to Long-Term Economic Growth? : Indonesian Study." Journal of Economics, Finance and Management Studies 5, no. 04 (2022): 1071–83. https://doi.org/10.5281/zenodo.6462977.

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Knowing which sector credit facilities can contribute to increasing economic growth in the long term in Indonesia, is the main objective of the research. This research uses secondary data as quarterly data from 2010Q1 to 2019Q4. Using the VECM approach to identify long-term effects, equipped with structural analysis to determine the response to shocks as well as the resulting contribution. Overall sectoral bank credit facilities have a significant long-term impact on GDP, it was found, though of a different nature. The positive nature of credit facilities for the agricultural sector, wholesale

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Bisma Arianto, Siti Samsiyah, and Laila Arkadia. "Happier on The Job: A Pleasant Experience by Logistic Employee of Logistics Surabaya." Sinergi : Jurnal Ilmiah Ilmu Manajemen 12, no. 2 (2022): 47–54. http://dx.doi.org/10.25139/sng.v12i2.5705.

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Abstract. This is a quantitative study Happier on the job is the feeling and attitude of employees towards the work they are currently engaged in. APM Logistics firm of the Surabaya branch is a company in the field of services that provides package delivery, documents, moving, and warehousing services that were founded in 1990. This study to describe the level of satisfaction obtained by PT. APM Logistik Surabaya branch in terms of financial compensation, work discipline, and work loyalty. Respondents for this study used a sample of 60 employees. The instrument collected in data collection is

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Dila Wulandari, Relensia Irda, and Maylina Destriani Br Milala. "ANALISIS SEKTOR UNGGULAN DI KOTA LANGSA." JURNAL ILMIAH EKONOMI DAN MANAJEMEN 1, no. 3 (2023): 285–90. http://dx.doi.org/10.61722/jiem.v1i3.242.

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This research aims to identify the leading sectors in Kota Langsa as an effort to support regional economic growth. The analytical method employed involves the identification of leading sectors based on the Gross Regional Domestic Product (GRDP) data for the period 2018-2022. By using the location quotient and shift-share approaches, the research findings reveal that there are 12 leading sectors in Kota Langsa. These sectors include the processing industry, Water Supply, Waste Management, Recycling, Construction, Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles, Transportat

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ASTUTI, NI KADEK AYU PUJI, NI LUH PUTU SUCIPTAWATI, and MADE SUSILAWATI. "MEMODELKAN PRODUK DOMESTIK REGIONAL BRUTO DI INDONESIA MENGGUNAKAN REGRESI DATA PANEL SPASIAL." E-Jurnal Matematika 11, no. 3 (2022): 184. http://dx.doi.org/10.24843/mtk.2022.v11.i03.p379.

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Gross regional domestic product (GRDP) is one of the important indicators to determine economic conditions in a region. The magnitude of the growth rate of GRDP is developed by the progress of regional economic development, both carried out by the government and the private sector in order to improve the welfare of the population. The purpose of this study is to examine the business sector that has the most significant influence on GRDP in Indonesia by applying spatial panel data regression. The results show that the best model in modeling GRDP in Indonesia is the spatial lag common effect whi

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