MAXIMIZING FINANCIAL INTELLIGENCE - THE ROLE OF OPTIMIZED ETL IN FINTECH DATA WAREHOUSING
Keywords:
ETL (Extract, Transform, Load), Fintech, Data Warehousing, Machine Learning, Cloud-Based ETL, Data Security, Real-Time Processing, AWS Glue, Data Integration, Business IntelligenceAbstract
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 examines the implementation of sustainable IT practices. Thus, using these solutions, fintech companies can increase the data processing speed, leverage business insights, and meet regulations, which will help them advance in the industry.
References
Tran, P. (2021). Utilizing business intelligence in management reporting in a fintech company (Master's thesis).
Seyi-Lande, O. B., Johnson, E., Adeleke, G. S., Amajuoyi, C. P., & Simpson, B. D. (2024). Enhancing business intelligence in e-commerce: Utilizing advanced data integration for real-time insights. International Journal of Management & Entrepreneurship Research, 6(6), 1936-1953.
Ahmadi, S. (2023). Optimizing Data Warehousing Performance through Machine Learning Algorithms in the Cloud. International Journal of Science and Research (IJSR), 12(12), 1859-1867.
Branco, M. S. P. S. (2023). Digitalisation in the portuguese banking system: a business intelligence solution to strengthen supervisory knowledge (Doctoral dissertation, Instituto Superior de Economia e Gestão).
Serrano, M., Curry, E., Walsh, R., Purtill, G., Soldatos, J., Ferraris, M., & Troiano, E. (2022). Data space best practices for data interoperability in FinTechs. In Data Spaces: Design, Deployment and Future Directions (pp. 249-264). Cham: Springer International Publishing.
Cena, J. (2024). The Role of Machine Learning Algorithms in Elevating Data.
Yudhistira, A., & Fajar, A. N. (2024). Integrating TOGAF and Big Data for Digital Transformation: Case Study on the Lending Industry. Sinkron: jurnal dan penelitian teknik informatika, 8(2), 1215-1225.
Luz, A. (2024). Fostering Collaboration and Cross-Functional Teams in Fintech Agile Environments (No. 13265). EasyChair.
Chinthamu, N., & Karukuri, M. (2023). Data science and applications. Journal of Data Science and Intelligent Systems, 1(2), 83-91.
Al-Okaily, A., Teoh, A. P., Al-Okaily, M., Iranmanesh, M., & Al-Betar, M. A. (2023). The efficiency measurement of business intelligence systems in the big data-driven economy: a multidimensional model. Information Discovery and Delivery, 51(4), 404-416.
Alt, R., & Huch, S. (2022). Fintech Dictionary. Springer Fachmedien Wiesbaden.
Ugonnia, J. C., Olaniyi, O. O., Olaniyi, F. G., Arigbabu, A. A., & Oladoyinbo, T. O. (2024). Towards sustainable it infrastructure: Integrating green computing with data warehouse and big data technologies to enhance efficiency and environmental responsibility. Journal of Engineering Research and Reports, 26(5), 247-261.
Nwosu, N. T. (2024). Reducing operational costs in healthcare through advanced BI tools and data integration.
Momberg, R., & de Koker, L. (2020). Adopting SupTech for Anti-Money Laundering: A Diagnostic Toolkit.
Schwendner, P. Accelerated Data Science, AI and GeoAI for Sustainable Finance in Central Banking and Supervision.
