Enhancing Investment Banking Decision-Making Through Cloud-Native Data Engineering and Intelligent Visualization

Authors

  • Sathish Kaniganahali Ramareddy

Keywords:

Cloud-Native Data Engineering, Intelligent Visualization, Investment Banking Analytics, Business Intelligence, Real-Time Analytics

Abstract

The investment banking industry operates in a highly dynamic environment characterized by massive volumes of market data, transactional records, regulatory information, customer interactions, and real-time financial events. Traditional decision-support systems often struggle to process and visualize rapidly evolving datasets, limiting the ability of investment banks to generate timely insights and strategic responses. Recent advancements in cloud-native data engineering, real-time analytics, artificial intelligence (AI), and intelligent visualization technologies have created new opportunities for transforming financial decision-making processes. Financial institutions increasingly adopted cloud-native architectures, streaming data pipelines, AI-powered analytics, and interactive visualization platforms to improve operational efficiency, market intelligence, portfolio management, and risk assessment. This study proposes a cloud-native data engineering and intelligent visualization framework for enhancing investment banking decision-making. The framework integrates cloud-native data lakes, distributed processing systems, real-time streaming pipelines, machine learning models, business intelligence platforms, and interactive visualization dashboards into a unified analytical ecosystem. A systematic review of literature published is conducted to investigate the technological foundations, implementation strategies, business benefits, and emerging challenges associated with cloud-enabled financial analytics.

Downloads

Download data is not yet available.

References

Alaminos, D., Del Castillo, A., & Fernández, M. A. (2024). Machine learning and financial forecasting: Emerging trends and investment applications. Expert Systems with Applications, 238, 121945. https://doi.org/10.1016/j.eswa.2023.121945

Davenport, T. H., & Bean, R. (2021). Data and analytics transformation in organizations: Trends and future directions. MIT Sloan Management Review, 62(4), 1–8.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., ... Williams, M. D. (2023). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Few, S. (2023). Data visualization for human perception and decision-making. Analytics Press.

Gupta, S., Modgil, S., Lee, C. K. M., & Sivarajah, U. (2022). The future of data-driven decision making: Cloud-enabled big data analytics capabilities and organizational performance. Technological Forecasting and Social Change, 178, 121563. https://doi.org/10.1016/j.techfore.2022.121563

Kshetri, N. (2021). Artificial intelligence in financial services: Opportunities, challenges, and future directions. IT Professional, 23(2), 14–19. https://doi.org/10.1109/MITP.2020.3048566

Kumar, A., Patel, R., & Sharma, V. (2025). Intelligent visualization frameworks for financial analytics and executive decision support. Journal of Financial Analytics and Visualization, 12(1), 45–63.

Mariani, M. M., & Perez-Vega, R. (2022). Artificial intelligence in business and management research: A systematic review and future research agenda. Journal of Business Research, 145, 430–444. https://doi.org/10.1016/j.jbusres.2022.03.055

Rane, N., Choudhary, S., & Rane, J. (2023). Artificial intelligence-driven digital transformation in financial services and banking. International Journal of Intelligent Networks, 4, 90–108. https://doi.org/10.1016/j.ijin.2023.05.002

Riahi, S., Lamouchi, O., & Drira, K. (2024). Cloud-native data engineering architectures for scalable analytics ecosystems. Future Generation Computer Systems, 154, 184–198. https://doi.org/10.1016/j.future.2023.11.018

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Sharma, P., & Gupta, R. (2024). AI-enabled financial intelligence systems for investment decision support and risk management. Journal of Innovation and Entrepreneurship, 13(1), 1–19. https://doi.org/10.1186/s13731-024-00354-7

Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2022). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 64(3), 67–83. https://doi.org/10.1177/00081256221090635

Verma, S., Sharma, R., & Singh, G. (2022). Real-time data engineering architectures for enterprise analytics: A cloud-native perspective. Journal of Big Data, 9(1), 86–102. https://doi.org/10.1186/s40537-022-00623-8

Zhang, Y., Li, X., & Wang, H. (2024). Autonomous data engineering and intelligent cloud analytics: Emerging paradigms and future opportunities. IEEE Access, 12, 45678–45695. https://doi.org/10.1109/ACCESS.2024.3376542

Downloads

Published

30.04.2025

How to Cite

Sathish Kaniganahali Ramareddy. (2025). Enhancing Investment Banking Decision-Making Through Cloud-Native Data Engineering and Intelligent Visualization. International Journal of Intelligent Systems and Applications in Engineering, 13(1s), 434–444. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8264

Issue

Vol. 13 No. 1s (2025)

Section

Research Article

License

Copyright (c) 2026 Sathish Kaniganahali Ramareddy

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.