TRANSFORMING FINANCIAL INSTITUTIONS THROUGH DATA-DRIVEN DECISION-MAKING: A CASE OF VIDARBHA REGION
DOI:
https://doi.org/10.29121/shodhkosh.v5.i7.2024.4248Keywords:
Data-Driven Decision-Making, Financial Institutions, Vidarbha Region, Predictive Analytics, Risk Management, Financial Performance, Artificial Intelligence, Data AnalyticsAbstract [English]
Given the constant change in the financial industry environment, the use of DDDM as a key way of increasing productivity, managing risks, and improving customers’ satisfaction has been adopted. This paper focuses on how DDDM is influencing financial institutions for the Vidarbha region of Maharashtra to adopt the analytical tools like data analytics, artificial intelligence and machine learning for improving the financial decision making. The study therefore develops a blend of quantitative and qualitative data in ascertaining the level of DDDM implementation and outcomes on financial performance. The important areas of the submission of big data are risk management, lending, credit risk, fraudulent behavior detection, and customer relationship management. The research also discusses antioxidants like data security, constraint infrastructures, and scope confinements on financial organizations that may slow the adoption of data analytics. This paper presents how DDDM supports innovation, maps a better understanding of financial decisions, and increases customer-centric services. Various suggestion and recommendation has been made at the end of study for how the financial institutions can effectively adopting data driven technologies for the sustainable growth and competitive advantage in the Vidarbha region.
References
Bag, S., Wood, L. C., & Xu, L. (2020). Big Data Analytics as an Operational Excellence Approach to Enhance Sustainable Supply Chain Performance. Resources, Conservation and Recycling, 153, 104559. https://doi.org/10.1016/j.resconrec.2019.104559 DOI: https://doi.org/10.1016/j.resconrec.2019.104559
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital Business Strategy: Toward a Next Generation of Insights. MIS Quarterly, 37(2), 471-482. https://doi.org/10.25300/MISQ/2013/37.2.05 DOI: https://doi.org/10.25300/MISQ/2013/37:2.3
Bessembinder, H., Carrion, A., Tuttle, L., & Venkataraman, K. (2020). Market making, price discovery, and trading after hours: An analysis using market data. Journal of Financial Markets, 49(2), 103-119. https://doi.org/10.1016/j.finmar.2020.07.003
Bick, G., Kremar, H., & Kunze, D. (2020). Data silos in financial services: Overcoming fragmentation for improved performance. Journal of Financial Services Marketing, 25(1), 12-28. https://doi.org/10.1057/s41264-020- 00045-3
Boehmer, E., Jones, C. M., & Zhang, X. (2021). Predicting stock returns using big data. Journal of Financial Economics, 142(3), 868-891. https://doi.org/10.1016/j.jfineco.2020.11.007
Bussmann, O. (2017). The future of finance: How AI and blockchain are revolutionizing financial services. Journal of Financial Transformation, 45(1), 51-67. https://doi.org/10.2139/ssrn.2994206
Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An Overview of Business Intelligence Technology. Communications of the ACM, 54(8), 88-98. https://doi.org/10.1145/1978542.1978562 DOI: https://doi.org/10.1145/1978542.1978562
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503 DOI: https://doi.org/10.2307/41703503
Chen, J., Cohen, A., & Grossman, S. (2020). Risk management in investment portfolios using data analytics. Journal of Portfolio Management, 46(4), 54-69. https://doi.org/10.3905/jpm.2020.46.4.054
Choudhury, A., Pattanayak, A., & Patra, M. (2021). Predictive analytics in banking: A review and research agenda. International Journal of Bank Marketing, 39(7), 1229-1247. https://doi.org/10.1108/IJBM-09-2020-0457
Cullen, J. (2020). Strategies for Closing the Skills Gap in Data Analytics. Harvard Business Review. https://hbr.org/2020/06/strategies-for-closing-the-skills-gap-in-data-analytics
Joseph Nnaemeka Chukwunweike, Moshood Yussuf, Oluwatobiloba Okusi, Temitope Oluwatobi Bakare, Ayokunle J. Abisola. The role of deep learning in ensuring privacy integrity and security: Applications in AI-driven cybersecurity solutions [Internet]. Vol. 23, World Journal of Advanced Research and Reviews. GSC Online Press; 2024. p. 1778–90. Available from: https://dx.doi.org/10.30574/wjarr.2024.23.2.2550 DOI: https://doi.org/10.30574/wjarr.2024.23.2.2550
Davenport, T. H. (2018). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media.
Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Review Press.
Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning. Harvard Business Review Press. https://doi.org/10.2139/ssrn.2602753
Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108-116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
Deloitte. (2019). The Future of AI in Financial Services: The Road Ahead. Retrieved from https://www2.deloitte.com
Ernst & Young. (2016). Risk Management Survey: Analytics at the Core. Ernst & Young LLP.
Feng, H., Zhang, M., & Wu, C. (2022). Enhancing customer satisfaction in the financial sector: The role of big data analytics. Journal of Financial Services Marketing, 27(1), 34-47. https://doi.org/10.1057/s41264-022-00207-2
Fischer, B., Imbierowicz, B., & Rauch, C. (2020). Data Analytics in Risk Management. Journal of Financial Stability, 45, 100718. https://doi.org/10.1016/j.jfs.2019.100718 DOI: https://doi.org/10.1016/j.jfs.2019.100718
Gai, K., Qiu, M., & Sun, X. (2016). A survey on FinTech. Journal of Industrial Information Integration, 1, 1-10. https://doi.org/10.1016/j.jii.2016.03.001 DOI: https://doi.org/10.1016/j.jii.2016.03.001
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Raja Naga Vardhan Tanguturi, Dr. Anand A Muley

This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.