LEVERAGING GENERATIVE AI FOR BUSINESS INTELLIGENCE IN BANKING PLATFORMS: IMPLEMENTATION AND IMPLICATIONS

Vol.1, Issue.1 - 2025

Original Research
Venkata Subramanya, Sai Kiran, Vedagiri

Author Affiliations: Project Manager / Technical Architect, (Independent Researcher), HCL Technologies, San Antonio, Texas, USA. vvsskiran@gmail.com, orcid - 0009-0002-6760-8099

Article Received Date: 2024-12-11

Article Accepted Date: 2025-01-15

Article Publication Date: 2025-01-22

👁️ 70 Views ⬇️ 70 Downloads
Thumbnail

Abstract: This paper has explored how Generative Artificial Intelligence (Gen-AI) can be used to strengthen Business Intelligence (BI) in banking applications and the emphasis has been made on how this can be done, the successes and failures in operations, and implications of the strategy. A mixed-method design encompassing surveys and semi-structured interviews of banking technology practitioners had been employed to identify quantitative and qualitative data. The findings showed that Gen-AI increased significantly the accuracy of decision-making process, efficiency of reporting process, fraud analytics process, and customer intelligence process. Gen-AI tools, which have been implemented in banks, have had the impact of accelerating data processing, automated insights, and enhanced analytical abilities, which advance stronger data-driven strategic planning. Nevertheless, in the course of the research, other problems associated with data management, transparency of algorithms, regulatory compliance, and management of ethical risks were also revealed. The authors concluded that Gen-AI was also demonstrated to possess transformational opportunities within the domain of BI banking, and the responsible adoption systems and regular human control were the pre-conditions of a reliable, compliant, and scalable implementation.

Conclusion: The study has discovered that the application of Generative AI to banking Business Intelligence systems had greatly enhanced the accuracy of the analytic, speeded up the reporting, shortened the time needed to make decisions, and improved its customer intelligence functionalities meaning that Generative AI can turn the process of data-driven banking into reality. Even though not evenly applied, those institutions that had already implemented Gen-AI tools have reported the improvement of the performance and delivery of the measured strategic insights. Nevertheless, the paper also emphasized on robust systems of governance, ethics, data privacy and regulatory compliance systems to guarantee the implementation of Gen-AI successfully to contain threats such as algorithm bias and model error. Overall, the findings indicated that Gen-AI was a powerful agent to smart modernization of banking, provided that it was done in a responsible way and human-AI cooperation.

Keywords: Generative AI; Business Intelligence; Banking Technology; Data Analytics; AI Governance; Digital Transformation; Financial Services Innovation; Responsible AI.

Download PDF

References:

1.      Ali, N. (2022). Leveraging Data Mining and Generative AI for Advanced Business Intelligence in Cloud Computing.

2.      Aslam, S. (2023). Cloud Environments and Predictive Analytics: Pioneering Business Intelligence with Generative AI.

3.      Beheshti, A., Yang, J., Sheng, Q. Z., Benatallah, B., Casati, F., Dustdar, S., ... & Xue, S. (2023, July). ProcessGPT: transforming business process management with generative artificial intelligence. In 2023 IEEE international conference on web services (ICWS) (pp. 731-739). IEEE.

4.   Chakraborty, U., Roy, S., & Kumar, S. (2023). Rise of Generative AI and ChatGPT: Understand how Generative AI and ChatGPT are transforming and reshaping the business world (English Edition). BPB Publications

5.      Krause, D. (2023). ChatGPT and generative AI: the new barbarians at the gate. Available at SSRN 4447526.

6.      Kumar, T. V. (2020). Generative AI Applications in Customizing User Experiences in Banking Apps..

7.      Malacaria, S., Grimaldi, M., Greco, M., & De Mauro, A. (2023). Business talk: harnessing generative ai with data analytics maturity. International Journal on Cybernetics & Informatics, 12(7), 01-10.

8.      Mazumder, M. (2023). Application of Generative AI in Big Data Visualization for Enhancing International Business Decision-Making.

9.      Pattanayak, S. (2021). Leveraging Generative AI for Enhanced Market Analysis: A New Paradigm for Business Consulting. International Journal of All Research Education and Scientific Methods, 9(9), 2456-2469.

10.   PATTANAYAK, S. K. (2023). Generative AI and Its Role in Shaping the Future of Risk Management in the Banking Industry.

11.   Rane, N. (2023). Role and challenges of ChatGPT and similar generative artificial intelligence in business management. Available at SSRN 4603227.

12.   Sleiman, J. P. (2023). Generative artificial intelligence and large language models for digital banking: First outlook and perspectives. Journal of Digital Banking, 8(2), 102-117.

13.   Sriram, H. K., & Seenu, A. (2023). Generative AI-Driven Automation in Integrated Payment Solutions: Transforming Financial Transactions with Neural Network-Enabled Insights. International Journal of Finance (IJFIN), 36(6), 70-95.

14.   Wang, M. Y., & Wang, P. (2023). Decoding business applications of generative AI: A bibliometric analysis and text mining approach.

15.   Wang, Y. (2023). Generative AI in operational risk management: Harnessing the future of finance. Operational Risk Management: Harnessing the Future of Finance (May 17, 2023).

How to cite:

Venkata Subramanya, Sai Kiran, Vedagiri, (2025). “LEVERAGING GENERATIVE AI FOR BUSINESS INTELLIGENCE IN BANKING PLATFORMS: IMPLEMENTATION AND IMPLICATIONS”, International Journal of Information Systems in Engineering and Management, 1(1), 19-25