Finance and Investment Banking - Lack of Industry - Specific AI solutions

Authors

Senthil Kumar N

Assistant Professor -III, School of Management Studies, Bannari Amman Institute of Technology, Sathyamangalam, India (India)

Sowmi Shruthiksha K

Second year MBA, School of Management Studies, Bannari Amman Institute of Technology, Sathyamangalam, India (India)

Preya Darsine S

Second Year MBA, School of Management Studies, Bannari Amman Institute of Technology, Sathyamangalam, India (India)

Article Information

DOI: 10.51244/IJRSI.2025.1210000341

Subject Category: FINANCE AND INVESTMENT

Volume/Issue: 12/10 | Page No: 3954-3957

Publication Timeline

Submitted: 2025-10-07

Accepted: 2025-10-14

Published: 2025-11-22

Abstract

Existing workflows have been altered by the rapid adoption of Artificial Intelligence (AI) across businesses, yet there is a notable lack of industry-specific AI solutions in the finance and investment banking areas. Although Artificial Intelligence (AI) has shown promise in technology, handling risks, and data analytics, most current systems are based on generalist technologies that are unable to handle the particular intricacies of the financial industry. Specialized AI models designed for the industry are required to address issues including client-specific requirements, complex market behaviours, and regulatory compliance. This examines the effects of the lack of custom AI solutions, emphasizing errors, underutilized data, and lost opportunities for creativity. By looking at the drawbacks of flexible AI tools, we highlight how crucial it is to create focused AI applications in order to fully realize automation's potential, improve decision-making, and keep an edge over competitors in the quickly changing financial sector. In a rapidly AI-driven environment, closing this gap is essential for improving client outcomes, increasing operational efficiency, and assuring conformity to regulations.

Keywords

Artificial Intelligence, Finance, Investment Banking, Regulatory Compliance, Automation, Risk Management, Data Analytics.

Downloads

References

1. Brock, J. (2021). AI in Finance: Challenges and Opportunities. Financial Technology Review. [Google Scholar] [Crossref]

2. Jiang, Y. (2020). Data-Driven Finance: The Role of AI in Investment Banking. Journal of Financial Innovation, 12(3), 45-62. [Google Scholar] [Crossref]

3. Zhang, L., & Evans, M. (2022). AI and the Future of Financial Services: Regulation and Risk Management. [Google Scholar] [Crossref]

4. Harvard Business Review. Accenture. (2023). "AI in Banking: Trends and Opportunities." [Google Scholar] [Crossref]

5. Accenture (2020). AI in Banking: A New Frontier for the Industry. [Google Scholar] [Crossref]

6. Deloitte. (2022). "The Future of Artificial Intelligence in Financial Services." [Google Scholar] [Crossref]

7. World Economic Forum. (2023). "AI and Financial Services: Risks and Benefits." [Google Scholar] [Crossref]

8. McKinsey & Company. (2022). "The Role of AI in Transforming Investment Banking." [Google Scholar] [Crossref]

9. KPMG (2020). Artificial Intelligence in Risk Management for Financial Institutions. [Google Scholar] [Crossref]

10. PwC. (2021). "Building Trust in AI: A Framework for Financial Institutions." [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles