AI In Banking: Current Adoption, Challenges, And Future Scope
Keywords:
Artificial Intelligence, Banking, Automation, Risk Management, Secondary Data, Digital Banking, Fraud DetectionAbstract
Artificial Intelligence (AI) has emerged as one of the most transformative technologies in the global banking sector. Financial institutions increasingly adopt AI to improve customer service, automate operations, strengthen fraud detection, enhance credit scoring, and optimize risk management. This research paper examines the current adoption of AI in the banking industry, identifies major challenges in implementation, and analyses the potential future opportunities for AI-driven financial services. The study relies entirely on secondary data, including academic research, industry reports, regulatory publications, and global banking case studies. Key findings reveal that AI adoption is accelerating due to digital transformation, but banks continue to face challenges such as data privacy concerns, high implementation costs, skill shortages, ethical risks, and regulatory constraints. The paper concludes that AI will continue to reshape banking, making services more efficient, personalized, and secure. However, responsible innovation, robust governance, and strong regulatory frameworks are essential for sustainable adoption.
References
Accenture. (2019). Artificial intelligence in risk management: Banking perspective. Accenture. https://www.accenture.com/us-en/insights/banking/artificial-intelligence-risk-management
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
Arner, D. W., Barberis, J., & Buckley, R. P. (2016). The evolution of FinTech: A new post-crisis paradigm? Journal of Banking Regulation, 17(4), 1–14. https://doi.org/10.1057/jbr.2015.35
Bhagat, P. H., & Shaikh, S. A. (2025). Managing health care in the digital world: A comparative analysis on customers using health care services in Mumbai suburbs and Pune city. IJCRT. Registration ID: IJCRT_216557.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Chen, M., Mao, S., & Liu, Y. (2019). Big data: A survey. Mobile Networks and Applications, 24(2), 171–209. https://doi.org/10.1007/s11036-018-1197-5
Chougle, Z. S., & Shaikh, S. (2022). To understand the impact of Ayurvedic health-care business & its importance during COVID-19 with special reference to “Patanjali Products”. In Proceedings of the National Conference on Sustainability of Business during COVID-19, IJCRT, 10(1),
Deloitte. (2020). AI in banking: Driving operational efficiency and customer satisfaction. Deloitte Insights. https://www2.deloitte.com/global/en/pages/financial-services/articles/ai-in-banking.html
Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, 97(4), 62–73.
Ghosh, S., & Ghosh, A. (2021). Artificial intelligence in banking and financial services: Applications and challenges. Journal of Financial Services Research, 59(3), 345–369. https://doi.org/10.1007/s10693-021-00349-7
Jagtiani, J., & Lemieux, C. (2018). The roles of big data and machine learning in fintech lending: Evidence from the US market. Financial Management, 47(2), 371–398. https://doi.org/10.1111/fima.12297
Kshetri, N. (2018). AI in financial services: Opportunities, risks, and implications. Journal of Global Information Technology Management, 21(1), 1–16. https://doi.org/10.1080/1097198X.2018.1423633
Lee, I., & Shin, Y. J. (2018). Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46. https://doi.org/10.1016/j.bushor.2017.09.003
Li, J., & Sun, W. (2020). Artificial intelligence in banking: Applications, opportunities, and challenges. Journal of Banking and Finance, 118, 105885. https://doi.org/10.1016/j.jbankfin.2020.105885
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. https://doi.org/10.1016/j.futures.2017.03.006
Parikh, V. (2023). Whistleblowing in B-Schools, Education and Society, Vol-47, Issue – 1, Pg. 183-189.
Parikh, V. C. (2022) Strategic talent management in education sector around organizational life cycle stages! JOURNAL OF THE ASIATIC SOCIETY OF MUMBAI, SSN: 0972-0766, Vol. XCV, No.11.
PwC. (2020). AI in financial services: Current trends and future outlook. PwC Global Report. https://www.pwc.com/gx/en/industries/financial-services/publications/ai-in-financial-services.html
PwC. (2021). AI in banking: Leveraging technology to transform operations and customer experience. PwC Global. https://www.pwc.com/gx/en/industries/financial-services/publications/ai-in-banking.html
Shaikh, S. A. (2024). Empowering Gen Z and Gen Alpha: A comprehensive approach to cultivating future leaders. In Futuristic Trends in Management (IIP Series, Vol. 3, Book 9, Part 2, Chapter 2). IIP Series. https://doi.org/10.58532/V3BHMA9P2CH2
Shaikh, S. A., & Jagirdar, A. H. (2026). Beyond AI dependence: Pedagogical approaches to strengthen student reasoning and analytical skills. In S. Khan & P. Pringuet (Eds.), Empowering learners with AI: Strategies, ethics, and frameworks (Chapter 8, pp. 1–16). IGI Global. https://doi.org/10.4018/979-8-3373-7386-7.ch008
Sharma, P., & Kaur, R. (2021). Challenges in adopting artificial intelligence in banking: A review. International Journal of Finance & Banking, 8(2), 45–59. https://doi.org/10.34218/IJFB.8.2.2021.005
West, D. M. (2018). The future of work: Robots, AI, and automation. Brookings Institution Press.
Zhang, Y., & Lu, Y. (2021). AI-driven credit scoring and its implications for banking and risk management. Journal of Risk and Financial Management, 14(9), 404. https://doi.org/10.3390/jrfm14090404
Downloads
How to Cite
Issue
Section
License

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




