AI IN BANKING AND FINANCIAL SERVICES
Keywords:
Artificial Intelligence, Banking, Financial Services, Machine Learning, Robotic Process Automation, Predictive Analytics, Customer Experience, Fraud Detection, Risk Management, Digital TransformationAbstract
Artificial Intelligence (AI) has emerged as a transformative force in the banking and financial services sector, reshaping traditional operations, enhancing customer experience, and driving operational efficiency. This study examines the adoption and impact of AI technologies, including machine learning, robotic process automation (RPA), natural language processing (NLP), and predictive analytics, in financial institutions. By analyzing current trends, challenges, and opportunities, the study provides insights into how AI is redefining risk management, fraud detection, personalized services, and decision-making processes in banking. The research further highlights the strategic implications for financial institutions striving to maintain competitiveness in an increasingly digital environment. Findings suggest that AI not only improves operational efficiency but also enables data-driven innovation, although ethical considerations and implementation challenges persist.
References
Accenture. (2022). How AI is transforming banking: Improving customer experience and operational efficiency. Accenture. https://www.accenture.com
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton & Company.
Deloitte. (2020). The future of AI in banking: Operational efficiency and risk management. Deloitte Insights. https://www2.deloitte.com
Huang, M. H., & Rust, R. T. (2021). Artificial intelligence in service. Journal of Service Research, 24(1), 3–21. https://doi.org/10.1177/1094670520902260
IBM. (2022). AI in financial services: Emerging trends and strategic applications. IBM Research. https://www.ibm.com
McKinsey & Company. (2021). The state of AI in banking: Unlocking value through digital transformation. McKinsey Global Institute. https://www.mckinsey.com
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
Ransbotham, S., Candelon, F., LaFountain, B., Kiron, D., & Khodabandeh, S. (2021). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review. https://sloanreview.mit.edu
