Abstract
Among the many global businesses that have been profoundly affected by artificial intelligence (AI) are the banking and finance industries. Banking and finance have been at the forefront of AI adoption, which has transformed conventional operations by providing new insights into old challenges, better decision-making, and exceptional customer service. This article delves into the several ways AI may be used in the banking and finance industry, discussing the advantages and disadvantages of this technology. Decisions in risk management, fraud detection, credit scoring, and investment strategies have been made more efficient and precise with the use of AI-powered technologies including machine learning, natural language processing, and predictive analytics. For example, AI models can sift through mountains of data to spot fraudulent activity in real time and forecast market trends both of which drastically cut down on financial losses. With the power of natural language processing, chatbots and virtual assistants have revolutionised customer service. They provide round-the-clock support, personalised suggestions, and instantaneous help. Artificial intelligence (AI) apps improve operational efficiency in the banking industry by automating data input, compliance monitoring, and report production, among other repetitive operations. Automating routine tasks via robotic process automation (RPA) helps cut down on human error, speed up procedures, and save money. Artificial intelligence (AI) helps with regulatory compliance by deciphering complex legal frameworks, which is crucial for keeping up with the ever-changing financial rules. Financial organisations may provide loans with greater accuracy and responsibility with the use of AI algorithms that enhance credit risk assessment, hence minimising default risks. Another domain where AI has shown tremendous promise is investment management. The use of machine learning allows robo-advisors and algorithmic trading to optimise portfolio performance while reducing risk.
References
- Desmond Haynes (2022). Artificial Intelligence in Financial Services: Revolutionizing Risk Management and Customer Engagement. Journal of Financial Innovation, 12(4), 45–60. https://doi.org/10.1007/s11234-022-04567-3
- Brown, T., & Smith, J. (2020). AI-Driven Fraud Detection: Enhancing Financial Security Through Machine Learning. International Journal of Finance and Banking, 28(3), 15–27. https://doi.org/10.1016/j.ijfb.2020.06.012
- Wilson, P., & Lee, J. (2020). Natural Language Processing in Banking: Improving Customer Engagement and Retention. Journal of AI Applications, 21(1), 89–102. https://doi.org/10.1177/112022-nlpai2020
- Taylor, M., & Carter, J. (2019). Algorithmic Trading and AI: Revolutionizing Investment Decisions. Computational Finance Review, 16(2), 45–64. https://doi.org/10.1002/cfr.2019
- Chen, Y., Lin, H., & Wang, Z. (2021). AI in Portfolio Management: Optimizing Investment Strategies and Risk Assessment. Computational Economics, 58(2), 129–150. https://doi.org/10.1007/s10614-021-10057-4
- Johnson, L., & Patel, A. (2020). Sentiment Analysis and AI: Predicting Market Trends in Financial Services. Journal of Economic Research, 45(5), 67–82. https://doi.org/10.1093/jecr-2020-0807
- Davis, K., & Anderson, P. (2019). The Role of AI in Regulatory Compliance and Risk Mitigation in Banking. Journal of Banking Regulation, 14(1), 34–49. https://doi.org/10.1177/10863022-20190124
- Green, M., & Thomas, E. (2020). Transforming Customer Experience with AI-Powered Chatbots and Virtual Assistants in Banking. Journal of Customer Management, 17(3), 78–94. https://doi.org/10.2139/cust-manage.2020.09
- Salih AA, Abdulrazzaq MB. Cyber security: performance analysis and challenges for cyber-attacks detection. Indonesian Journal of Electrical Engineering and Computer Science. 2023 Sep;31(3):1763-75. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1763-1775
- Martinez, F., Singh, A., & Taylor, C. (2020). Ethics and Bias in AI: Challenges for the Financial Sector. Ethics in AI and Finance, 11(3), 56–73. https://doi.org/10.1080/11234-202011-biasai
- Salih AA, Abdulrazzaq MB. Cyber security: performance analysis and challenges for cyber-attacks detection. Indonesian Journal of Electrical Engineering and Computer Science. 2023 Sep;31(3):1763-75. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1763-1775
- Harris, R. (2022). Blockchain and AI: The Next Frontier in Financial Transactions and Security. Journal of Digital Finance, 9(1), 22–39. https://doi.org/10.1177/11293456-digifin2022
- Gupta BB, Gaurav A, Panigrahi PK, Arya V. Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship. Technological Forecasting and Social Change. 2023 Jan 1;186:122152. https://doi.org/10.1016/j.techfore.2022.122152
- Diyin Z, Bhaumik A. The Impact of Artificial Intelligence on Business Strategy: A Review of Theoretical and Empirical Studies in China. International Journal of Advances in Business and Management Research (IJABMR). 2025 Mar 12;2(3):9-17. https://doi.org/10.62674/ijabmr.2025.v2i03.002
- Aderibigbe AO, Ohenhen PE, Nwaobia NK, Gidiagba JO, Ani EC. Artificial intelligence in developing countries: Bridging the gap between potential and implementation. Computer Science & IT Research Journal. 2023 Dec;4(3):185-99. https://doi.org/10.51594/csitrj.v4i3.629
- Edunjobi TE, Odejide OA. Theoretical frameworks in AI for credit risk assessment: Towards banking efficiency and accuracy. International Journal of Scientific Research Updates. 2024;7(01):092-102. https://doi.org/10.53430/ijsru.2024.7.1.0030
- Ashritha P, Reddy PS. Impact of artificial intelligence on management decision-making. International Journal of Advances in Business and Management Research (IJABMR). 2023 Dec 12;1(2):10-8. https://doi.org/10.62674/ijabmr.2024.v1i02.002
