The Use of Artificial Intelligence in Banking Industry
DOI:
https://doi.org/10.46799/ijssr.v3i7.447Abstract
Industry 4.0, also known as the fourth industrial revolution, has altered society and the economy by introducing intelligent robotics, artificial intelligence (AI), cloud computing enormous data sets, the Internet of Things (IoT), and 3D printers, among other scientific advances. To maintain competitiveness and keep up with global competition, it is vital to adapt to modern technology. The financial sector is a vibrant market with intense competition for products and services, and advancements in information technology have led to the development of highly valuable new technologies. This essay addresses the possible advantages of artificial intelligence in the banking sector. The study utilized a Systematic Literature Review (SLR) to evaluate the current literature on AI in the banking industry. The results of the SLR demonstrate that AI has been utilized in the banking industry in a variety of ways, including credit rating models and bank collapse prediction. In establishing credit card eligibility, logistic regression models were shown to be effective, with an accuracy rate of 80.43 percent. With a precision rate of 75.7% and a recall rate of 75.7%, artificial neural networks (ANNs) were shown to be the most accurate method for predicting bank collapse based on financial characteristics. Overall, the study indicates that AI has the ability to dramatically improve the banking business by enhancing efficiency, precision, and decision-making procedures. The study has limitations and potential biases, including the exclusion of non-English language articles and the possibility of a selection bias. To explore the full potential of AI in the banking business, additional study is required.
References
Anderson, Ross, Barton, Chris, Bölme, Rainer, Clayton, Richard, Ganán, Carlos, Grasso, Tom, Levi, Michael, Moore, Tyler, & Vasek, Marie. (2019). Measuring the changing cost of cybercrime.
Bisht, Deepa, Singh, Rajesh, Gehlot, Anita, Akram, Shaik Vaseem, Singh, Aman, Montero, Elisabeth Caro, Priyadarshi, Neeraj, & Twala, Bhekisipho. (2022). Imperative role of integrating digitalization in the firms finance: A technological perspective. Electronics, 11(19), 3252.
Dzombo, Gift Kimonge, Kilika, James M., & Maingi, James. (2017). The effect of branchless banking strategy on the financial performance of commercial banks in Kenya. International Journal of Financial Research, 8(4), 167–183.
George, A. Shaji, & George, A. S. Hovan. (2023). A review of ChatGPT AI’s impact on several business sectors. Partners Universal International Innovation Journal, 1(1), 9–23.
Goodell, John W., Kumar, Satish, Lim, Weng Marc, & Pattnaik, Debidutta. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577.
Hassoun, Abdo, Aït-Kaddour, Abderrahmane, Abu-Mahfouz, Adnan M., Rathod, Nikheel Bhojraj, Bader, Farah, Barba, Francisco J., Biancolillo, Alessandra, Cropotova, Janna, Galanakis, Charis M., & Jambrak, Anet Režek. (2022). The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 1–17.
Kavitha, M., Gnaneswar, G., Dinesh, R., Sai, Y. Rohith, & Suraj, R. Sai. (2021). Heart disease prediction using hybrid machine learning model. 2021 6th International Conference on Inventive Computation Technologies (ICICT), 1329–1333. IEEE.
Kotabe, Masaaki Mike, & Helsen, Kristiaan. (2022). Global marketing management. John Wiley & Sons.
Machkour, Badr, & Abriane, Ahmed. (2020). Industry 4.0 and its Implications for the Financial Sector. Procedia Computer Science, 177, 496–502.
Madakam, Somayya, Holmukhe, Rajesh M., & Jaiswal, Durgesh Kumar. (2019). The future digital work force: robotic process automation (RPA). JISTEM-Journal of Information Systems and Technology Management, 16.
Munirathinam, Sathyan. (2020). Industry 4.0: Industrial internet of things (IIOT). In Advances in computers (Vol. 117, pp. 129–164). Elsevier.
Rahmayati, Rahmayati. (2021). Competition Strategy In The Islamic Banking Industry: An Empirical Review. International Journal Of Business, Economics, And Social Development, 2(2), 65–71.
Sun, Huidong, Rabbani, Mustafa Raza, Sial, Muhammad Safdar, Yu, Siming, Filipe, José António, & Cherian, Jacob. (2020). Identifying big data’s opportunities, challenges, and implications in finance. Mathematics, 8(10), 1738.
Wójcik, Dariusz, & Ioannou, Stefanos. (2020). COVID?19 and finance: market developments so far and potential impacts on the financial sector and centres. Tijdschrift Voor Economische En Sociale Geografie, 111(3), 387–400.
Xie, Xiufeng, Zhang, Xinyu, & Zhu, Shilin. (2017). Accelerating mobile web loading using cellular link information. Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, 427–439.
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