Review of Improving Banking Operational Efficiency through AI and ML: Strategy, Implementation and Impact
DOI:
https://doi.org/10.61098/jkst.v3i1.125Keywords:
Operational Banking Efficiency, Artificial Intelligence, Machine Learning, Systematic Literature ReviewAbstract
Artificial Intelligence (AI) and Machine Learning (ML) are critical components in the financial industry's ongoing transformation of operational dynamics with the aim of improving efficiency. Utilizing a systematic literature review (SLR) approach, this article investigates the implementation and impact of AI and ML in banking. It focuses on the ways in which these technologies can optimize operational processes, the obstacles encountered by banks during their integration, and the potential future developments of these innovations in banking operations. The study evaluates the transformative potential of AI and ML in the banking industry, identifies the obstacles to their adoption, and addresses three research questions by examining the potential of AI and ML to enhance operational efficiency. Our research demonstrates that AI and ML substantially increase productivity by virtue of their sophisticated data processing and decision-making functionalities. Nevertheless, persistent integration obstacles include concerns regarding data security, substantial upfront investments, and deficiencies in expertise in AI and ML. In the future, AI and ML have the potential to significantly transform the finance industry. By proposing three novel use cases automating credit evaluation process, augmenting banking operations via Advanced Optical Character Recognition (OCR) solutions and optimizing data analysis efficiency the paper makes a scholarly contribution. The following recommendations provide banks with actionable insights on how to optimize operational efficiency through the utilization of AI and ML.
Downloads
References
Y. Chen, E. Kumara, V. S.-A. of O. Research, and undefined 2021, “Invesitigation of finance industry on risk awareness model and digital economic growth,” Springer, vol. 326, p. 15, Jul. 2023, doi: 10.1007/s10479-021-04287-7.
A. R.-P. J. of A. of Egypt and undefined 2021, “Impact of E-Banking on Customer Satisfaction,” archives.palarch.nl, vol. 18, no. 8, p. 4220.
A. George, A. G.-P. U. I. Innovation, and undefined 2023, “A review of ChatGPT AI’s impact on several business sectors,” puiij.com, 2020.
A. Ashta, H. H.-S. Change, and undefined 2021, “Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance,” Wiley Online Libr., vol. 30, no. 3, pp. 211–222, May 2021, doi: 10.1002/jsc.2404.
O. Bukola Adeoye et al., “Fintech, Taxation, and Regulatory Compiliance: Navigating The New Financial Landscape,” Financ. Account. Res. J., vol. 6, no. 3, pp. 320–330, Mar. 2024, doi: 10.51594/FARJ.V6I3.858.
P. Brereton, B. Kitchenham, D. Budgen, and M. Turner, “Lessons from applying the systematic literature review process within the software engineering domain,” J. Syst. Softw., 2007.
T. T.-J. of A. Finance, E. and Business, and undefined 2020, “The impact of balanced scorecard on performance: The case of Vietnamese commercial banks,” oak.go.kr.
A. Harika, M. Sunil Kumar, V. Anantha Natarajan, and S. Kallam, “Business Process Reengineering: Issues and Challenges,” pp. 363–382, 2021, doi: 10.1007/978-981-15-6707-0_35.
F. Königstorfer, S. T.-J. of behavioral and experimental, and undefined 2020, “Applications of Artificial Intelligence in commercial banks–A research agenda for behavioral finance,” Elsevier.
I. Lee and Y. J. Shin, “Machine learning for enterprises: Applications, algorithm selection, and challenges,” Bus. Horiz., vol. 63, no. 2, pp. 157–170, 2020, doi: https://doi.org/10.1016/j.bushor.2019.10.005.
S. K. Hashemi, S. L. Mirtaheri, and S. Greco, “Fraud Detection in Banking Data by Machine Learning Techniques,” IEEE Access, vol. 11, no. January, pp. 3034–3043, 2023, doi: 10.1109/ACCESS.2022.3232287.
V. Mahalakshmi, N. Kulkarni, … K. K.-M. T., and undefined 2022, “The role of implementing artificial intelligence and machine learning technologies in the financial services industry for creating competitive intelligence,” Elsevier.
M. Satheesh, S. N.-I. Management, and undefined 2021, “Applications of artificial intelligence on customer experience and service quality of the banking sector,” americanscholarspress.us, vol. 6.
S. Carbo-Valverde, P. Cuadros-Solas, and F. Rodríguez-Fernández, A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests, vol. 15, no. 10 October. 2020. doi: 10.1371/journal.pone.0240362.
P. Carmona, A. Dwekat, and Z. Mardawi, “No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure,” Res. Int. Bus. Financ., vol. 61, p. 101649, 2022, doi: https://doi.org/10.1016/j.ribaf.2022.101649.
M. Rahman, T. H. Ming, T. A. Baigh, and M. Sarker, “Adoption of artificial intelligence in banking services: an empirical analysis,” Int. J. Emerg. Mark., vol. 18, no. 10, pp. 4270–4300, Nov. 2023, doi: 10.1108/IJOEM-06-2020-0724/FULL/HTML.
M. Thowfeek, … S. S.-S. S., and undefined 2020, “Drivers of artificial intelligence in banking service sectors,” researchgate.net, vol. 6.
I. Kristiana, A. Ramadhan, A. Trisetyarso, E. Abdurachman, and M. Zarlis, “Capability Development to Generate Business Value Through Customer-centric Analytics in the Banking Industry: A Systematic Review,” J. Syst. Manag. Sci., vol. 13, no. 2, pp. 64–82, 2023, doi: 10.33168/JSMS.2023.0205.
B. Kitchenham, “Kitchenham , B .: Guidelines for performing Systematic Literature Reviews in software engineering . EBSE Technical Report EBSE-2007-01 Guidelines for performing Systematic Literature Reviews in Software Engineering,” Icse, no. January 2007, 2021.
K. B. Ooi et al., “The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions,” J. Comput. Inf. Syst., vol. 00, no. 00, pp. 1–32, 2023, doi: 10.1080/08874417.2023.2261010.
A. Corallo, A. M. Crespino, M. Lazoi, and M. Lezzi, “Model-based Big Data Analytics-as-a-Service framework in smart manufacturing: A case study,” Robot. Comput. Integr. Manuf., vol. 76, p. 102331, 2022, doi: https://doi.org/10.1016/j.rcim.2022.102331.
E. Zdravevski, P. Lameski, C. Apanowicz, and D. Ślȩzak, “From Big Data to business analytics: The case study of churn prediction,” Appl. Soft Comput. J., vol. 90, 2020, doi: 10.1016/j.asoc.2020.106164.
Y. Cao and J. Zhai, “A survey of AI in finance,” J. Chinese Econ. Bus. Stud., vol. 20, no. 2, pp. 125–137, 2022, doi: 10.1080/14765284.2022.2077632.
D. Pramod, “Robotic process automation for industry: adoption status, benefits, challenges and research agenda,” Benchmarking An Int. J., vol. 29, no. 5, pp. 1562–1586, Jan. 2022, doi: 10.1108/BIJ-01-2021-0033.
Y. Shen, C. Guo, H. Li, J. Chen, Y. Guo, and X. Qiu, “Financial Feature Embedding with Knowledge Representation Learning for Financial Statement Fraud Detection,” Procedia Comput. Sci., vol. 187, pp. 420–425, 2021, doi: 10.1016/j.procs.2021.04.110.
J. Lee, “Access to finance for artificial intelligence regulation in the financial services industry,” Eur. Bus. Organ. Law Rev., 2020, doi: 10.1007/s40804-020-00200-0.
P. Parameswarappa, “Importance of Information Governance &Implementation in Banking Industry,” Available SSRN 4190576, 2022, [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4190576
J. Lee and A. Darbellay, Data Governance in AI, FinTech and LegalTech: Law and Regulation in the Financial Sector. books.google.com, 2022. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=9eRvEAAAQBAJ&oi=fnd&pg=PR1&dq=%22data+governance%22+framework+and+regulatory+compliance+and+advanced+analytics+and+banking+sector&ots=sK-Sjmy-BL&sig=uTxwiFD01IYDSaYfUapm4j2NhJg
M. Rhahla, S. Allegue, and T. Abdellatif, “Guidelines for GDPR compliance in Big Data systems,” J. Inf. Secur. Appl., vol. 61, p. 102896, 2021, doi: https://doi.org/10.1016/j.jisa.2021.102896.
H. Sadok, F. Sakka, and M. E. H. El Maknouzi, “Artificial intelligence and bank credit analysis: A review,” Cogent Econ. Financ., vol. 10, no. 1, 2022, doi: 10.1080/23322039.2021.2023262.
E. Indriasari, H. Prabowo, F. L. Gaol, and B. Purwandari, “Intelligent Digital Banking Technology and Architecture: A Systematic Literature Review,” Int. J. Interact. Mob. Technol., vol. 16, no. 19, pp. 98–117, 2022, doi: 10.3991/ijim.v16i19.30993.
I. W. R. Wijaya and Mudjahidin, “Development of conceptual model to increase customer interest using recommendation system in e-commerce,” Procedia Comput. Sci., vol. 197, no. 2021, pp. 727–733, 2021, doi: 10.1016/j.procs.2021.12.194.
A. S. Edu, “Positioning big data analytics capabilities towards financial service agility,” Aslib J. Inf. Manag., vol. 74, no. 4, pp. 569–588, Jan. 2022, doi: 10.1108/AJIM-08-2021-0240.
D. Broby, “The use of predictive analytics in finance,” J. Financ. Data Sci., vol. 8, pp. 145–161, 2022, doi: 10.1016/j.jfds.2022.05.003.
H. Hampton and A. Foley, “A review of current analytical methods, modelling tools and development frameworks applicable for future retail electricity market design,” Energy, vol. 260, p. 124861, 2022, doi: https://doi.org/10.1016/j.energy.2022.124861.
M. M. Mariani and S. Fosso Wamba, “Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies,” J. Bus. Res., vol. 121, pp. 338–352, 2020, doi: https://doi.org/10.1016/j.jbusres.2020.09.012.
Q. Shambour, “A deep learning based algorithm for multi-criteria recommender systems,” Knowledge-Based Syst., vol. 211, p. 106545, 2021, doi: 10.1016/j.knosys.2020.106545.
M. S. Khan and H. Umer, “ChatGPT in finance: Applications, challenges, and solutions,” Heliyon, vol. 10, no. 2, p. e24890, 2024, doi: 10.1016/j.heliyon.2024.e24890.
L. Nosrati, A. M. Bidgoli, and H. H. S. Javadi, “Identifying People’s Faces in Smart Banking Systems Using Artificial Neural Networks,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, 2024, doi: 10.1007/s44196-023-00383-7.
A. Mehrotra, “Artificial Intelligence in Financial Services - Need to Blend Automation with Human Touch,” 2019 Int. Conf. Autom. Comput. Technol. Manag. ICACTM 2019, pp. 342–347, 2019, doi: 10.1109/ICACTM.2019.8776741.
A. Gandhi, K. Adhvaryu, S. Poria, E. Cambria, and A. Hussain, “Multimodal Sentiment Analysis: A Systematic review of History, Datasets, Multimodal Fusion Methods, Applications, Challenges and Future Directions,” Inf. Fusion, 2022, doi: https://doi.org/10.1016/j.inffus.2022.09.025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Idha Kristiana
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Penulis menyimpan hak cipta dan memberikan jurnal hak penerbitan pertama naskah secara simultan dengan lisensi di bawah Creative Common Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) yang mengizinkan orang lain untuk berbagi pekerjaan dengan sebuah pernyataan kepenulisan pekerjaan dan penerbitan awal di jurnal ini. Penulis bisa memasukkan ke dalam penyusunan kontraktual tambahan terpisah untuk distribusi non ekslusif versi kaya terbitan jurnal (contoh: mempostingnya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan penerbitan awalnya di jurnal ini.
