Financial and Banking Strategic Studies

Meta-synthesis of artificial intelligence adoption implications in the banking industry: Exploring the benefits, explicit and hidden costs, and implementation risks

Document Type : Original Article

Authors

Ali Ghasemian 1
Farshad Bayati 2
Faraz Salehi 3

1 Department of Strategic Management, Orientation of Crisis Management, Passive Defense Faculty, Malek Ashtar University of Technology, Tehran, Iran.

2 Department of Digital Banking and Artificial Intelligence, Tejarat Bank, Tehran, Iran.

3 Department of Industrial Engineering, Amirkabir University, Tehran, Iran.

https://doi.org/10.22105/fbs.2026.597374.1208
Abstract
Purpose: In addition to benefits, the adoption of Artificial Intelligence (AI) technology in banks is associated with costs and risks. The fragmented nature of prior research findings highlights the need for a comprehensive understanding of the implications of AI adoption in the banking industry in order to support the development of more effective implementation processes. Accordingly, this study aims to examine the implications of AI adoption in the banking industry.
Methodology: In terms of purpose, the study is applied, and in terms of data, it adopts a qualitative approach. Relevant sources were identified through a systematic search using Persian and English keywords across major national and international databases, including Scopus, Web of Science, ScienceDirect, Noormags, Magiran, and others, covering the period from 2010 to 2026 and from 1389 to 1405 in the Iranian calendar. Following the screening process, 32 studies were selected for final analysis. Data were analyzed using Sandelowski and Barroso’s seven-step meta-synthesis method. The credibility of the classifications and findings was assessed through Cohen’s kappa coefficient, face validity, and expert judgment.
Findings: The analysis resulted in the extraction of 169 basic codes, which were subsequently compared, consolidated, and classified into 65 conceptual codes and 13 main categories. The identified categories included economic, operational and process-related, strategic and organizational benefits, among others; explicit and hidden costs; and technical, data, security, governance, implementation, and other risks.
Originality/Value: The results indicate that AI adoption in the banking industry is a multidimensional phenomenon that cannot be evaluated solely in terms of efficiency gains or cost reduction. The proposed framework can assist bank managers, policymakers, and regulatory authorities in making more accurate and balanced decisions regarding the design, implementation, and development of AI systems by simultaneously assessing their benefits, explicit and hidden costs, and implementation risks.

Keywords

Artificial intelligence
AI in the banking industry
Economic implications of AI
Operational implications of AI
Benefits
Costs
Risks of AI
Subjects
[1]     AL-Dosari, K., Fetais, N., & Kucukvar, M. (2024). Artificial intelligence and cyber defense system for banking industry: A qualitative study of AI applications and challenges. Cybernetics and Systems, 55(2), 302–330. https://doi.org/10.1080/01969722.2022.2112539
[2]     Li, Z. (2026). Does artificial intelligence enhance bank profitability? Evidence from China. Finance Research Letters, 90, 109366. https://doi.org/10.1016/j.frl.2025.109366
[3]     Abdulsalam, T. A., & Tajudeen, R. B. (2024). Artificial intelligence (AI) in the banking industry: A review of service areas and customer service journeys in emerging economies. Business & Management Compass, 68(3), 19–43. https://doi.org/10.56065/9hfvrq20
[4]     Carbó-Valverde, S., & Rodríguez-Fernández, F. (2025). Banking in the era of artificial intelligence: A survey of the economic, social and strategic implications. International Journal of Banking, Accounting and Finance, 15(1–2), 164–180. https://doi.org/10.1504/IJBAAF.2025.146534
[5]     Mehdipour Zarrinkamar, M., Hosseini, H., & Gharibi, H. (2025). The impact of financial artificial intelligence literacy, financial technology acceptance culture, and data-driven decision-making quality on the efficiency of financial processes in Iranian banks. Strategic Studies in Financial Management and Insurance, 2(3), 162-178. (In Persian). https://doi.org/10.22105/ssfmi.v2i3.85
[6]     Darshan, S., Hariharan, A. (2025). The impact of artificial intelligence (AI) on banking operations and customer service. Economic Sciences, 21(4), 86–91. https://doi.org/10.69889/21h4as06
[7]     Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204, 189–198. https://doi.org/10.1016/j.ejor.2009.08.003
[8]     Kuanova, L. A., & Otegen, A. N. (2026). Artificial intelligence in banking risk management: A bibliometric analysis. International Journal of Financial Studies, 14(4), 93. https://doi.org/10.3390/ijfs14040093
[9]     Aghamohammadi, H., & Enayati Hatkeluii, H. (2024). Identifying the human capital challenges of banking 4.0 affected by the progress of artificial intelligence: A meta-synthesis study. Financial and Banking Strategic Studies, 2(2), 98-114. (In Persian). https://doi.org/10.22105/fbs.2024.455806.1081
[10]   Ali, A., & Shah, M. (2024). What hinders adoption of artificial intelligence for cybersecurity in the banking sector. Information, 15(12), 760. https://doi.org/10.3390/info15120760
[11]   Ahmed, I. E., Mehdi, R., & Mohamed, E. A. (2023). The role of artificial intelligence in developing a banking risk index: an application of adaptive neural network-based fuzzy inference system (ANFIS). Artificial Intelligence Review, 56(11), 13873–13895. https://doi.org/10.1007/s10462-023-10473-9
[12]   Byambaa, O., Yondon, C., Rentsen, E., Darkhijav, B., & Rahman, M. (2025). An empirical examination of the adoption of artificial intelligence in banking services: The case of Mongolia. Future Business Journal, 11(1), 76. https://doi.org/10.1186/s43093-025-00504-y
[13]   Tahmasebi, D. (2026). Examining and prioritizing the impacts of artificial intelligence usage: The golden key to successful marketing in the banking system. Financial research journal, 28(1), 1-26. (In Persian). 10.22059/frj.2025.387854.1007688
[14]   Kumar, K., Kuhar, N., & Sharma, M. (2024). The future of banking: how artificial intelligence is reforming the industry. 2024 International Conference on Emerging Technologies and Innovation for Sustainability (Emergin) (pp. 219–224). IEEE. https://doi.org/10.1109/EmergIN63207.2024.10961596
[15]   Finio, M., O’Brien, K., & Downie, A. (2025). AI in banking. https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449
[16]   Goyal, K., Garg, M., & Malik, S. (2025). Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: A hybrid approach (SEM-ANN). Future Business Journal, 11(1), 44. https://doi.org/10.1186/s43093-025-00464-3
[17]   Meena, R., Mishra, A. K., & Raut, R. K. (2024). Strategic insights: Mapping the terrain of artificial intelligence (AI) in banking through mixed method approach. VINE Journal of Information and Knowledge Management Systems, 55(5), 1192–1222. https://doi.org/10.1108/VJIKMS-01-2024-0028
[18]  Narang, A., Vashisht, P., & Bajaj, S. B. (2024). Artificial intelligence in banking and finance. International Journal of Innovative Research in Computer Science and Technology, 12(2), 130–134. https://doi.org/10.55524/ijircst.2024.12.2.23
[19]   Noreen, U., Shafique, A., Ahmed, Z., & Ashfaq, M. (2023). Banking 4.0: Artificial intelligence (AI) in banking industry & consumer’s perspective. Sustainability, 15(4), 3682. https://doi.org/10.3390/su15043682
[20]   Ozili, P. K. (2024). Artificial intelligence in central banking: Benefits and risks of ai for central banks. Industrial applications of big data, AI, and blockchain (pp. 70–82). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-1046-5.ch004
[21]   Rahman, M., Ming, T. H., Baigh, T. A., & Sarker, M. (2021). Adoption of artificial intelligence in banking services: An empirical analysis. International Journal of Emerging Markets, 18(10), 4270–4300. https://doi.org/10.1108/IJOEM-06-2020-0724
[22]   Singh, R. K., Mishra, R., Kumar, S., & Bag, S. (2025). Applications of artificial intelligence for optimizing banking and financial operations: A systematic literature review and future research agenda. Journal of Economic Surveys, 39(5), 2284–2302. https://doi.org/10.1111/joes.12693
[23]   Tuge, N. B., & Msweli, N. T. (2026). Explainable artificial intelligence in the banking sector: A systematic literature review. Applied Artificial Intelligence, 40(1), 2676353. https://doi.org/10.1080/08839514.2026.2676353
[24]   Bisht, S., Sengupta, S., Tewari, I., Bisht, N., Pandey, K., & Upadhyay, A. (2024). AI-driven tools transforming the banking landscape: Revolutionizing finance. 2024 10th international conference on advanced computing and communication systems (ICACCS) (Vol. 1, pp. 934–938). IEEE. https://doi.org/10.1109/ICACCS60874.2024.10717099
[25]   Fares, O. H., Butt, I., & Lee, S. H. M. (2022). Utilization of artificial intelligence in the banking sector: a systematic literature review. Journal of Financial Services Marketing, 1–18. https://doi.org/10.1057/s41264-022-00176-7
[26]   Fundira, M., & Mbohwa, C. (2025). AI ethics in banking services: A systematic and bibliometric review of regulatory and consumer perspectives. Discover Artificial Intelligence, 5(1), 319. https://doi.org/10.1007/s44163-025-00432-4
[27]   Gallego-Gomez, C., & De-Pablos-Heredero, C. (2020). Artificial intelligence as an enabling tool for the development of dynamic capabilities in the banking industry. International Journal of Enterprise Information Systems (Ijeis), 16(3), 20–33. https://doi.org/10.4018/IJEIS.2020070102
[28]   Vuković, D. B., Dekpo-Adza, S., & Matović, S. (2025). AI integration in financial services: A systematic review of trends and regulatory challenges. Humanities and Social Sciences Communications, 12(1), 562. https://doi.org/10.1057/s41599-025-04850-8
[29]   Ghandour, A. (2021). Opportunities and challenges of artificial intelligence in banking: Systematic literature review. TEM Journal, 10(4), 1581-1587. https://doi.org/10.18421/TEM104-12
[30]   Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1–16. https://doi.org/10.1016/j.ejor.2022.04.027
[31]   Smit, J. (2024). A literature review on the impact of artificial intelligence on the future of banking and how to achieve a smooth transition. Open Journal of Business and Management, 12(1), 509–520. https://doi.org/10.4236/ojbm.2024.121031
[32]   Mansouri Moayyed, F., Kordheydari, R., & ghasemian sahei, A. (2021). The role of knowledge brokers in developing of technical knowledge marketing model for knowledge-intensive business service. Management Research in Iran, 24(2), 35-60. (In Persian). https://mri.modares.ac.ir/article_523.html
[33]   Paul, J., & Barari, M. (2022). Meta-analysis and traditional systematic literature reviews—What, why, when, where, and how? Psychology & Marketing, 39(6), 1099–1115. https://doi.org/10.1002/mar.21657
[34]   kordheydari,  rahil, Mansouri Moayyed, F., & Khodadad Hoseini, H. (2019). Metasynthesis of factors affecting to develop startups in new technology-based firms in entrepreneurial ecosystem. Journal of Entrepreneurship Development, 12(1), 141-160. (In Persian). https://doi.org/10.22059/jed.2019.279279.652965
[35]   Sandelowski, M., & Barroso, J. (2007). Handbook for synthesizing qualitative research. Springer Publishing Company. https://books.google.com/books?id=0I6KBQAAQBAJ.
[36]   Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
[37]   Rodrigues, A. R. D., Ferreira, F. A. F., e Silva Nunes Teixeira, F. J. C., & Zopounidis, C. (2022). Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognition-driven framework. Research in International Business and Finance. https://doi.org/10.1016/j.ribaf.2022.101616
[38]   Omoge, A. P., Gala, P., & Horky, A. (2022). Disruptive technology and AI in the banking industry of an emerging market. International Journal of Bank Marketing, 40(6), 1217–1247. https://doi.org/10.1108/IJBM-09-2021-0403
[39]   Kalyani, S., & Gupta, N. (2023). Is artificial intelligence and machine learning changing the ways of banking: A systematic literature review and meta analysis. Discover Artificial Intelligence, 3(1), 41. https://doi.org/10.1007/s44163-023-00094-0
[40]   Mi Alnaser, F., Rahi, S., Alghizzawi, M., & Ngah, A. H. (2023). Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking. Heliyon, 9(8). https://doi.org/10.1016/j.heliyon.2023.e18930
[41]   Baffour Gyau, E., Appiah, M., Gyamfi, B. A., Achie, T., & Naeem, M. A. (2024). Transforming banking: Examining the role of AI technology innovation in boosting banks financial performance. International Review of Financial Analysis, 96, 103700. https://doi.org/10.1016/j.irfa.2024.103700
[42]   Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57(8), 216. https://doi.org/10.1007/s10462-024-10854-8
[43]   Durongkadej, I., Hu, W., & Wang, H. E. (2024). How artificial intelligence incidents affect banks and financial services firms? A study of five firms. Finance Research Letters, 70, 106279. https://doi.org/10.1016/j.frl.2024.106279
[44]   Li, F., & Chen, Z. (2025). Dynamic quantification anti-fraud machine learning model for real-time transaction fraud detection in banking. Discover Computing, 28(1), 59. https://doi.org/10.1007/s10791-025-09549-7
[45]   Sonehekpon, E. S. (2025). Effect of artificial intelligence on banking stability: Evidence from developed countries. Finance Research Open, 1(4), 100074. https://doi.org/10.1016/j.finr.2025.100074
[46]   Li, X., Zhang, D., Zeng, N., & Meng, D. (2026). How does artificial intelligence reshape bank profitability in China? Evidence from a multi-period difference-in-differences model. International Journal of Financial Studies, 14(2), 39. https://doi.org/10.3390/ijfs14020039
[47]   Harandi, A., & Ebrahimi, A. (2024). Identifying factors affecting artificial intelligence in the social media marketing with a metasynthesis approach. Media Management Review, 3(3), 311-340. (In Persian). https://doi.org/10.22059/mmr.2024.384200.1124
[48]   Abbasi, A., Khodabakhsh, M., & Saber Samiei, D. (2025). Modeling employee creativity using artificial intelligence in the Iranian banking industry: A mixed approach. Dynamic Management and Business Analysis, 5(2), 1-19. (In Persian). https://dmbaj.org/index.php/dmba/article/view/297
[49]   Joghtaie, R., Nasiri, M., & Masoumi, E. (2025). Designing a model for the adoption of generative artificial intelligence in the Iranian banking industry. Journal of Technology in Entrepreneurship and Strategic Management (JTESM), 5(1), 1-22. (In Persian). https://journaltesm.com/index.php/journaltesm/article/view/398
[50]   Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
[51]   Ghasemian-Sahibi, A., Kordheidari Raheel, R., & Aghaei-Daghlian, M. (2021). A comprehensive look at qualitative, quantitative, and mixed methods in marketing research (From data to publication: Examining concepts, tools, and processes). The Arshadhan Educational Institute. (In Persian). https://elmnet.ir/doc/32022769-21561.
Financial and Banking Strategic Studies
Volume 4, Issue 2
Summer 2026
Pages 160-185

Home

Indexing and Abstracting

Guide for Authors

Editorial Board

Submit Manuscript

Contact Us