Main Article Content
Abstract
Purpose – This study explores the integration of Artificial Intelligence (AI) in Islamic finance, analyzing global trends, ethical implications, and future research directions. The purpose is to assess AI’s role in enhancing Sharia compliance, regulatory adherence, and operational efficiency in Islamic financial institutions.
Methodology – Using a bibliometric analysis approach, this research examines publications from 2010 to 2023 indexed in Scopus and Web of Science. Keywords such as Islamic finance, "artificial intelligence, AI in finance, Sharia compliance, and globalization guided the search. Analytical tools, including VOSviewer and CiteSpace, were employed to visualize publication trends, citation networks, and influential research contributions from key countries like Pakistan, Malaysia, Turkey, Saudi Arabia, Indonesia, Qatar, Iran, and the UAE.
Findings – The research indicate that AI significantly enhances financial inclusion, risk assessment, compliance automation, and customer service in Islamic finance. Key research areas highlight AI-driven solutions for Sharia-compliant financial products, ethical considerations, and regulatory frameworks. However, limitations exist, as this study focuses only on English-language publications, potentially omitting critical insights from non-English sources.
Implications – This research contributes to the understanding of AI's role in Islamic finance, offering practical implications for financial institutions seeking to integrate AI for efficiency, transparency, and compliance.
Originality – It provides an original bibliometric perspective on AI applications in Islamic finance, underscoring the sector’s potential for innovation and sustainable growth in a globalized financial landscape.
Keywords
Article Details
Copyright (c) 2025 Muhammad Saeed Iqbal, Fifi Anti Mapika Sari Binti Sukamto, Siti Noraisyah Binti Norizan, Shanayyara Mahmood, Arooj Fatima, Faiza Hashmi
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References
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- Beck, T., Demirgüç-Kunt, A., & Merrouche, O. (2013). Islamic vs. conventional banking: Business model, efficiency, and stability. Journal of Banking & Finance, 37(2), 433-447. https://doi.org/10.1016/j.jbankfin.2012.09.016
- Bornmann, L., & Mutz, R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11), 2215-2222. https://doi.org/10.1002/asi.23329
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References
Aggarwal, C. C. (2018). Neural networks and deep learning: A textbook. Springer. https://doi.org/10.1007/978-3-319-94463-0
Alam, A., Nugroho, D. W., Isman, I., & Ahmi, A. (2025). Bibliometric study on corporate governance of Islamic banks. International Journal of Economics and Financial Issues, 15(2), 397-410. https://doi.org/10.32479/ijefi.18280
Aliyu, S., & Yusof, R. M. (2016). Profitability and cost efficiency of Islamic banks: A panel analysis of some selected countries. International Journal of Economics and Financial Issues, 6(4), 1736-1743.
Arner, D. W., Barberis, J., & Buckley, R. P. (2017). FinTech, RegTech and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business, 37(3), 371-413. https://scholarlycommons.law.northwestern.edu/njilb/vol37/iss3/2
Ayub, M. (2007). Understanding Islamic finance. John Wiley & Sons. https://doi.org/10.1002/9781119209096
Beck, T., Demirgüç-Kunt, A., & Merrouche, O. (2013). Islamic vs. conventional banking: Business model, efficiency, and stability. Journal of Banking & Finance, 37(2), 433-447. https://doi.org/10.1016/j.jbankfin.2012.09.016
Bornmann, L., & Mutz, R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11), 2215-2222. https://doi.org/10.1002/asi.23329
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence: What it can – and cannot – do for your organization. Harvard Business Review. https://hbr.org/2017/07/the-business-of-artificial-intelligence
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. https://doi.org/10.1002/asi.20317
Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1-40. https://doi.org/10.1515/jdis-2017-0006
Clarivate Analytics. (2021). Web of science platform. https://clarivate.com/academia-government/scientific-and-academic-research/research-discovery-and-referencing/web-of-science/
El-Gamal, M. A. (2006). Islamic finance: Law, economics, and practice. Cambridge University Press. https://doi.org/10.1017/CBO9780511753756
Elsevier. (2021). Scopus: Comprehensive, multidisciplinary, trusted abstract and citation database. https://www.elsevier.com/products/scopus
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses. The FASEB Journal, 22(2), 338-342. https://doi.org/10.1096/fj.07-9492LSF
Glänzel, W., & Schubert, A. (2004). Analyzing scientific networks through co-authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257-276). Springer. https://doi.org/10.1007/1-4020-2755-9_12
Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation.” AI Magazine, 38(3), 50-57. https://doi.org/10.1609/aimag.v38i3.2741
Hassan, M. K., Rashid, M., & Jaffar, M. A. (2020). Artificial intelligence in Islamic banking. In Rafay, A. (2020). Handbook of Research on Theory and Practice of Global Islamic Finance (pp. 513-528). PA: IGI Publishing, https://doi.org/10.4018/978-1-7998-0218-1
Iqbal, A., Iqbal, M. S., Fatima, A., Ghori, M. A., & Tahir, S. (2025). Optimizing resilience: Impact of credit risk management on financial performance in Islamic banking. Zakariya Journal of Social Science, 4(1), 01-16. https://doi.org/10.59075/zjss.v4i1.503
Iqbal, M. S., & Fikri, D. S. M. (2024a). Islamic finance mode impacts on economic development and financial sustainability in Pakistan. Hamdard Islamicus, 47(4). https://doi.org/10.57144/hi.v47i4.901
Iqbal, M. S., & Fikri, S. M. (2024b). Assessing and pricing Islamic sukuk: An overview. Ihtifaz: Journal of Islamic Economics, Finance, and Banking, 7(1), 26-38. https://doi.org/10.12928/ijiefb.v7i1.10376
Iqbal, M. S., & Fikri, M. (2023a). Impact of panel of board and Shari'ah penal on zakat reserves in Islamic banks of Pakistan. COMSATS Journal of Islamic Finance, 8(2). https://lahore.comsats.edu.pk/CIF/Journal/Vo8-2/DOI10.26652.cjif.8202325.pdf
Iqbal, M. S., & Fikri, S. M. (2023b). Comparison of credit risk management practices among Islamic and public commercial banks in Pakistan. International Journal of Management Research and Emerging Sciences, 13(3). https://doi.org/10.56536/ijmres.v13i3.509
Lacity, M., & Willcocks, L. (2016). Robotic process automation: The next transformation lever for shared services, The Outsourcing Unit, Working Research Paper Series, Paper 16/01. https://www.umsl.edu/~lacitym/OUWP1601.pdf
Moed, H. F. (2005). Citation analysis in research evaluation. Springer. https://doi.org/10.1007/1-4020-3714-7
Rana, A., Iqbal, MSI., & Rana, A. (2024). Impact of monetary management on nurses’ turnover decisions and job anxiety as a mediator and resilience as a moderator. Journal of Nurses and Midwives Pakistan, 4(1), 42–53. https://www.kgpublisher.com/index.php/pjnm/article/view/124
Usmani, M. M. T. (2021). An introduction to Islamic finance (Vol. 20). Brill.
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Wilson, R. (2009). The development of Islamic finance in the GCC. The Centre for the Study of Global Governance. https://eprints.lse.ac.uk/55281/1/Wilson-2009.pdf
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning-based natural language processing. IEEE Computational Intelligence Magazine, 13(3), 55–75. https://doi.org/10.1109/MCI.2018.2840738