From Trust to Transparency: A Systematic Qualitative Review of Regulatory, Ethical, and Organizational Challenges in AI-Driven Letter of Credit Automation
DOI:
https://doi.org/10.70142/ijbmel.v3i2.461Keywords:
Artificial Intelligence, Letter of Credit Automation, Trade Finance, gulatory and Ethical Challenges, Digital TransformationAbstract
This study presents a systematic qualitative literature review examining regulatory, ethical, and organizational challenges in the adoption of artificial intelligence (AI) for letter of credit (LC) automation in trade finance. Synthesizing prior interdisciplinary research, the review finds that AI technologies—such as machine learning and natural language processing—offer substantial efficiency, accuracy, and transparency gains in documentary credit examination. However, these benefits are constrained by regulatory uncertainty stemming from legacy legal frameworks, ethical concerns related to algorithmic opacity, bias, and accountability, and organizational challenges involving trust, governance, and workforce readiness. The findings highlight that AI-driven LC automation constitutes a socio-technical transformation rather than a purely technological upgrade, requiring alignment between evolving regulation, responsible AI governance, and organizational change management. This study contributes to the trade finance and digital transformation literature by providing an integrated perspective on how transparency can be institutionalized without undermining trust in automated financial decision-making
References
Agarwal, L., Jain, N., Singh, N., & Kumar, S. (2023). AI-based planning of business management. In AI-Based Data Analytics: Applications for Business Management (pp. 135–152). Routledge.
Ahmed, W. A. H., & MacCarthy, B. L. (2023). Blockchain-enabled supply chain traceability – How wide? How deep? International Journal of Production Economics, 263, 108963. https://doi.org/10.1016/j.ijpe.2023.108963
Akhavan, F., & Hassannayebi, E. (2024). A hybrid machine learning with process analytics for predicting customer experience in online insurance services. Decision Analytics Journal, 11, 100452. https://doi.org/10.1016/j.dajour.2024.100452
Almustafa, E., Assaf, A., & Allahham, M. (2023). Implementation of artificial intelligence for financial process innovation of commercial banks. Revista de Gestão Social e Ambiental, 17(9), 45–60.
Arif, M. (2025). AI-driven trade finance: Transforming global commerce with cutting-edge technology. Springer.
Aujara, S. M. (2019). Documentary letter of credit discrepancy and risk management in the Nigerian crude oil export. Journal of Financial Risk Management.
Bahoo, S., Cucculelli, M., Goga, X., & Mondolo, J. (2024). Artificial intelligence in finance: A comprehensive review through bibliometric and content analysis. SN Business & Economics, 4(2). https://doi.org/10.1007/s43546-023-00618-x
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Cheong, B. C. (2024). Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6. https://doi.org/10.3389/fhumd.2024.1421273
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Dixon-Woods, M., Agarwal, S., Jones, D., Young, B., & Sutton, A. (2005). Synthesizing qualitative and quantitative evidence: A review of possible methods. Journal of Health Services Research & Policy, 10(1), 45–53.
Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92.
Fink, A. (2019). Conducting research literature reviews: From the internet to paper (5th ed.). Sage.
Finlay, L. (2002). Negotiating the swamp: The opportunity and challenges of reflexivity in research practice. Qualitative Research, 2(2), 209–230.
Global Trade Review. (2026). How LLMs are transforming trade finance operations. GTR. Retrieved from https://www.gtreview.com/
Goodman, E. P., & Trehu, J. (2022). AI audit washing and accountability. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4227350
Greenhalgh, T., Peacock, R., & Wong, G. (2018). Systematic reviews and evidence synthesis: Methodology and synthesis logic. Oxford University Press.
Hannes, K., & Lockwood, C. (2011). Synthesizing qualitative research: Choosing the right approach. Wiley-Blackwell.
Hawker, S., Payne, S., Kerr, C., Hardey, M., & Powell, J. (2002). Appraising the evidence: Reviewing disparate data systematically. Qualitative Health Research, 12(9), 1284–1299.
Ikhsan, R. B., Fernando, Y., Prabowo, H., Yuniarty, A. G., & Kuncoro, E. A. (2025). AI adoption in banking: Extending TAM with perceived trust. Digital Business, 5(1), 100103. https://doi.org/10.1016/j.digbus.2024.100103
International Chamber of Commerce. (2025). UCP 600: Uniform rules for documentary credits. ICC Publishing.
Jones, E. (2023). Digital disruption: Artificial intelligence and international trade policy. Oxford Review of Economic Policy, 39(1), 70–84.
https://doi.org/10.1093/oxrep/grac049
Khalil, M. A., Padmanabhan, R., Hadid, M., Elomri, A., & Kerbache, L. (2025a). AI-driven transformation in trade finance. Digital Business, 5(2), 100130. https://doi.org/10.1016/j.digbus.2025.100130
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (EBSE Technical Report). Keele University.
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., ... & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. PLoS Medicine, 6(7), e1000100.
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282.
MDPI. (2025). Blockchain-enabled trade finance innovation. Sustainability, 12(1), 188. Retrieved from https://www.mdpi.com/2071-1050/12/1/188
Medium. (2025). The trade finance revolution that wasn’t: Why a decade of digital transformation left banks still using fax machines. Medium.com. Retrieved from https://medium.com/@tobias_pfuetze/the-trade-finance-revolution-that-wasnt
Mirishli, S. (2025). Regulating AI in financial services: Legal frameworks and compliance challenges. arXiv. https://arxiv.org/abs/2503.14541
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097.
Murinde, V., Rizopoulos, E., & Zachariadis, M. (2022). The impact of the FinTech revolution on the future of banking: Opportunities and risks. International Review of Financial Analysis, 81, 102103. https://doi.org/10.1016/j.irfa.2022.102103
National Institute of Standards and Technology (NIST). (2025). Post-quantum cryptography (PQC) project overview. U.S. Department of Commerce. Retrieved from https://csrc.nist.gov/projects/post-quantum-cryptography
Newgen Soft. (2026). Intelligent document processing for trade finance. NewgenONE AURA. Retrieved from https://newgensoft.com/solutions/industries/financial-institutions/transaction-banking/trade-finance/intelligent-document-processing-idp-trade-finance/
Nezhad, M. E., Rashidian, S., & Botta, C. (2024). Revolutionizing trade finance: Leveraging the power of blockchain and AI in electronic letters of credit. Uniform Law Review, 29(1), 87–115. https://doi.org/10.1093/ulr/unae023
Ngadi Permana, Mohammad Chaidir, & Sri Utami Nurhasanah. (2024). Mengungkap Pengaruh Biaya Perdagangan dan Faktor Risiko terhadap Kinerja Hedge Fund. MASMAN Master Manajemen, 2(2), 187–196.
https://doi.org/10.59603/masman.v2i2.683
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
Pahari, S., Polisetty, A., Sharma, S., Jha, R., & Chakraborty, D. (2023). Adoption of AI in the banking industry: A case study on Indian banks. Indian Journal of Marketing, 53(3), 45–59. https://doi.org/10.17010/ijom/2023/v53/i3/172654
Paré, G., Trudel, M.-C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183–199.
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., ... & Duffy, S. (2006). Guidance on the conduct of narrative synthesis in systematic reviews. ESRC Methods Programme.
Rueckel, D., Muehlburger, M., & Koch, S. (2020). An updated framework of factors enabling digital transformation. Pacific Asia Journal of the Association for Information Systems, 12(4), 1–32.
Ruslaini Ruslaini, & Eri Kusnanto. (2020). Sustainability Dalam Rantai Pasok Global: Tinjauan Literatur Dari Perspektif Bisnis Internasional dan Manajemen Rantai Pasok. Journal of Business, Finance, and Economics (JBFE), 1(2).
https://doi.org/10.32585/jbfe.v1i2.5691
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
van Giffen, B., Herhausen, D., & Fahse, T. (2022). Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research, 144, 93–106.
https://doi.org/10.1016/j.jbusres.2022.01.076
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
Vial, G. (2019). Understanding digital transformation. Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii–xxiii.
Yulianti, G., Bernardi, B., Permana, N., & Wijayanti, F. A. K. W. (2023). Transformasi Pendidikan Indonesia: Menerapkan Potensi Kecerdasan Buatan (AI). Journal of Information Systems and Management (JISMA), 2(6), 102–106.
