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An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process
Mounaf Asaad Khalil, Majed Hadid, Regina Padmanabhan, Adel Elomri, Laoucine Kerbache. Decision Analytics Journal, 2025-02-18
Topic: Risk, prevention and care in population studies
Digital transformation in banking has significantly improved efficiency, including in the critical Letter of Credit (LC) examination area. Despite these advancements, LC examination remains complex, labor-intensive, and error-prone, leading to operational risks and inefficiencies. Integrating Artificial Intelligence (AI) offers a promising solution but requires human checkers to verify AI-generated decisions, ensuring accuracy and compliance. Assigning these verification tasks is essential to fully capitalize on AI’s potential, balancing time savings with risk reduction. This paper explores the underexamined challenge of optimizing the hybrid process of AI-assisted LC examination to enhance trade finance. The research aims to minimize examination risk and maximize checker capacity utilization by offering practical strategies for improvement. Through data-driven research collaboration with international banks and FinTech companies and benchmarking relevant literature, an Integer Linear Programming model was developed to assign review tasks for LC documents indexed by AI based on their criticality and discrepancies to human checkers. The model also considers monetary value, checker expertise, and availability factors. Real case studies evaluated improvements over baseline practices, objectives prioritization, trade-off analysis, and varying supply–demand scenarios. The model achieved a 68.3% reduction in operational risks, improving compliance and trustworthiness while minimizing errors and financial losses. Utilization rates ranged from 34% for risk-focused strategies to 73% for efficiency-driven approaches, providing flexibility for resource allocation aligned with organizational priorities. Using the proposed AI-optimization framework and results, the study offers actionable insights for managers and guidance for researchers. • Propose an integrated artificial intelligence and optimization framework for letter of credit examination. • Develop an integer linear programming model to enhance efficiency and reduce risks through dual objectives. • Use real-world data to evaluate improvements, objectives trade-offs, and supply–demand sensitivity. • Achieve 68.3% risk reduction while ensuring high utilization and zero delays compared to baseline. • Provide research avenues on artificial intelligence integration, compliance, and sustainability.
Key findings
- Digital transformation in banking has significantly improved efficiency, including in the critical Letter of Credit (LC) examination area.
- Using the proposed AI-optimization framework and results, the study offers actionable insights for managers and guidance for researchers. • Propose an integrated artificial intelligence and optimization framework for letter of credit examination. • Develop an integer linear programming model to enhance efficiency and reduce risks through dual objectives. • Use real-world data to evaluate improvements, objectives trade-offs, and supply–demand sensitivity. • Achieve 68.3% risk reduction while ensuring high utilization and zero delays compared to baseline. • Provide research avenues on artificial intelligence integration, compliance, and sustainability.
Why it matters
This paper is about artificial intelligence and optimization in the Letter of Credit examination process in banking and contains no information on Alzheimer's disease. It reports no findings relevant to treating, curing or preventing dementia.
New to this subject? Start with our plain-English guide: Prevention →
Study details
- Effect size68.3%
- Evidence strengthLow
How this study scored 39/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Decision Analytics Journal) |
| Journal's citation record | 0 / 4 | Decision Analytics Journal: journal h-index 59 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 14 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 11.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 14 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Laoucine Kerbache: h-index 24 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Hamad bin Khalifa University: institution h-index 280 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 39 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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APA 7th
Khalil, M. A., Hadid, M., Padmanabhan, R., Elomri, A., & Kerbache, L. (2025). An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process. Decision Analytics Journal. https://doi.org/10.1016/j.dajour.2025.100552
Vancouver
Khalil MA, Hadid M, Padmanabhan R, Elomri A, Kerbache L. An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process. Decision Analytics Journal. 2025. doi:10.1016/j.dajour.2025.100552.
BibTeX
@article{khalil2025-17a12141,
author = {Khalil, Mounaf Asaad and Hadid, Majed and Padmanabhan, Regina and Elomri, Adel and Kerbache, Laoucine},
title = {{An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process}},
journal = {Decision Analytics Journal},
year = {2025},
doi = {10.1016/j.dajour.2025.100552},
url = {https://doi.org/10.1016/j.dajour.2025.100552}
}