Abstract
<jats:p>Saudi Arabia is a major issuer of Sukuk in the GCC, with an increasingly developed secondary market infrastructure, including official issuance programs and exchange market information services. The variety of Sukuk structures, i.e., Ijara, Murabaha, Wakala bil istithmar, Mudaraba, Musharakah, and their combinations, poses an ongoing question: what Sukuk structure offers the most efficient combination of funding costs, risk, liquidity, and structural complexity under Shariah constraints. In 2020–2025, two developments facilitated the feasibility of this comparison: the growth of market data availability and information in Saudi Arabia, i.e., Sukuk issuance programs, exchange market watch services, etc. [3, 2], as well as the maturity of AI technologies, i.e., NLP, machine learning, and explainability, in the field of Islamic finance and capital markets [10, 11]. In this review, we synthesize the evidence from 2020–2025 on the application of AI in the comparison of the efficiency of Sukuk structure in Saudi Arabia. Efficiency is a multidimensional concept that encompasses pricing, i.e., spread/yield, issuance/structure costs, liquidity, Shariah risk, etc. In this review, we discuss the data sources, feature engineering strategies, and model families that may be applied in the comparison of Sukuk structure efficiency, as well as the pitfalls that may confound the analysis, i.e., non-comparable covenants, endogeneity of structure, lack of ground truth data on Shariah risk, etc. In addition, we suggest a blueprint of research on the application of AI in the comparison of Sukuk structure efficiency in Saudi Arabia, following the PRISMA 2020 guidelines [1] as well as the transparency requirements of Q1 operations and finance outlets.</jats:p>