Natural Language Processing for Evaluating Shariah Governance Disclosures and Investor Confidence in the Saudi Sukuk Market

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@article{1715870,
      author = {Abdul Mannan Usmani},
      title = {Natural Language Processing for Evaluating Shariah Governance Disclosures and Investor Confidence in the Saudi Sukuk Market},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {817-828},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715870.pdf},
      abstract = {This review paper examines how natural language processing (NLP) can be used to evaluate Shariah governance disclosures and strengthen investor confidence in the Saudi sukuk market. The topic is timely because Saudi Arabia has become one of the most influential sukuk jurisdictions, while regulators and standard setters are simultaneously increasing expectations around governance clarity, disclosure discipline, and market transparency. Yet the information environment surrounding sukuk remains uneven. Important Shariah governance signals are dispersed across prospectuses, annual reports, Shariah committee statements, risk-factor sections, transaction documents, exchange announcements, and post-issuance updates. Traditional manual reading remains valuable but is increasingly insufficient for comparing disclosure quality across issuers, structures, and reporting periods at scale. This paper therefore reviews recent literature from 2020 to 2026 on Shariah-related disclosure, Islamic finance governance, sukuk market development, and NLP-based analysis of financial narratives. Following a structured review design, sources were identified through Scopus, Web of Science, Google Scholar, ScienceDirect, SpringerLink, MDPI, and official Saudi and international institutional repositories. The review synthesizes evidence around five themes: the economics of disclosure and investor confidence; the distinct content of Shariah governance disclosure in Islamic finance; the current Saudi regulatory and market context; NLP methods suitable for Arabic-English financial texts; and implementation implications for issuers, regulators, rating agencies, and investors. The paper argues that NLP can move the market beyond generic transparency claims by converting narrative disclosures into measurable indicators of Shariah board independence, governance specificity, monitoring depth, uncertainty, readability, and consistency over time. It proposes a Saudi-oriented review framework in which disclosure analytics are used not to replace Shariah scholars or legal review, but to support comparability, early-warning detection, and evidence-based confidence formation. The review concludes that stronger, machine-readable, and semantically richer Shariah governance disclosures can plausibly reduce information asymmetry, improve screening efficiency, support pricing discipline, and deepen confidence in Saudi sukuk among both domestic and international investors.},
      keywords = {Natural Language Processing; Sukuk; Shariah Governance; Disclosure Quality; Investor Confidence; Saudi Arabia; Islamic Finance; Review Paper},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1715870}
  }