AI-Based Prototype for Identifying Murabahah, Ujroh, Nisbah Variables Using Quran-Hadith Foundations

Abstrak

This study is important because Murabahah, Ujroh, and Nisbah contracts form the backbone of contemporary Islamic banking, yet their variables are often implemented without computationally verifiable links to primary Quran–Hadith foundations. The objective of this research is to develop and evaluate an AI-Based Prototype for Identifying Murabahah, Ujroh, and Nisbah Variables Using Quran-Hadith Foundations in order to enhance transparency, consistency, and doctrinal authenticity in Islamic financial transactions. The study employs a hybrid methodological framework combining natural language processing (NLP), semantic classification, supervised machine learning, and rule-based inference, integrated with Shariah expert validation. Textual data derived from the Qur'an and authenticated Hadith literature are processed to extract jurisprudential concepts and convert them into measurable contractual parameters. The results indicate that the prototype successfully identifies core variables, including cost disclosure and profit margin (Murabahah), service fee structure and duration (Ujroh), and proportional profit-sharing ratios and risk allocation (Nisbah). Statistical validation demonstrates consistent classification accuracy and alignment with Shariah expert assessments. The novelty of this research lies in integrating foundational Islamic textual analysis directly into an AI computational model, rather than relying solely on contemporary regulatory interpretations. Policy implications include supporting regulators, Shariah supervisory boards, and Islamic financial institutions in developing standardized AI-assisted compliance frameworks, thereby strengthening governance, transparency, and digital transformation in Islamic finance.