ALGORITHMIC SHARIA COMPLIANCE: A MACHINE LEARNING FRAMEWORK FOR AUTOMATED RISK ASSESSMENT AND SCREENING IN ISLAMIC FINTECH

Nurul Ain Safrizon (1), Vugar Abdullayev (2)
(1) UIN Mahmud Yunus BatusangkarID Indonesia,
(2) Azerbaijan University of Architecture and ConstructionAZ Azerbaijan

Abstract

Rapid expansion of Islamic Financial Technology (Islamic FinTech) has increased the complexity of ensuring continuous Sharia compliance across digital financial products, investment services, and automated transactions. Conventional compliance assessment primarily depends on manual evaluation by Sharia scholars and supervisory boards, creating challenges related to scalability, consistency, operational efficiency, and timely risk identification. This study aimed to develop and evaluate a machine learning framework for automated Sharia compliance assessment and financial risk screening that integrates predictive intelligence with explainable and transparent decision support. A mixed-methods sequential explanatory research design was employed, combining quantitative analysis of 125,000 anonymized financial transaction records using supervised machine learning algorithms with qualitative evidence obtained from expert interviews, institutional document analysis, and regulatory validation. Comparative evaluation demonstrated that the proposed framework achieved high predictive performance, with XGBoost providing the highest classification accuracy while Explainable Artificial Intelligence techniques enhanced transparency through interpretable decision explanations. Qualitative findings confirmed that automated screening significantly reduced compliance review time, strengthened institutional consistency, and improved stakeholder confidence without replacing the essential role of Sharia scholars in complex jurisprudential decisions. Results indicate that algorithmic Sharia compliance functions most effectively as a human-centered decision-support framework integrating machine learning, Islamic jurisprudence, financial governance, and explainable artificial intelligence. Responsible implementation of this framework provides a scalable pathway toward trustworthy Islamic FinTech, enhanced regulatory accountability, and sustainable digital financial innovation.

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References

Abualruz, H., Yasin, I., Abu Sabra, M. A., Abunab, H. Y., Azayzeh, R., Zubidi, Y., Emad, S., & alriyati, B. (2025). The role of artificial intelligence in enhancing triage decisions in healthcare settings: A systematic review. Applied Nursing Research, 86, 152024. https://doi.org/https://doi.org/10.1016/j.apnr.2025.152024

Agbloyor, E. K., Pan, L., Dwumfour, R. A., & Gyeke-Dako, A. (2023). We are back again! What can artificial intelligence and machine learning models tell us about why countries knock at the door of the IMF? Finance Research Letters, 57, 104244. https://doi.org/https://doi.org/10.1016/j.frl.2023.104244

Agnihotri, A., & Kohli, N. (2025). (XAI-AGUWEM) Explainable Artificial Intelligence-based Attention Guided Uncertainty Weighting Ensemble Model for the Classification of COVID-19 and Pneumonia in X-ray Medical Images. Recent Advances in Electrical and Electronic Engineering, 18(7), 862–884. https://doi.org/https://doi.org/10.2174/0123520965334135241115064754

Ali, H., & Aysan, A. F. (2025). Decoding digital signals: AI sentiment and financial performance at ?slamic banks. Borsa Istanbul Review, 25(5), 953–971. https://doi.org/https://doi.org/10.1016/j.bir.2025.05.011

Ali, W., & Khan, A. Z. (2025). Factors influencing readiness for artificial intelligence: a systematic literature review. Data Science and Management, 8(2), 224–236. https://doi.org/https://doi.org/10.1016/j.dsm.2024.09.005

Awadh, M. Al, & Mallick, J. (2024). A decision-making framework for landfill site selection in Saudi Arabia using explainable artificial intelligence and multi-criteria analysis. Environmental Technology & Innovation, 33, 103464. https://doi.org/https://doi.org/10.1016/j.eti.2023.103464

Baffour Gyau, E., Appiah, M., Gyamfi, B. A., Achie, T., & Naeem, M. A. (2024). Transforming banking: Examining the role of AI technology innovation in boosting banks financial performance. International Review of Financial Analysis, 96, 103700. https://doi.org/https://doi.org/10.1016/j.irfa.2024.103700

Balta, I., Lemon, J., Popescu, C. A., McCleery, D., Iancu, T., Pet, I., Stef, L., Douglas, A., & Corcionivoschi, N. (2025). Food safety – the transition to artificial intelligence (AI) modus operandi. Trends in Food Science & Technology, 165, 105278. https://doi.org/https://doi.org/10.1016/j.tifs.2025.105278

Ben Jabeur, S. (2024). Natural capital accounting for sustainability: Bibliometric analysis and explainable artificial intelligence modeling for citation counts. Journal of Cleaner Production, 451, 142138. https://doi.org/https://doi.org/10.1016/j.jclepro.2024.142138

Chaudhary, M., Gaur, L., Chakrabarti, A., Singh, G., Jones, P., & Kraus, S. (2025). An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligence. Journal of Innovation & Knowledge, 10(3), 100700. https://doi.org/https://doi.org/10.1016/j.jik.2025.100700

G, U. M., & P, U. M. (2024). SmartScanPCOS: A feature-driven approach to cutting-edge prediction of Polycystic Ovary Syndrome using Machine Learning and Explainable Artificial Intelligence. Heliyon, 10(20), e39205. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e39205

Gandía, J. A. G., Ancillo, A. de L., & Núñez, M. T. del V. (2025). The Role of Artificial Intelligence and Knowledge in Enhancing Corporate Sustainability. Journal of Innovation & Knowledge, 10(5), 100792. https://doi.org/https://doi.org/10.1016/j.jik.2025.100792

Ghaemi Asl, M., Ben Jabeur, S., & Ben Zaied, Y. (2024). Analyzing the interplay between eco-friendly and Islamic digital currencies and green investments. Technological Forecasting and Social Change, 208, 123715. https://doi.org/https://doi.org/10.1016/j.techfore.2024.123715

Ghaemi Asl, M., Ben Jabeur, S., Hosseini, S. S., & Tajmir Riahi, H. (2024). Fintech’s impact on conventional and Islamic sustainable equities: Short- and long-term contributions of the digital financial ecosystem. Global Finance Journal, 62, 101022. https://doi.org/https://doi.org/10.1016/j.gfj.2024.101022

Ghaemi Asl, M., Nasr Isfahani, M., & Mohammadi, M. (2024). How does the mineral resource exploitation sector interact with Islamic and traditional ventures? Insights amidst the impact of green reforms and state-of-the-art technological advancements. Resources Policy, 98, 105287. https://doi.org/https://doi.org/10.1016/j.resourpol.2024.105287

Ghavi Hossein-Zadeh, N. (2025). Artificial intelligence in veterinary and animal science: applications, challenges, and future prospects. Computers and Electronics in Agriculture, 235, 110395. https://doi.org/https://doi.org/10.1016/j.compag.2025.110395

Ha, L. T. (2025). How Robotics & Artificial Intelligence development help the global mitigate energy crisis: Fresh insights from the R2 decomposed linkage method. Sustainable Futures, 10, 101044. https://doi.org/https://doi.org/10.1016/j.sftr.2025.101044

Hassan, M. M., Nag, A., Biswas, R., Ali, M. S., Zaman, S., Bairagi, A. K., & Kaushal, C. (2025). Explainable artificial intelligence for natural language processing: A survey. Data & Knowledge Engineering, 160, 102470. https://doi.org/https://doi.org/10.1016/j.datak.2025.102470

Hou, C.-H., & Liu, Y.-H. (2025). Using artificial intelligence for predictive analysis of dementia awareness among community adult learners and evaluation of dementia-friendliness in community environments. Computers in Human Behavior, 167, 108604. https://doi.org/https://doi.org/10.1016/j.chb.2025.108604

Ibrahim, M. M., Islam Khan, A. U., & Kaplan, M. (2025). From headlines to stock trends: Natural language processing and explainable artificial intelligence approach to predicting Türkiye’s financial pulse. Borsa Istanbul Review, 25(6), 1152–1165. https://doi.org/https://doi.org/10.1016/j.bir.2025.06.013

Idrees, M. A., & Ullah, S. (2024). Comparative analysis of FinTech adoption among Islamic and conventional banking users with moderating effect of education level: A UTAUT2 perspective. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100343. https://doi.org/https://doi.org/10.1016/j.joitmc.2024.100343

Khan, A. I., Al Badi, A., & Alqahtani, M. (2025). Explainable Artificial Intelligence for Computer Vision and Quantum Machine Learning. Procedia Computer Science, 258, 3723–3730. https://doi.org/https://doi.org/10.1016/j.procs.2025.04.627

Kumar, R., Singh, A., Kassar, A. S. A., Humaida, M. I., Joshi, S., & Sharma, M. (2025). Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors. CMES - Computer Modeling in Engineering and Sciences, 142(2), 1289–1349. https://doi.org/https://doi.org/10.32604/cmes.2025.059152

Lin, C. Y., & Lobo Marques, J. A. (2024). Stock market prediction using artificial intelligence: A systematic review of systematic reviews. Social Sciences & Humanities Open, 9, 100864. https://doi.org/https://doi.org/10.1016/j.ssaho.2024.100864

Lupiyoadi, R., Pramesti, M., Ikhsan, R. B., Usman, L. H., Games, D., Kurniawan, & Fakhrorazi, A. (2025). Extending UTAUT with perceived intelligence for the adoption of artificial intelligence in MSMEs and startups in Indonesia: A multi-group analysis. Journal of Open Innovation: Technology, Market, and Complexity, 11(4), 100673. https://doi.org/https://doi.org/10.1016/j.joitmc.2025.100673

Lyu, H., Shan, P., Hou, C., & Duan, S. (2025). Artificial intelligence for student performance prediction in blended learning: A systematic literature review. Neurocomputing, 658, 131659. https://doi.org/https://doi.org/10.1016/j.neucom.2025.131659

Mertzanis, C. (2025). Artificial intelligence and investment management: Structure, strategy, and governance. International Review of Financial Analysis, 107, 104599. https://doi.org/https://doi.org/10.1016/j.irfa.2025.104599

Muhammed, D., Ahvar, E., Ahvar, S., Trocan, M., Montpetit, M.-J., & Ehsani, R. (2024). Artificial Intelligence of Things (AIoT) for smart agriculture: A review of architectures, technologies and solutions. Journal of Network and Computer Applications, 228, 103905. https://doi.org/https://doi.org/10.1016/j.jnca.2024.103905

Ortega Perals, P., Maturo, F., Cruz Rambaud, S., & Sánchez García, J. (2025). The moderating role of government intervention in the relationship between investment in artificial intelligence and the development of financial markets. International Review of Economics & Finance, 103, 104452. https://doi.org/https://doi.org/10.1016/j.iref.2025.104452

Pradeep, P., Caro-Martínez, M., & Wijekoon, A. (2024). A practical exploration of the convergence of Case-Based Reasoning and Explainable Artificial Intelligence. Expert Systems with Applications, 255, 124733. https://doi.org/https://doi.org/10.1016/j.eswa.2024.124733

Puttegowda, M., & Ballupete Nagaraju, S. (2025). Artificial intelligence and machine learning in mechanical engineering: Current trends and future prospects. Engineering Applications of Artificial Intelligence, 142, 109910. https://doi.org/https://doi.org/10.1016/j.engappai.2024.109910

Roy, J. K., & Vasa, L. (2024). Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management: International Journal of Business Analytics, 11(1). https://doi.org/https://doi.org/10.4018/IJBAN.347504

Shahzad, U., Ghaemi Asl, M., Panait, M., Sarker, T., & Apostu, S. A. (2023). Emerging interaction of artificial intelligence with basic materials and oil & gas companies: A comparative look at the Islamic vs. conventional markets. Resources Policy, 80, 103197. https://doi.org/https://doi.org/10.1016/j.resourpol.2022.103197

Shawon, S. M., Neha, N. I., Jui, A. N., Dey, N., & Zubair, H. T. (2025). Advances in soil moisture measurement techniques and prediction using artificial intelligence: An extensive and systematic review. Smart Agricultural Technology, 12, 101613. https://doi.org/https://doi.org/10.1016/j.atech.2025.101613

Sofyan, A. S., Rusanti, E., Nurmiati, N., Sofyan, S., Kurniawan, R., & Caraka, R. E. (2024). Islam in business ethics research: a bibliometric analysis and future research agenda. International Journal of Ethics and Systems, 42(2), 345–377. https://doi.org/https://doi.org/10.1108/IJOES-02-2024-0058

Song, Y., Zhang, Y., Zhang, Z., & Sahut, J.-M. (2025). Artificial intelligence, digital finance, and green innovation. Global Finance Journal, 64, 101072. https://doi.org/https://doi.org/10.1016/j.gfj.2024.101072

Tabassum, F., Azim, M. I., Islam, M. R., Rahman, M. A., Ali, L., Rahman, M. M., & Hossain, M. J. (2025). Energy data security and pricing model in local energy markets using artificial intelligence. Applied Energy, 401, 126737. https://doi.org/https://doi.org/10.1016/j.apenergy.2025.126737

Talaat, F. M., Kabeel, A. E., & Shaban, W. M. (2025). Towards sustainable energy management: Leveraging explainable Artificial Intelligence for transparent and efficient decision-making. Sustainable Energy Technologies and Assessments, 78, 104348. https://doi.org/https://doi.org/10.1016/j.seta.2025.104348

Tamascelli, N., Campari, A., Parhizkar, T., & Paltrinieri, N. (2024). Artificial Intelligence for safety and reliability: A descriptive, bibliometric and interpretative review on machine learning. Journal of Loss Prevention in the Process Industries, 90, 105343. https://doi.org/https://doi.org/10.1016/j.jlp.2024.105343

Tao, M. (2024). Digital brains, green gains: Artificial intelligence’s path to sustainable transformation. Journal of Environmental Management, 370, 122679. https://doi.org/https://doi.org/10.1016/j.jenvman.2024.122679

Tavakoli, S. S., Mozaffari, A., Danaei, A., & Rashidi, E. (2023). Explaining the effect of artificial intelligence on the technology acceptance model in media: a cloud computing approach. The Electronic Library, 41(1), 1–29. https://doi.org/https://doi.org/10.1108/EL-04-2022-0094

Vahidpour, M., Daneshvar, A., Amini Khouzani, M., & Homayounfar, M. (2025). A multi-layer machine learning approach for cryptocurrency trading utilizing technical indicators and sentiment index. International Journal of Intelligent Computing and Cybernetics, 18(4), 706–730. https://doi.org/https://doi.org/10.1108/IJICC-03-2025-0128

Vieira, C., Rocha, L., Guimarães, M., & Dias, D. (2025). Exploring transparency: A comparative analysis of explainable artificial intelligence techniques in retinography images to support the diagnosis of glaucoma. Computers in Biology and Medicine, 185, 109556. https://doi.org/https://doi.org/10.1016/j.compbiomed.2024.109556

Ye, S., Khishe, M., Ibrahim, B. F., & Smerat, A. (2025). Advanced financial risk forecasting using enhanced kernel-based extreme learning machines: Tackling challenges in bankruptcy problem. Ain Shams Engineering Journal, 16(9), 103518. https://doi.org/https://doi.org/10.1016/j.asej.2025.103518

Ying, H., Pranolo, A., Nuryana, Z., & Syafitri, A. I. (2024). Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis. Telematics and Informatics Reports, 16, 100171. https://doi.org/https://doi.org/10.1016/j.teler.2024.100171

Yüksel, S., Dinçer, H., Acar, M., Ergün, E., Eti, S., & Gökalp, Y. (2024). Financial multidimensional assessment of a green hydrogen generation process via an integrated artificial intelligence-based four-stage fuzzy decision-making model. International Journal of Hydrogen Energy, 83, 577–588. https://doi.org/https://doi.org/10.1016/j.ijhydene.2024.08.140

Authors

Nurul Ain Safrizon
nurulainsafrizon@gmail.com (Primary Contact)
Vugar Abdullayev
Safrizon, N. A., & Abdullayev, V. (2026). ALGORITHMIC SHARIA COMPLIANCE: A MACHINE LEARNING FRAMEWORK FOR AUTOMATED RISK ASSESSMENT AND SCREENING IN ISLAMIC FINTECH. Journal of Moeslim Research Technik, 3(2), 183–200. https://doi.org/10.70177/technik.v3i2.4129

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