Large Language Models (LLM) and Islamic Finance

The Large Language Model (LLM) has great potential for Islamic finance, a sector that is grounded in Sharia principles that emphasizes ethics, transparency, and risk sharing, but its application is still underexplored. This study examines how LLM improves transparency, risk management, and customer experience in Islamic finance using a qualitative method approach. A systematic literature review was conducted through the Scopus database, selecting peer-reviewed articles relevant to LLM, Islamic finance, as well as tasks such as sentiment analysis and risk assessment. Articles are filtered by title/abstract, full text, with data extracted for research purposes, methodology, findings, and limitations, organized into themes. Thematic and quantitative analyses synthesize findings to propose an LLM integration framework, addressing challenges such as hallucinations and data privacy with techniques such as Labeled Guide Prompting and DP-LoRA. The findings show LLMs such as ChatGPT-4o, TAT-LLM, and FinQAPT automate audits, improve risk management through predictive analytics for profit-sharing contracts, and provide culturally sensitive financial advice, outperforming traditional methods in efficiency and objectivity. The framework offers practical implications for financial institutions, advancing ethical AI in the Sharia financial system while emphasizing human oversight for reliability and compliance.