SAHM: Arabic Financial Instruction-Tuning
Dataset and Models

Figure 1: Overview of the SAHM benchmark. The diagram illustrates the seven core Arabic financial NLP tasks and their thematic coverage across Islamic finance, accounting, and business domains.

Abstract

We present SAHM, the first large-scale benchmark and instruction-tuning dataset for Arabic financial NLP. Despite advances in English benchmarks such as FiQA, FinQA, and FinChain, there is no comprehensive resource for evaluating large language models (LLMs) on Arabic financial and Shari’ah-compliant reasoning. SAHM fills this gap through seven expert-verified tasks covering Islamic Finance Shari’ah Standards QA, Fatwa QA and MCQ, Business and Accounting MCQ, Financial Report Sentiment Analysis, Extractive Summarization, and Event–Cause Reasoning QA. All data are derived from authentic Arabic regulatory, juristic, and corporate sources and curated via a hybrid LLM–human pipeline ensuring linguistic fidelity and legal–financial accuracy. We benchmark 13 proprietary, open-source, and Arabic-centric LLMs, showing that proprietary models (e.g., GPT-5) outperform others in open-ended reasoning, while Arabic-tuned models achieve higher accuracy on domain-specific MCQs but struggle with causal and doctrinal reasoning. By combining financial, legal, and linguistic perspectives, SAHM provides the first holistic evaluation suite for Arabic financial NLP, establishing a foundation for reproducible, culturally grounded research and future instruction-tuned model development.

Figure 2: Illustrative examples — juristic QA, fatwa QA/MCQ, business & accounting MCQs, financial sentiment, extractive summarization, and event–cause QA.

Figure 3: Data pipeline—AAOIFI book → OCR → text files → LLM draft QA → expert verification and legal compliance.

Tasks Covered

• Islamic Finance Shari’ah Standards QA
• Islamic Financial Fatwa QA
• Islamic Financial Fatwa MCQ
• Business MCQ
• Accounting MCQ
• Financial Report Sentiment Analysis (MCQ)
• Report Extractive Summarization
• Event–Cause Reasoning QA

Highlights

• First comprehensive Arabic financial NLP benchmark unifying modern finance with Shari’ah-compliant reasoning.
• Native-speaker–verified datasets with normalization scripts and standardized evaluation protocols.
• Benchmarks 13 LLMs and reveals gaps in causal inference, hybrid legal–financial reasoning, and doctrinal fidelity.

Citation

If you find this work useful, please cite: