Artificial Intelligence, Large Language Models, and Machine Learning in Islamic Finance: A Bibliometric and Content Analysis Review (2018-2025)
This bibliometric and content analysis review of 45 peer-reviewed articles (2018–2025) maps the rapid growth of AI and machine learning research in Islamic finance while critically identifying a significant gap in the application of large language models and retrieval-augmented generation to AAOIFI standards, proposing a new research agenda to address this frontier.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine the world of money as a massive, bustling city. In this city, there are two very different neighborhoods. One is the "Conventional District," where banks and investors move fast, using complex algorithms and artificial intelligence (AI) to predict the future, spot fraud, and make loans. The other is the "Islamic Finance District," a place built on a different set of rules. Here, money isn't just a tool for profit; it's a trust. The rules forbid charging interest (like a fee for borrowing money), gambling on uncertain outcomes, or investing in things considered harmful. Instead, this district relies on a massive, ancient library of rules called Shariah, which acts like a strict but fair constitution for every financial deal.
Recently, the "Conventional District" started using super-smart computer brains called Large Language Models (LLMs). Think of these as digital librarians that can read millions of books in a second, understand complex stories, and even write new ones. They are changing how the conventional city works. But a big question remained: Are these super-smart computers helping the "Islamic Finance District"? Can they read the ancient rulebooks, check if a new deal is fair, and help manage the money without breaking the sacred rules? This is the mystery a group of researchers set out to solve. They wanted to see if the high-tech tools of the future had crossed the bridge into this unique financial world, or if the two were still living in separate universes.
The researchers, a team of four scholars from Morocco, decided to play detective. They didn't just guess; they went on a massive digital treasure hunt. They searched through a giant library of scientific papers (called Scopus) looking for any study published between 2018 and 2025 that mentioned Artificial Intelligence, Machine Learning, or those fancy new Large Language Models in the context of Islamic Finance. They started with a huge pile of 868 potential clues. But, like a good detective, they had to be picky. They threw out anything that wasn't a serious research paper, anything in the wrong language, or anything that didn't actually talk about the specific topic. After three rounds of strict screening, they were left with a final "corpus" of just 45 real, peer-reviewed articles.
What they found was a story of rapid growth mixed with a surprising blind spot. The field is exploding. In 2020, there were only two papers on this topic. By 2025, the number jumped to 21 papers in a single year! It's like a small campfire that suddenly became a roaring bonfire. The researchers mapped out who was building this fire. They found that Indonesia is the biggest campfire keeper, publishing the most papers. Countries like Bahrain, Jordan, and Saudi Arabia are also lighting up the map, with Bahrain's work getting the most attention from other scientists. They even drew a map of who is talking to whom, showing that researchers from Southeast Asia and the Middle East are starting to hold hands and work together, with Malaysia acting as a bridge between the two regions.
However, when the team looked closely at what these 45 papers were actually doing, they found four main themes, like four different tools being used in the workshop:
- Banking Operations: Using standard AI to check if a bank is running efficiently or to predict if a borrower will pay back their loan.
- Social Finance: Using text-searching tools to help manage charity funds (like Zakat) or to check if a contract matches religious texts.
- Capital Markets: Using complex math to predict stock prices or the value of special Islamic bonds (Sukuk).
- Ethics: Discussing how to make sure AI behaves morally according to Islamic principles.
Here is the twist, the big "aha!" moment of the paper: Despite the researchers specifically searching for the most advanced tools—Large Language Models (LLMs), Generative AI, and systems that can "retrieve" information from a database (called RAG)—none of the 45 papers actually used them.
It's as if the researchers went to a library looking for the newest, most powerful super-computers, only to find that everyone was still using abacuses and typewriters. The papers used older, simpler versions of AI, but the super-smart "digital librarians" that can read and understand the massive rulebooks of Islamic finance were completely missing. The researchers pointed out a huge, empty space in the field: no one has yet built a system that takes the 100+ official rulebooks (called AAOIFI standards) and feeds them into a super-smart AI to automatically check if a new financial product is allowed.
The paper concludes that while the field is growing fast and doing some cool things with older AI, it is missing its next giant leap. The authors suggest that the future of Islamic Finance lies in building these missing tools. They propose 15 new questions for scientists to answer, with the most important ones being: "Can we build an AI that reads the official rulebooks and checks deals in real-time?" and "Will this AI be as good as a human religious scholar at making these decisions?"
In short, the paper tells us that the "Islamic Finance District" is getting a lot of new tech, but it hasn't yet unlocked the door to the most powerful AI tools available today. The researchers are essentially handing the keys to the next generation of scientists, saying, "We've mapped the territory, and we know exactly where the biggest treasure is hidden. Now, go build the machine that can find it."
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