Large Language Models In The Financial Industry

Eden AI
3 min readFeb 24, 2024
Photo by Chris Liverani on Unsplash

Large language models (LLMs) have emerged as a powerful tool with many applications across industries, including finance. These models include many great tools such as OpenAI’s GPT-3, Anthropic Claude, Google’s Gemini, and Meta’s Llama 2, which are trained on vast amounts of text data and can generate human-like text, perform language-based tasks, and even aid in decision-making. In the financial sector, LLMs are revolutionising various processes, from customer service and risk assessment to market analysis and trading strategies. This post explores the role of LLMs in the financial industry, highlighting their potential benefits, challenges, and future implications.

What Are LLMs

Large language models (LLMs) are smart computer programs that learn from lots of text to understand and create human-like language. They’re built using transformer technology, which lets them understand entire pieces of text at once, unlike older models that went word by word. This makes them faster to train, especially when using powerful GPUs. Businesses use LLMs for tasks like customer service, market analysis, and making better decisions.

How Do LLMs Work

LLMs work by representing words as special numbers (vectors) to understand how words are related. Unlike older models, LLMs can tell when words have similar meanings or connections by placing them close together in this number space. This helps LLMs understand the meaning of words and phrases in text. Using this understanding, LLMs can create human-like language and do different tasks, making them helpful tools for businesses in areas like customer service and decision-making.

LLMs In The Financial Industry

LLMs help the financial industry by analysing text data from sources like news and social media, giving companies new insights. They also automate tasks like regulatory compliance and document analysis, reducing the need for manual work. LLM-powered chatbots improve customer interactions by offering personalised insights on finances. These tools also drive innovation and efficiency in businesses by offering features like natural language instructions and writing help. Overall, LLMs are changing the financial industry for the better by improving decision-making, compliance, customer interactions, and efficiency.

Examples of LLMs In The Financial Industry

BloombergGPT and FinGPT are advanced models used in finance language processing, but they differ in their approach and accessibility.


Developed by Bloomberg, BloombergGPT is a closed-source model that excels in automating and enhancing financial tasks. It offers exceptional performance but requires substantial investments and lacks transparency and collaboration opportunities.


In contrast, FinGPT is an open-source alternative focused on accessibility and transparency. It automates real-time financial data collection from various sources, simplifying data acquisition. FinGPT is cost-effective and adapts to changes in the financial landscape through reinforcement learning.

BloombergGPT is powerful but limited in accessibility, FinGPT is a cost-effective, open-source alternative that emphasises transparency and collaboration, catering to different needs in financial language processing.

Large language models (LLMs) represent a significant advancement in the financial industry, offering new capabilities to analyse data, enhance decision-making processes, and improve customer interactions. However, this is not without challenges, but despite these challenges, the potential benefits of LLMs in finance are vast, and organisations that embrace this technology stand to gain a competitive edge. To learn more, reach out to us today at or get in touch via and we will assist you.

This post was enhanced using information from:

AWS What are Large Language Models (LLM)?

Turintech How can LLMs bring value to the financial industry

Dhingra, G. (2023) LLMs in Finance: BloombergGPT and FinGPT — What You Need to Know



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