Financial LLM: Use Cases and Examples

A major expectation in banking is that LLMs can serve as intelligent assistants for both customers and employees. In the world of asset management, including hedge funds, mutual funds, and other investment firms, the interest in financial LLMs centers on gaining an informational edge and productivity boost. Fintech companies and startups view financial LLMs as an opportunity to differentiate their products with AI and to build new services faster. When the insurance industry speaks of a financial LLM (sometimes explicitly an "insurance LLM"), they mean a model that can understand insurance-specific language and workflows. Companies that provide financial data, analytics, and news have been quick to explore LLMs, often coining their solutions as financial LLMs to market their domain expertise. Their perspective is that a financial LLM should function as an expert financial analyst that a user can talk to on demand.

What Is a “Financial LLM”?

A financial LLM is a large language model, trained or fine-tuned on financial data, to be tailored for the finance domain, able to answer questions or generate content with an understanding of financial context, instruments, and regulations.

Such models grasp industry jargon (tickers, regulations, accounting terms), handle numeric and tabular context, and comply with financial regulations in their outputs. 

Organizations seek to apply the power of GPT-style models to banking, markets, insurance, and financial analytics while incorporating domain expertise and control. General-purpose LLMs (like GPT-4) lack certain finance-specific knowledge or precision, and companies have begun developing specialized “FinLLMs”. 

BloombergGPT was one of the first large models trained specifically on a wide range of financial data (in addition to general text).

Core Features and Capabilities of Financial LLMs

Financial LLMs can  answer questions, analyze and  summarize  text, classify sentiment or intent, check compliance, and produce financial writing.  

Financial LLMs are used to generate content tailored to finance needs: draft research reports, write personalized portfolio explanations for clients, compose client emails, or generate financial news articles. Such a model writes in a style and context that financial professionals and customers expect.

Question Answering on Financial Knowledge

It's a chat assistant that understands your world and speaks your financial language, backed by actual data.

Financial LLMs help answer questions like “What happened in Company X’s Q3 results?” or “What does Basel III actually require?” not by guessing, but by pulling answers from internal docs, or research. 

They’re built to understand finance, and talk like a human, whether you’re a banker checking policy, or an investor tracking the market. 

Most financial LLMs now prioritize auditability, because in this space you need to show where the answer came from. No black box. Just traceable output linked to source.

Document Summarization and Report Generation

It summarizes lengthy financial documents (research reports, earnings call transcripts, 10-K filings, insurance policies) into concise, clear narratives. 

A financial LLM produces an executive summary of a 100-page annual report or distils key points from an earnings call in a few sentences. This is a highly valued feature given the volume of texts. 

JPMorgan’s internally-developed DocLLM is designed to process visually complex documents and extract key information, providing summaries and answering questions about the content.

Automating report generation (writing first drafts of market commentary or credit memos) is another capability of LLM.

Sentiment Analysis and Market Insights

LLMs are getting better at pulling signals from fast sources like news, Twitter, and analyst notes.

They can tag headlines or posts as positive, negative, or neutral for a stock. That's basic for a fintech LLMs. 

Regulatory Compliance and Risk Assessment

Finance is heavily regulated. LLMs in this space need to support compliance and risk, not just generate text. Most real deployments use retrieval augmentation or guardrails to keep answers accurate and policy-aligned.

FinLLMs are used to cross-check text against rules - for example, scan loan docs for compliance issues, flag SEC or FINRA violations, and pull policy red flags from internal communications.

Financial LLMs are also used for risk checks. They parse financial statements, credit history, and reports to surface red flags or consolidate exposure data.

Domain-tuned models are safer, because they stay within boundaries: no leaks, no speculation, no policy violations.

Financial Data Extraction and Synthesis

Extracting structured data from unstructured financial text is another core capability. 

An LLM ingests a pile of earnings reports or claim forms and pulls out key fields (revenues, dates, loss amounts, etc.), performing data entry and aggregation.

These models can then synthesize data across sources by aggregating and comparing data from multiple quarterly reports to answer “How did revenue grow quarter-over-quarter?”. 

They can fill out templates or spreadsheets with information gathered from documents. This capability supports use cases like automating due diligence (consolidating data on a company from various filings) and feeding downstream analytics or models.

FinBERT (financial sentiment analysis)

FinBERT is a specialized open-source BERT-based model trained on financial text (news, filings, social media) for sentiment analysis.

FinBERT was released years ago and hasn’t been actively updated, but 2024 year’s  paper shows that it’s still useful, especially when fine-tuned and combined with a time-series model like LSTM.

FinBERT hasn’t been updated in years, however this 2024 paper shows it’s still usable when fine-tuned and combined with other models like LSTM. FinBERT is based on BERT, trained on financial text to work as a sentiment classifier, not a full LLM by current standards.

The study shows it still holds as a reliable component inside a larger pipeline. If you work with financial news and need sentiment signals, you can fine-tune it on your own data and feed the output into whatever model you already use (forecasting, scoring, classification).

Load the model, run inference on news or filings, and map the output to positive, neutral, or negative. Output can be used as a feature in trading logic: entry/exit signals, risk filters, portfolio weighting. Use cases: news sentiment on equities, regulatory sentiment for risk exposure, general signal extraction from contracts or disclosures.

For example, FinBERT can be used with QuantConnect, a cloud platform for developing, testing, and deploying algorithmic trading strategies across equities, FX, futures, options, derivatives, and crypto.

FinGPT (financial sentiment analysis)

FinGPT is an open-source financial large language model (LLM) developed by the SecureFinAI Lab at Columbia University for sentiment analysis, market trend prediction, and financial report summarization. FinGPT is a model built using transformer architecture.

The model itself hasn't been updated since 2023 due to lack of funding, but it's still being actively used. For example, in 2025, there was news about fine-tuning this model to do extra tasks like financial risk prediction via audio analysis or end-to-end trading. 

FinGPT v3.3 shows that a fine-tuned open-source model can outperform GPT-4 and earlier domain-specific models like FinBERT on narrow financial tasks without needing GPT-4 scale or cost.