Best AI Models for Finance & Financial Analysis

What We Looked At

• Numerical accuracy
• Real-time market data
• Excel and spreadsheet integration
• Long document handling
• Financial reasoning depth

AI for earnings analysis

Earnings calls and 10-K filings are a good fit for Claude's context window. Paste a full transcript — typically 30,000–50,000 tokens — and ask specific questions: 'What were the top three factors management cited for the revenue miss?' 'What guidance assumptions changed from last quarter?' 'What risks did they flag most prominently?' The 200K context handles most SEC filings in a single pass. For systematic analysis across many filings, you'll want a pipeline rather than manual prompting.

Excel and financial modelling

GPT-4o through Microsoft Copilot is the practical answer for Excel workflows — writing formulas, building financial models, explaining what a formula does in plain English. For Python-based modelling (NumPy, pandas, QuantLib for options pricing), Claude and DeepSeek V4 Pro both write clean, correct financial code. DeepSeek is dramatically cheaper for high-volume code generation, though Claude's architectural reasoning is better when you're designing a model from scratch.

Important disclaimer

AI models get numbers wrong. They don't have real-time market data unless connected to a live tool. They can misread a table or pull a figure from the wrong row in a document. Don't use AI output as the sole basis for an investment decision — verify figures against primary sources. For anything regulated (investment advice, fund management, trading), run compliance review before deploying AI in any client-facing capacity.