The application of IAG in finance is no longer just theory: risk analysis startups, investment funds, and even traditional banks are integrating generative models as co-pilots to interpret reports, build dashboards, predict scenarios, and automate compliance.
But... how safe, accurate, and useful are they really?
What financial tasks can a generative model undertake?
- Balance sheet analysis : interpreting ratios, highlighting anomalies, comparing with benchmarks.
- Earnings summaries : generating insights from quarterly calls.
- Report generation : with sections in natural language + Excel tables.
- Scenario simulation : using descriptions to modify parameters.
- Process automation : KYC, contract drafting, compliance review.
What models are being used?
- GPT-4.5 : very powerful in numerical reasoning + text generation.
- Claude 3 Opus : superior accuracy in long documents and legal structures.
- Sonar Pro : veracity and traceability with sources, useful for risk and compliance.
- FinGPT : an open source model adapted to the financial domain, still evolving.
Current limitations
- They don't do calculations on their own : they need integration with spreadsheets or code.
- Financial terms can be "confused" if they are not well defined in the prompt.
- Privacy and compliance : Sending sensitive financial data to a closed model is a risk (except with private environments).
Where do they shine?
In textual analysis tasks (reports, contracts, emails) and as co-pilots for analysts, IAG already offers tangible improvements in time and clarity.
They won't replace the CFO yet, but they are already indispensable for any financial analyst who wants to optimize their workflow.
We recommend reading our article about text generation APIs: how to use IAG in your own product (without knowing much about code).