Generative AI for finance
Useful when it shortens the path to a number you can defend.
Definition
Generative AI helps in finance when it gets a person from a question to a figure they can check. It does not help when it narrates a chart, invents precision, or stands in for a risk model. Baltasar Miguel Fenoll works on that line: data scientist by training, deployment engineer by practice, based in Madrid.
Where it earns a place
- Natural-language data visualization — ask, get a chart, still see what was calculated.
- Self-service stress testing — run the test without waiting on a specialist queue.
- Analytics that already exist — route them so a decision does not wait on a deck.
Where it does not
- Replacing a risk model with a chatbot.
- Unaudited numbers in a confident tone.
- Any output a person cannot trace back to an input.
Why the math stays in the loop
B.Sc. Mathematics and Physics, University of Granada (2018–24). M.Sc. Big Data Science, University of Navarra (2024–25). The degrees are not decoration. Finance here means decisions under uncertainty. A tool that hides the uncertainty is not finished.
How that becomes software is the AI deployment note. The rest of the profile is on the home page.
Questions
Where does generative AI help in finance?
When it shortens the path from a question to a figure a person can check: natural-language charts, a stress test someone can launch themselves, or analytics that already exist and no longer wait on a deck.
Where does generative AI not belong in finance?
Replacing a risk model with a chatbot, unaudited numbers in a confident tone, or any output a person cannot trace back to an input.
What finance-related tools has he built?
Natural-language data visualization, a self-service stress-testing launcher, and work on risk and analytics. He does not publish client names or performance figures on this site.
Contact
Comparing notes on risk tooling or GenAI in finance?LinkedInor mrbaltuki@gmail.com.