# Mission: AI in Finance

## Why

Become **AI-in-finance literate** — the informed generalist, not the builder. Hold a
sharp conversation on the topic, read the news and the sales pitches critically, and
tell substance from hype. When a headline says "AI is transforming banking," know
exactly which AI, doing what, and what could go wrong.

## Success looks like

- Take any "AI in finance" claim and place it: what kind of AI is it, what job is it
  doing, and is that job old-and-solved or new-and-shaky?
- Name the main applications (fraud detection, credit scoring, algo trading, robo-advice,
  document work) and roughly how each works — without writing code
- Spot the built-in risk of a given use: bias, black-box, hallucination, overfitting,
  systemic correlation
- Separate genuine capability from marketing — recognise the tells of "AI-washing"
- Follow a regulator's or a journalist's argument about AI risk in markets and push back
  where it's thin

## Constraints

- **Generalist track.** No coding, no math derivations. Concepts, vocabulary, and
  judgment — not implementation.
- Lessons short enough to finish in one sitting.
- Every claim cited to a high-trust source (regulators, standard-setters, primary
  research) — this is a topic drowning in vendor hype, so provenance matters more than usual.

## Out of scope

- Building or training models; ML math; Python/quant tooling
- Investment advice or specific stock/fund calls
- Deep macro finance theory beyond what a use case needs