AI in Finance: Scope and Examples
summary
Describes an image-first pipeline that converts S&P500 time series into pictures, trains a Mondrian image classifier for buy/no‑buy and an autoencoder that predicts the final 20% of a sequence with high precision on historical data; builds a simulated market where multi-agent RL with parameterized agents (risk‑aversion, connectivity) learns bid/ask skewing and inventory hedging that matches calibrated behavior; generates realistic payments via state‑action planning, offers an open‑source Sphinx table normalizer, and a skill‑based Docubot that interactively auto‑edits slides.
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chapters (6)
- 0:00AI foundations and goals
- 4:23Image-based decision systems
- 12:58Multi-agent reinforcement
- 24:24Synthetic data generation
- 34:44Automated document tools
- 40:55Q&A session
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