AI Foundations
AI Risk Management
Neural networks, LLMs, and machine learning for markets.
In this lesson
- How to manage risk around AI systems
- Why model confidence is not a risk engine
Key takeaways
- 1Position limits and kill switches must be deterministic
- 2Model drift and bad data need monitoring
- 3Risk controls should reject trades even when AI sounds confident
Lesson summary
AI risk management means the trading or research system stays bounded even when the model is wrong.
Mental model
The core idea behind AI risk management
AI risk management means the trading or research system stays bounded even when the model is wrong. The risk layer must be more reliable than the model layer.
- How to manage risk around AI systems
- Why model confidence is not a risk engine
Mechanics
How to reason about AI risk management
Position limits, max-loss rules, and order constraints should be deterministic.
Data quality checks should detect stale feeds, missing fields, and abnormal spreads.
Kill switches should stop automation when assumptions break.
- Position limits and kill switches must be deterministic
- Model drift and bad data need monitoring
- Risk controls should reject trades even when AI sounds confident
Example
A concrete AI risk management example
A model may become highly confident during a news spike, but the risk engine can still reject orders if spreads widen beyond the allowed threshold.
Common mistakes
The usual AI risk management trap
Using model confidence as permission to increase risk confuses prediction with control. The model can be most confident during unfamiliar regimes.
Risk notes
Before you rely on AI risk management
Model drift, bad input data, delayed APIs, correlated positions, and execution slippage can compound faster than a human can intervene.
- Set hard exposure limits.
- Monitor data freshness.
- Add a kill switch.
Practice
Make AI risk management stick
- Set hard exposure limits.
- Monitor data freshness.
- Add a kill switch.
Review
Key terms
- Slippage
- The difference between expected and executed price, common in low-liquidity or fast markets.
- Machine Learning
- Algorithms that learn patterns from data instead of being explicitly programmed.
- Large Language Model (LLM)
- An AI model trained to predict and generate text from context.
- Backtesting
- Testing a strategy on historical data before risking real capital.
Source notes
Editorial references
These references are starting points for verifying the mechanisms, risk checks, and product context behind this lesson.
Before you continue
Can you do these?
- Set hard exposure limits.
- Monitor data freshness.
- Add a kill switch.
Related learning
Keep reading
Checkpoint
Finish this lesson
Pass the check to save progress, then continue through the track in order.
Lock in this lesson
Answer every question correctly to complete the lesson.
AI risk management should be separate from…