AI Hedge Orchestrator

Learning objectives

• Explain the main treasury decision that AI Hedge Orchestrator is designed to support.
• Use Market condition, AI guidance to test how different assumptions change the scenario.
• Interpret optimization Summary in plain language and connect them to an action or conclusion.
• State one limitation, risk, or governance consideration before using the result in a real decision.

Run modes

• Supported modes: Browser
• Demo type: Interactive browser demo
• Primary launch surface: index.html

Before you start

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

This helps set classroom expectations before students click into the live experience.

Core context

Look for exposure, hedge instrument, hedge ratio, cost, and residual risk.

Observe how the recommended hedge changes under FX, rate, or cash-flow scenarios.

Concepts covered

What students should note

Note that hedging is about reducing unwanted risk, not eliminating every uncertainty.

List of steps

• Choose the run mode that fits the class: Browser.
• Review the default assumptions before changing anything.
• Change one or two inputs, then use `Optimize Hedge Ratios`.
• Read optimization Summary first, then compare any supporting metrics, charts, or AI text.
• Capture one insight, one limitation, and one action recommendation.

Input variables explained

• `Market condition` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `AI guidance` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.

Decision buttons explained

• `Optimize Hedge Ratios` is the main action that computes, compares, or generates the next result from the current inputs.
• `Export Results` saves the current result so learners can document evidence or compare scenarios later.
• `Reset Demo` returns the demo to a known starting state so students can begin a fresh comparison.

Outputs explained

• `Hedge Recommendations` are the primary result because they connect each exposure slice to a hedge choice and ratio.
• `Coverage and Decision Trace` explains how much risk is covered, what remains unhedged, and how the recommendation was reached.
• `Optimization Summary` gives the top-line before students drill into the detailed exposure book and recommendation logic.

What to notice

• A lower residual risk can still come with higher cost, so students should discuss trade-off rather than assume one best answer
• Compare AI guidance with the market-condition setting to see whether the recommendation becomes more conservative or more opportunistic
• Use the decision trace to explain the recommendation, not just to report the final hedge ratio

Discussion and reflection

• What business or technical decision would you make differently after using AI Hedge Orchestrator?
• If you changed one assumption and ran `Optimize Hedge Ratios`, which output moved the most and why?
• What would you still want to validate with real data, policy, or expert review before acting on the result?