Liquidity Management

Learning objectives

• Explain the main banking decision that Liquidity Management is designed to support.
• Change input assumptions and predict how the output should respond before running the demo.
• Interpret the result in plain language, not just as a number, chart, or AI recommendation.
• State one limitation, risk, or governance consideration before using the result in a real decision.

Run modes

• Supported modes: Colab, Local
• Demo type: Launch guide for Colab and Local analysis
• Primary launch surface: index.html

Before you start

The browser page opens immediately, but the actual analysis runs in Colab or Local Python and may take noticeable time because the simulation is intentionally compute-heavy.

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

Core context

Look for the main decision, data input, and output the demo is designed to explain.

Observe how changing one assumption changes the result or recommendation.

Concepts covered

What students should note

Note the limitation students should mention before applying the result in a real decision.

List of steps

• Choose the run mode that fits the class: Colab, Local.
• Review the default assumptions before changing anything.
• Change one or two inputs, then use `Run the main action`.
• Read the output first, then compare any supporting metrics, charts, or AI text.
• Capture one insight, one limitation, and one action recommendation.

Input variables explained

• Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
• Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Decision buttons explained

• Use the main run or simulate action to compute the scenario after inputs are set.
• Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Outputs explained

• Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
• Students should explain whether the output is descriptive, predictive, simulated, or recommended.

What to notice

• Use this page to set expectations, then move students into Colab or Local for the real analysis workflow
• Focus on how the liquidity position changes when assumptions around inflows, outflows, or timing are stressed
• Ask students which metric would trigger action now versus which one is more useful for trend monitoring

Discussion and reflection

• What business or technical decision would you make differently after using Liquidity Management?
• If you changed one assumption and ran `main action`, 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?