Interest Rate Risk

Learning objectives

• Explain the main banking decision that Interest Rate Risk is designed to support.
• Use Synthetic accounts, Total portfolio size (INR crore) to test how different assumptions change the scenario.
• Interpret account Type Table, ai Insight 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, Colab, Local
• Demo type: Multi-mode demo
• Primary launch surface: index.html

Before you start

Browser mode is fastest to start. Colab and Local modes may spend extra time installing packages or opening notebooks.

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: Browser, Colab, Local.
• Review the default assumptions before changing anything.
• Change one or two inputs, then use `Build Portfolio`.
• Read account Type Table first, then compare any supporting metrics, charts, or AI text.
• Capture one insight, one limitation, and one action recommendation.

Input variables explained

• `Synthetic accounts` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Total portfolio size (INR crore)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Rate shock (basis points)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Enable AI-style interpretation` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Savings` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Current` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Fixed Deposit` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Recurring Deposit` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.

Decision buttons explained

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

Outputs explained

• The most important outputs are the post-shock annual interest cost, the exposure mix by account type, and any AI-style interpretation of repricing pressure.
• Students should compare current versus shocked cost rather than read either number in isolation.

What to notice

• 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
• Note the limitation students should mention before applying the result in a real decision
• Compare the headline output with supporting views such as account Type Table, ai Insight before drawing a conclusion

Discussion and reflection

• What business or technical decision would you make differently after using Interest Rate Risk?
• If you changed one assumption and ran `Build Portfolio`, 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?