What this demo is about
Interest-rate risk is driven by balance size, product mix, and how quickly those balances reprice when market rates move.
Banking Risk • Browser Mode • 10-15 min
Interest Rate Risk
Explore how a synthetic banking portfolio reacts to interest-rate shocks by changing the account mix, portfolio size, and repricing assumptions directly in the browser.
Runs fully in browser
Colab notebook available
Local Python still supported
Learning objectives
- Understand how account mix changes repricing exposure.
- Estimate how a rate shock changes annual interest cost.
- Identify which balance categories deserve management attention first.
Available Modes
Browser
Fastest classroom mode for instant shock testing and concept explanation.
Colab
Best for a guided notebook workflow with Python, pandas, and charts.
Local Python
Best for editing the generators and analysis scripts directly.
Input Variables
100 bps
Step 1: set the portfolio scale and the size of the rate move you want students to test.
Deposit Mix
Step 2: change the account-type mix. The sliders can sum above or below 100%; the browser model normalizes them automatically. Current total: 100%.
Decision Buttons
Build Portfolio regenerates the synthetic balance book. Export CSV downloads the browser-generated portfolio. Reset Inputs returns the classroom baseline.
Total exposure
₹0
Weighted average rate
0.00%
Current annual interest
₹0
Post-shock annual interest
₹0
Shock delta
₹0
Largest repricing driver
None yet
Exposure by Account Type
Interest Rate Distribution
Output and Explanations
| Account type | Accounts | Exposure | Avg. rate | Shock delta | What it means |
|---|
Build the portfolio to see which deposit category dominates repricing risk.