For Risk Analysts ·
What you'll accomplish
By the end of this guide, you'll be able to get working Python code for risk calculations — VaR, loss distributions, KRI trend analysis, correlation analysis — from ChatGPT, even if you have minimal Python experience. You'll unlock quantitative analysis capabilities that previously required a quant developer, and you'll run your first analysis in under an hour.
What you'll need
pip install jupyterpip install jupyter pandas numpy scipy matplotlib and press Enterjupyter notebook to start Jupyter — it will open in your browserWhat you should see: A browser window showing your file system, where you can create Python notebooks
Go to {{tool:ChatGPT.url}} and describe exactly what you want:
I'm a risk analyst with a CSV file of [describe data: e.g., 500 rows of daily P&L observations / 200 rows of loan loss events with columns: Date, Business_Unit, Loss_Amount, Risk_Category].
I need Python code to [describe analysis: e.g., calculate historical VaR and CVaR at 95% and 99% confidence levels / plot a loss distribution curve / calculate correlation between two KRI series].
Please provide:
1. Complete Python code I can paste into Jupyter Notebook
2. Explanation of what each main block does
3. What the output will show
4. How to interpret the results
What you should see: Either: output tables/charts showing your analysis, OR error messages that tell you what to fix
If you get an error:
I ran your code and got this error: [paste error message]. My data looks like this: [paste 3-5 rows of sample data]. What needs to change?
Most errors are simple: a column name mismatch, a missing package, or a data format issue.
Once the code runs correctly with sample data, adapt it for your real data:
The code works. My actual CSV file is at C:/risk_data/loss_events_2025.csv and the column names are: [list your actual column names]. Update the code to use my actual file and columns.
Troubleshooting: If code errors persist after 2–3 debug cycles, simplify the request — ask for the analysis in smaller pieces rather than all at once
1. Historical VaR:
Python code to calculate historical VaR at 95% and 99% from a CSV with Date and PnL columns. Include CVaR and a distribution histogram with VaR marked.
2. Loss distribution analysis:
Python code to fit a loss distribution to my operational loss data (CSV: Date, Amount, Category). Fit a lognormal distribution, plot it, and output mean, standard deviation, and 95th/99th percentiles.
3. KRI trend analysis:
Python code to plot 4 KRI time series from a CSV (columns: Date, KRI_1, KRI_2, KRI_3, KRI_4). Show trend lines and mark where any series crossed its threshold value [specify threshold per KRI].
4. Risk heat map:
Python code to create a risk heat map from a CSV (columns: Risk_Name, Likelihood, Impact, both scored 1-5). Color code: green=low, yellow=medium, orange=high, red=critical.
5. Correlation analysis:
Python code to calculate and visualize the correlation matrix between multiple risk indicators in my CSV. Include a heatmap.