Banking Simulation Lab

Mesa Liquidity Data Generator

Generate synthetic customers, accounts, transactions, and liquidity metrics entirely in your browser. No Python runtime, cloud APIs, analytics scripts, or external charting libraries are required.

100% local Runs offline from `index.html`.
Monte Carlo Liquidity forecast with local simulation.
CSV export Take generated data into class exercises.

Controls

Customers
1000
Transactions
10000
Monte Carlo iterations
1000
Teaching delay per update (seconds)
0.01

This slows each progress-update batch, not each individual iteration. It helps students watch the simulation unfold without making the run excessively long.

The AI toggle uses local heuristic guidance only, so the demo stays fully self-contained.

Ready for offline browser execution

Executive Snapshot

What this run means

Generate a dataset to see customer mix, balance structure, and liquidity pressure points translated into a simple treasury narrative.

Dominant segment Not generated

No customer mix yet.

Most common account type Not generated

Generate data to compare deposit and credit-heavy books.

Highest attention area Awaiting simulation

Monte Carlo will highlight the required liquidity buffer.

Forecast

Monte Carlo liquidity outlook

Generate a dataset first, then run the forecast to estimate 30-day liquidity outcomes.

Mean final cash Not run yet
5% VaR Not run yet
Recommended buffer Not run yet
Most likely value Not run yet

Simulation Output

Monte Carlo Simulation

Waiting for a run
Click Run Monte Carlo Simulation after generating data. The results will appear here and this panel will jump into view automatically.
No simulation running 0 / 0
Work formula 0 × 30 days
Total path steps 0
Elapsed time 0.00s
Current throughput 0 iterations/s
Run the simulation to see the spread of 100 forecast outcomes across worst-case, base-case, and upside bands.

Outcome Density

Smoothed outcome distribution with the mean marked for quick interpretation.

Run the simulation to draw the outcome distribution and identify the most likely terminal value.

Mean Convergence

Tracks how the running mean stabilizes as iterations accumulate.

The convergence line will update while the simulation is running.
Treasury interpretation

No liquidity buffer recommendation yet.

Customers 0
Accounts 0
Transactions 0
Total transaction value ₹0
Average daily balance ₹0
Liquidity ratio 0%
Transaction velocity 0.00

Customer segments

Distribution of generated customers by segment.

Liquidity risk buckets

Accounts grouped into low, medium, and high liquidity risk.

Customers

Showing the first 100 generated customers.

Customer ID Name Segment Annual Income KYC Status

Accounts

Showing the first 100 generated accounts.

Account ID Type Currency Current Balance Available Balance

Transactions

Showing the first 100 generated transactions.

Transaction ID Type Amount Channel Status

Liquidity metrics

Showing the first 100 generated account-level liquidity records.

Account ID Liquidity Ratio Risk Score Stress Survival Days