🏦 Banking Risk Analytics: Monte Carlo Simulation & Stress Testing

Quantifying Financial Exposure and Capital Resilience

Data Strategist | Risk Management & Predictive Modeling

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📈 Executive Summary

This project implements a Monte Carlo Simulation framework to assess credit risk and portfolio exposure. By simulating 10,000 market scenarios, we quantify the Value at Risk (VaR) and evaluate bank solvency under extreme volatility.

📊 Strategic Risk Scenarios

1. Normal Market Operations

Low volatility and stable default rates. The bank maintains a healthy profit margin.

2. Market Crisis (Stress Test)

Simulating a high-volatility event (Pandemic/Financial Crisis). We identify a 97% failure probability, highlighting critical capital vulnerabilities.

3. Strategic Rescue (Recovery)

Implementation of Risk-Based Pricing. By adjusting interest rates, we restore the net profit margin and mitigate the impact of the crisis.

🛠️ Tech Stack

• Python (NumPy, Pandas)
• Statistical Modeling (Normal & Beta Distributions)
• Data Visualization (Seaborn, Matplotlib)

📈 Key Insights for Banking

• VaR 95% Calculation: Identifies the maximum potential loss in extreme scenarios.
• Sensitivity Analysis: Measures how changes in default rates affect total capital.
• Strategic Decision Making: Data-driven interest rate optimization.