Financial Risk Analyzer

A comprehensive financial risk analysis application for quantifying and visualizing market, credit, operational, liquidity, and systemic risks.

Overview

The Financial Risk Analyzer is a web-based dashboard that provides advanced risk analytics for financial institutions. It implements various risk models and presents the results in an interactive dashboard.

Features

• Market Risk: Value at Risk (VaR), Expected Shortfall, Volatility Forecasting
• Credit Risk: Probability of Default, Loss Given Default, Exposure at Default
• Operational Risk: Key Risk Indicators, Loss Distribution Approach
• Liquidity Risk: Liquidity Ratios, Stress Testing, Gap Analysis
• Systemic Risk: Network Analysis, Contagion Modeling, Vulnerability Assessment

Synthetic Data

The application includes a comprehensive synthetic data generation system that creates realistic financial datasets for all risk types. This allows for testing and demonstration without requiring real financial data.

Generated Datasets

• Market Data: Historical prices and returns for multiple assets with realistic correlations and volatility patterns.
• Credit Data: Firm characteristics, financial ratios, and default probabilities for a portfolio of companies.
• Operational Risk Data: Loss event history and Key Risk Indicators (KRIs) with trends.
• Liquidity Data: Balance sheet information for financial institutions over time.
• Systemic Risk Data: Interbank exposure network and capital buffers for contagion analysis.

How to Generate Data

To generate fresh synthetic data:

python data/synthetic_data.py

This will create CSV files in the data directory for all risk types, as well as visualizations like correlation heatmaps and network graphs.

Using the Data

The synthetic data is automatically used by the application. When you run the application, it will:

1. Attempt to load the synthetic data
2. Generate risk analysis results using the data
3. Display the results in the dashboard

To access the raw synthetic dataset via API:

GET /synthetic_data

Installation and Setup

1. Clone the repository:

git clone https://github.com/username/financial_risk_analyzer.git
cd financial_risk_analyzer

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python app.py

4. Access the dashboard at http://localhost:5000

Technology Stack

• Backend: Python, Flask
• Risk Models: NumPy, pandas, scikit-learn, NetworkX, arch
• Data Visualization: Plotly.js, Seaborn
• Frontend: Bootstrap 5, jQuery

API Documentation

The application provides a RESTful API for all risk calculations:

• Market Risk: /market_risk/var, /market_risk/volatility
• Credit Risk: /credit_risk/default_probability, /credit_risk/lgd
• Operational Risk: /operational_risk/kri, /operational_risk/lda
• Liquidity Risk: /liquidity_risk/ratios, /liquidity_risk/stress_test
• Systemic Risk: /systemic_risk/correlation_network, /systemic_risk/contagion
• Synthetic Data: /synthetic_data

License

MIT

Project Structure

financial_risk_analyzer/
│
├── app.py                 # Main application entry point
├── config.py              # Configuration settings
├── requirements.txt       # Project dependencies
│
├── models/                # Risk modeling modules
│   ├── market_risk.py
│   ├── credit_risk.py
│   ├── operational_risk.py
│   ├── liquidity_risk.py
│   └── systemic_risk.py
│
├── utils/                 # Utility functions
│   ├── data_loader.py
│   ├── data_processor.py
│   └── visualization.py
│
├── static/                # Static files
│   ├── css/
│   ├── js/
│   └── img/
│
├── templates/             # HTML templates
│   ├── index.html
│   ├── dashboard.html
│   └── components/
│
└── tests/                 # Unit and integration tests
    ├── test_market_risk.py
    └── test_credit_risk.py

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.