Financial Supply Chain Risk Platform

A comprehensive platform for assessing and visualizing financial supply chain risks, providing real-time analytics and risk mitigation strategies for trade finance, cross-border payments, and currency volatility.

🌟 Features

Core Risk Assessment

• Trade Finance Gap Analysis: SME rejection rates, instrument distribution, regional growth disparities
• Cross-Border Payment Risk: Cybersecurity vulnerabilities, payment network analysis, stablecoin adoption
• Currency Volatility Management: Exchange rate fluctuations, hedging effectiveness, CBDC impact assessment

Advanced Analytics

• Systemic Risk Calculation: Correlation-adjusted risk scoring across all components
• Regional Risk Analysis: Geographic concentration and growth rate disparities
• Network Vulnerability Assessment: Payment system security and resilience metrics
• Concentration Risk Analysis: Herfindahl-Hirschman Index for instrument distribution

Interactive Dashboard

• Real-time Risk Monitoring: Live risk gauges and component breakdowns
• Historical Trend Analysis: Time series visualization of risk metrics over time
• Correlation Heatmaps: Visual representation of risk component interdependencies
• Customizable Time Ranges: 7-90 day historical analysis with interactive controls

API Integration

• RESTful API: FastAPI-based endpoints for risk assessment
• Real-time Data Generation: Realistic simulation data for testing and demonstration
• JSON Export: Risk reports and metrics in structured JSON format

🏗️ Architecture

Financial Supply Chain Risk Platform/
├── src/
│   ├── models/           # Core risk models
│   │   ├── base.py       # Base model class
│   │   ├── trade_finance.py
│   │   ├── cross_border_payment.py
│   │   └── currency_volatility.py
│   ├── data/             # Data generation and processing
│   │   └── simulation_generator.py
│   ├── risk/             # Risk assessment engine
│   │   └── risk_assessor.py
│   ├── visualization/    # Dashboard and visualizations
│   │   ├── risk_visualizer.py
│   │   └── dashboard.py
│   └── api/              # API endpoints
│       └── main.py
├── requirements.txt      # Python dependencies
└── README.md

🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Setup Instructions

1. Clone the repository

   git clone https://github.com/deluair/Financial-Supply-Chain-Risk-Platform.git
   cd Financial-Supply-Chain-Risk-Platform

2. Create a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

📊 Usage

Interactive Dashboard

Launch the interactive dashboard for real-time risk visualization:

python -m src.visualization.dashboard

Navigate to http://localhost:8050 in your browser to access:

• Risk overview with gauges and component breakdowns
• Historical trend analysis with customizable time ranges
• Correlation heatmaps showing risk interdependencies
• Interactive controls for data refresh and time range selection

API Server

Start the FastAPI server for programmatic access:

python -m src.api.main

Access the API documentation at http://localhost:8000/docs

Example API Usage

Generate Risk Assessment:

curl -X POST "http://localhost:8000/assess-risk" \
     -H "Content-Type: application/json" \
     -d '{
       "trade_finance": {...},
       "cross_border": {...},
       "currency": {...}
     }'

Generate Simulation Data:

curl -X POST "http://localhost:8000/generate-data?num_records=100"

Programmatic Usage

from src.risk.risk_assessor import RiskAssessor
from src.data.simulation_generator import SimulationDataGenerator
from src.models.trade_finance import TradeFinanceGap

# Initialize components
risk_assessor = RiskAssessor()
data_generator = SimulationDataGenerator()

# Generate simulation data
trade_data, payment_data, currency_data = data_generator.generate_all_data(num_records=1)

# Create model instances
trade_finance = TradeFinanceGap(**trade_data[0])
# ... create other models

# Generate risk report
risk_report = risk_assessor.generate_risk_report(
    trade_finance=trade_finance,
    cross_border=cross_border,
    currency=currency
)

print(f"Total Risk Score: {risk_report['risk_scores']['total_risk_score']}")
print(f"Risk Level: {risk_report['risk_level']}")

📈 Risk Metrics

Trade Finance Metrics

• Gap-to-Market Ratio: Proportion of unmet financing demand
• SME Impact Score: Effect on small and medium enterprises
• Open Account Ratio: Adoption of modern trade finance instruments
• Regional Risk: Geographic concentration using coefficient of variation
• Concentration Risk: Instrument distribution using Herfindahl-Hirschman Index

Cross-Border Payment Metrics

• Stablecoin Penetration: Digital currency adoption rates
• Real-time Payment Ratio: Modern payment system usage
• Network Vulnerability: Security assessment across payment networks
• Technology Adoption Risk: Digital transformation readiness

Currency Volatility Metrics

• Average/Maximum Volatility: Historical exchange rate fluctuations
• Exposure Risk: Unhedged currency positions
• Hedging Effectiveness: Risk mitigation through financial instruments
• CBDC Impact: Central Bank Digital Currency effects

🎯 Risk Assessment Framework

Systemic Risk Calculation

The platform uses a weighted scoring system with correlation adjustments:

Systemic Risk = (Trade Finance Risk × 0.3 + 
                Payment Risk × 0.3 + 
                Currency Risk × 0.4) × (1 + Correlation Adjustment)

Risk Thresholds

• Critical: 80-100 (Immediate action required)
• High: 60-79 (Significant mitigation needed)
• Medium: 30-59 (Monitor and plan mitigation)
• Low: 0-29 (Acceptable risk level)

Correlation Factors

• Trade Finance ↔ Cross-Border: 0.4
• Trade Finance ↔ Currency: 0.3
• Cross-Border ↔ Currency: 0.5

🛠️ Development

Project Structure

• Models: Core business logic and data structures
• Risk Assessment: Advanced risk calculation algorithms
• Visualization: Interactive dashboards and charts
• API: RESTful endpoints for integration
• Data: Realistic simulation data generation

Key Dependencies

• NumPy/Pandas: Data processing and numerical computations
• SciPy: Statistical analysis and risk calculations
• Plotly/Dash: Interactive visualizations and dashboard
• FastAPI: High-performance API framework
• TensorFlow: Machine learning capabilities (future enhancements)

Testing

pytest tests/

Code Quality

black src/
isort src/
mypy src/

🔮 Future Enhancements

• Machine Learning Integration: Predictive risk modeling using TensorFlow
• Real-time Data Feeds: Integration with live financial data sources
• Advanced Visualization: 3D risk landscapes and network graphs
• Mobile Dashboard: Responsive design for mobile devices
• Multi-currency Support: Expanded currency risk analysis
• Regulatory Compliance: Built-in compliance checking and reporting

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📞 Support

For questions, issues, or contributions, please:

• Open an issue on GitHub
• Contact the development team
• Review the documentation and examples

🏆 Acknowledgments

• Financial risk assessment methodologies based on industry best practices
• Visualization framework inspired by modern financial dashboards
• Data simulation techniques following realistic market distributions

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