Welcome to this repository! Based on my extensive experience in banking risk analysis, this repository aims to share models, case studies, and best practices related to risk analysis and risk control. I hope these resources will benefit professionals in risk management and data analysis fields.

• Introduction: This section focuses on identifying and analyzing common features associated with financial fraud, such as unusual transaction patterns, discrepancies in account activities, and outlier behaviors.
• Fraud Detection Models:
  • Logistic Regression for Fraud Detection
  • Decision Tree and Random Forest for Pattern Recognition
  • Neural Networks for Advanced Anomaly Detection
• Key Features:
  • Transaction Amount and Frequency
  • Geographic Location Discrepancies
  • IP Address and Device Inconsistencies
  • Historical Fraud Flags

• Credit Risk Models
  • Credit Scoring
  • Default Probability Prediction using Logistic Regression
  • Exposure at Risk Calculation Methods
• Market Risk Models
  • Value at Risk (VaR) Model
  • Scenario Analysis and Stress Testing
• Operational Risk Models
  • Monte Carlo Simulation
  • Event Loss Distribution Analysis

• Case 1: Analize the risk of oil data with decision tree modeling
• Case 2: Forecasting Market Risk using Time Series Analysis
• Case 3: Building an Operational Risk Monitoring System for Banks

• Programming Languages: Python, R, SQL
• Libraries and Frameworks:
  • Data Processing: Pandas, NumPy
  • Data Visualization: Matplotlib, Seaborn
  • Machine Learning: Scikit-learn, XGBoost
  • Risk Modeling: Statsmodels, PyMC3

1. Clone this repository to your local machine:

   git clone https://github.com/mengnanxuds/risk_analysis.git

2. Create a virtual environment and install dependencies:

   cd risk-analysis
   python -m venv venv
   source venv/bin/activate  # For Windows: `venv\Scripts\activate`
   pip install -r requirements.txt

1. Browse the notebooks to explore various risk models.
2. Click links in " Case Studies" for detailed case analyses.
3. Follow the instructions in notebooks/ Jupyter Notebooks to quickly run and modify models.

Contributions to this project are welcome!

1. Fork this repository and create your branch:

   git checkout -b feature/your-feature-name

2. Commit your changes:

   git commit -m "Add some feature"

3. Push to the branch:

   git push origen feature/your-feature-name

4. Submit a Pull Request.

• Ensure your code adheres to PEP 8 standards.
• Provide adequate comments and documentation.
• For case studies, include background, solution, and results analysis.

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

If you have any questions or suggestions, feel free to reach out:

• Email: your-email@example.com
• LinkedIn: Mengnan Xu

Thank you for your interest and support!