- Introduction
- Features
- Technologies
- Methodology
- Financial Metrics
- Data Analysis
- Recommendations
- Installation
- Usage
This project serves as a comprehensive risk assessment tool in the insurance domain. Built on strong financial and data analysis principles, it employs Python's powerful libraries to deliver actionable insights. The aim is to assist insurance companies in premium determination, risk segmentation, and operational decision-making.
- Comparative Risk Analysis for Drivers
- Vehicle Risk Assessment
- Geographical Risk Mapping
- Operational Metrics for Agencies and Branches
- Python 3.x
- Pandas for Data Manipulation
- Numpy for Numerical Operations
- Jupyter Notebook for Interactive Analysis
The project adopts a rigorous data analysis approach, including but not limited to:
- Data Cleaning
- Data Grouping
- Statistical Testing
- Financial Ratio Calculation
Two key financial metrics are used to evaluate the risk:
-
Loss Ratio:
$\frac{{\text{{Claim Amount}}}}{{\text{{Total Premium}}}}$ -
Combined Ratio:
$\frac{{\text{{Claim Amount}} + \text{{Expense Amount}}}}{{\text{{Total Premium}}}}$
Utilizing Python's Pandas and Numpy libraries, the data analysis is both robust and detailed. Specific Python functions are created for:
- Calculating Loss and Combined Ratios
- Grouping Data by Age, Gender, Marital Status, etc.
- Generating Comparative Risk Profiles
Based on the rigorous data and financial analysis, the project provides targeted recommendations for:
- Premium adjustments for specific risk groups
- Operational improvements for underperforming agencies
- Strategic focus areas for risk mitigation
- Clone the GitHub repository:
git clone https://github.com/zhangqi0210/Risk_Assessment_in_Insurance.git my-project - Navigate to the project directory:
cd my-project
To run the Jupyter Notebook:
- Launch Jupyter Notebook:
jupyter notebook - Open the
Age_Gender_Marital.ipynbnotebook from the dashboard - Execute the cells to perform the analysis