This repository contains the code and resources for building and evaluating an insurance model aimed at predicting [your specific aim, e.g., insurance premiums, claim likelihood, etc.]. Built using [Programming Language/Framework].
To clone and run this application, you'll need Git and Python (which comes with pip) installed on your computer. From your command line:
# Clone this repository
$ git clone https://github.com/your-username/insurance-model.git
# Go into the repository
$ cd insurance-model
# Install dependencies
$ pip install -r requirements.txt
## Model
The model uses the Chain-Ladder method to predict future insurance claims. The Chain-Ladder method relies on the following equation:
\[
\text{Future Claims} = \text{Paid Claims} \times \left( \frac{\text{Total Claims}}{\text{Paid Claims}} \right)
\]
Where:
- \( \text{Future Claims} \) are the claims amounts that are expected in the future.
- \( \text{Paid Claims} \) are the claims amounts that have been paid till now.
- \( \text{Total Claims} \) are the estimated total claims, both paid and unpaid.
To calculate the claims development factors and ultimates, the method uses the following formula:
$$\text{CDF}_i = \frac{\text{Claims}_{i+1}}{\text{Claims}_i}$$
$$\text{Ultimate Claims}_i = \text{Claims}_i \times \text{CDF}_i$$
Note: The above equations are in LaTeX format and may not render properly in GitHub's native Markdown viewer.
**The Cauchy-Schwarz Inequality**
$$\left( \sum_{k=1}^n a_k b_k \right)^2 \leq \left( \sum_{k=1}^n a_k^2 \right) \left( \sum_{k=1}^n b_k^2 \right)$$