Library basiclife

Overview

The basiclife library is for building life insurance projection models. The models in this library project minimum sets of cashflows of hypothetical generic plain life policies and no specific regulations are assumed. The user should customize and extend the models to meet their own needs.

The modeled product is a plain term product with no surrender value. The projected cashflows are premiums, claims, expenses and commissions. The assumptions used are mortality rates, lapse rates, discount rates, expense, inflation and commission rates. The present values of the cashflows are also calculated. The premium amount for each individual model point is calculated as the net premium with loadings, where the net premium is calculated from the present value of the claims.

The library currently includes 2 basic projection models, BasicTerm_S and BasicTerm_M. Both of the models produces the exact same results but in different ways.

The BasicTerm_S model defines and executes formulas for each model point separately, while the Model BasicTerm_M model executes each formula at each time step for all model points at once. They produce the same results for the same model point. BasicTerm_S is straight forward, and its formulas are easier to understand, but it runs slower. It’s suitable for validation purposes. BasicTerm_M is runs fast, but its formulas are expressed as vector operations and can be more complex in some places.

• Model BasicTerm_S
• Model BasicTerm_M

How to Use the Library

As explained in the Creating a Project section, Create you own copy of the basiclife library. For example, to copy as a folder named basiclife under the path C:\path\to\your\, type below in an IPython console:

>>> import lifelib

>>> lifelib.create("basiclife", r"C:\path\to\your\basiclife")

Library Contents

File or Folder	Description
model_BasicTerm_S	The BasicTerm_S model.
model_BasicTerm_M	The BasicTerm_M model.
basic_term.xlsx	An Excel file that reproduces the results of a selected model point. The file also shows the derivation of the sample mortality rates.
generate_model_points.ipynb	A Jupyter notebook used for generating the sample model points from random numbers.