actuarialmath 1.1.0

Overview

The package comprises three sets of classes, which:

1. Implement general actuarial methods

   • Basic interest theory and probability laws

   • Survival functions, expected future lifetimes and fractional ages

   • Insurance, annuity, premiums, policy values, and reserves calculations

2. Adjust results for

   • Extra mortality risks

   • 1/mthly payment frequency using UDD or Woolhouse approaches

3. Specify and load a particular form of assumptions

   • Recursion inputs

   • Life table, select life table, or standard ultimate life table

   • Mortality laws, such as constant force of maturity, beta and uniform distributions, or Makeham’s and Gompertz’s laws

Quick Start

1. pip install actuarialmath

   • also requires numpy, scipy, matplotlib and pandas.

2. Start Python (version >= 3.10) or Jupyter-notebook

   • Select a suitable subclass to initialize with your actuarial assumptions, such as MortalityLaws (or a special law like ConstantForce), LifeTable, SULT, SelectLife or Recursion.

   • Call appropriate methods to compute intermediate or final results, or to solve parameter values implicitly.

   • Adjust the answers with ExtraRisk or Mthly (or its UDD or Woolhouse) classes.

Examples

# SOA FAM-L sample question 5.7
from actuarialmath import Recursion, Woolhouse
# initialize Recursion class with actuarial inputs
life = Recursion().set_interest(i=0.04)\
                  .set_A(0.188, x=35)\
                  .set_A(0.498, x=65)\
                  .set_p(0.883, x=35, t=30)
# modfy the standard results with Woolhouse mthly approximation
mthly = Woolhouse(m=2, life=life, three_term=False)
# compute the desired temporary annuity value
print(1000 * mthly.temporary_annuity(35, t=30)) #   solution = 17376.7

# SOA FAM-L sample question 7.20
from actuarialmath import SULT, Contract
life = SULT()
# compute the required FPT policy value
S = life.FPT_policy_value(35, t=1, b=1000)  # is always 0 in year 1!
# input the given policy contract terms
contract = Contract(benefit=1000,
                    initial_premium=.3,
                    initial_policy=300,
                    renewal_premium=.04,
                    renewal_policy=30)
# compute gross premium using the equivalence principle
G = life.gross_premium(A=life.whole_life_insurance(35), **contract.premium_terms)
# compute the required policy value
R = life.gross_policy_value(35, t=1, contract=contract.set_contract(premium=G))
print(R-S)   # solution = -277.19

Resources

1. Jupyter notebook or run in Colab, to solve all sample SOA FAM-L exam questions

2. User Guide, or download pdf

3. API reference

4. Github repo and issues