Practical, extensible, and open-source actuarial modeling and analysis.


JuliaActuary is focused on building packages that enable actuaries everywhere to build solutions using open-source tools.

Please read the blog post, Julia for Actuaries to see why Julia works so well for actuarial science.

JuliaActuary on GitHub


These packages are available for use in your project. Keep scrolling down for more info on each one.






Note: packages marked with ※ are developing: the functionality is built-out and tested, but the API may change.

For consistency, you can lock any package in its current state and not worry about breaking changes to any code that you write. Julia's package manager lets you exactly recreate a set of code and its dependencies. (More).

Adding and Using Packages

There are two ways to add packages:

More info can be found at the Pkg manager documentation.

To use packages in your code:

using PackageName


Hassle-free mortality and other rate tables.



Load and see information about a particular table:

julia> vbt2001 = MortalityTables.table("2001 VBT Residual Standard Select and Ultimate - Male Nonsmoker, ANB")

MortalityTable (Insured Lives Mortality):
       2001 VBT Residual Standard Select and Ultimate - Male Nonsmoker, ANB
       (:select, :ultimate, :metadata)
       Society of Actuaries
   mort.SOA.org ID:
   mort.SOA.org link:
       2001 Valuation Basic Table (VBT) Residual Standard Select and Ultimate Table -  Male Nonsmoker.
       Basis: Age Nearest Birthday. 
       Minimum Select Age: 0. 
       Maximum Select Age: 99. 
       Minimum Ultimate Age: 25. 
       Maximum Ultimate Age: 120

The package revolves around easy-to-access vectors which are indexed by attained age:

julia> vbt2001.select[35]          # vector of rates for issue age 35
julia> vbt2001.select[35][35]      # issue age 35, attained age 35
julia> vbt2001.select[35][50:end] # issue age 35, attained age 50 through end of table

julia> vbt2001.ultimate[95]        # ultimate vectors only need to be called with the attained age

Calculate the force of mortality or survival over a range of time:

julia> survival(vbt2001.ultimate,30,40) # the survival between ages 30 and 40

julia> decrement(vbt2001.ultimate,30,40) # the decrement between ages 30 and 40

Non-whole periods of time are supported when you specify the assumption (Constant(), Uniform(), or Balducci()) for fractional periods:

julia> survival(vbt2001.ultimate,30,40.5,Uniform()) # the survival between ages 30 and 40.5

Parametric Models

Over 20 different models included. Example with the Gompertz model

m = MortalityTables.Gompertz(a=0.01,b=0.2)

m[20]                 # the mortality rate at age 20
decrement(m,20,25)    # the five year cumulative mortality rate
survival(m,20,25) # the five year survival rate

MortalityTables package on Github 🡭


A collection of common functions/manipulations used in Actuarial Calculations.

Financial Maths

Risk Measures

Insurance mechanics

Excel Utilities

You can also copy/paste to/from Excel:

Copying to and from Excel

ActuaryUtilities package on GitHub 🡭


Common life contingent calculations with a convenient interface.


Package Overview

the mortality calculations


Basic Functions

Calculate various items for a 30-year-old male nonsmoker using 2015 VBT base table and a 5% interest rate

using LifeContingencies
using MortalityTables
using Yields
import LifeConingencies: V, ä      # pull the shortform notation into scope

# load mortality rates from MortalityTables.jl
tbls = MortalityTables.tables()
vbt2001 = tbls["2001 VBT Residual Standard Select and Ultimate - Male Nonsmoker, ANB"]

issue_age = 30
life = SingleLife(                 # The life underlying the risk
    mort = vbt2001.select[issue_age],    # -- Mortality rates

yield = Yields.Constant(0.05)      # Using a flat 5% interest rate

lc = LifeContingency(life, yield)  # LifeContingency joins the risk with interest

ins = Insurance(lc)                # Whole Life insurance
ins = Insurance(life, yield)       # alternate way to construct

With the above life contingent data, we can calculate vectors of relevant information:

cashflows(ins)                     # A vector of the unit cashflows
timepoints(ins)                    # The timepoints associated with the cashflows
survival(ins)                      # The survival vector
benefit(ins)                       # The unit benefit vector
probability(ins)                   # The probability of beneift payment

Or calculate summary scalars:

present_value(ins)                 # The actuarial present value
premium_net(lc)                    # Net whole life premium 
V(lc,5)                            # Net premium reserve for whole life insurance at time 5

Other types of life contingent benefits:

Insurance(lc,n=10)                   # 10 year term insurance
AnnuityImmediate(lc)               # Whole life annuity due
AnnuityDue(lc)                     # Whole life annuity due
ä(lc)                              # Shortform notation
ä(lc, n=5)                         # 5 year annuity due
ä(lc, n=5, certain=5,frequency=4)  # 5 year annuity due, with 5 year certain payable 4x per year
...                                # and more!

Constructing Lives

SingleLife(vbt2001.select[50])                 # no keywords, just a mortality vector
SingleLife(vbt2001.select[50],issue_age = 60)  # select at 50, but now 60
SingleLife(vbt2001.select,issue_age = 50)      # use issue_age to pick the right select vector
SingleLife(mort=vbt2001.select,issue_age = 50) # mort can also be a keyword

LifeContingencies package on GitHub 🡭


Flexible and composable yield curves and interest functions.

Provides a simple interface for constructing, manipulating, and using yield curves for modeling purposes.

It's intended to provide common functionality around modeling interest rates, spreads, and miscellaneous yields across the JuliaActuary ecosystem (though not limited to use in JuliaActuary packages).


using Yields

riskfree_maturities = [0.5, 1.0, 1.5, 2.0]
riskfree    = [5.0, 5.8, 6.4, 6.8] ./ 100     #spot rates

spread_maturities = [0.5, 1.0, 1.5, 3.0]      # different maturities
spread    = [1.0, 1.8, 1.4, 1.8] ./ 100       # spot spreads

rf_curve = Yields.Zero(riskfree,riskfree_maturities)
spread_curve = Yields.Zero(spread,spread_maturities)

yield = rf_curve + spread_curve               # additive combination of the two curves

discount(yield,1.0) # 1 / (1 + 0.058 + 0.018)

Yields package on GitHub 🡭


Meeting your exposure calculation needs.


using ExperienceAnalysis
using Dates

issue = Date(2016, 7, 4)
termination = Date(2020, 1, 17)
basis = ExperienceAnalysis.Anniversary(Year(1))
exposure(basis, issue, termination)

This will return an array of tuples with a from and to date:

4-element Array{NamedTuple{(:from, :to),Tuple{Date,Date}},1}:
 (from = Date("2016-07-04"), to = Date("2017-07-04"))
 (from = Date("2017-07-04"), to = Date("2018-07-04"))
 (from = Date("2018-07-04"), to = Date("2019-07-04"))
 (from = Date("2019-07-04"), to = Date("2020-01-17"))

Available Exposure Basis

Where period is a Period Type from the Dates standard library.

Calculate exposures with exposures(basis,from,to,continue_exposure).

ExperienceAnalysis package on GitHub 🡭



Resources to help get started.

Programming and Julia

Actuarial Usage and Examples


Each package includes examples on the Github site and in the documentation.

Walkthroughs and tutorials

Coming soon!


Help mode

You can also access help text when using the packages in the REPL by activating help mode, e.g.:

julia> ? survival

  Returns the survival through attained age to_age. The start of the 
  calculation is either the start of the vector, or attained age `from_age` 
  and `to_age` need to be Integers. 

  Add a DeathDistribution as the last argument to handle floating point 
  and non-whole ages:


  If given a negative to_age, it will return 1.0. Aside from simplifying the code, 
  this makes sense as for something to exist in order to decrement in the first place, 
  it must have existed and surived to the point of being able to be decremented.


  julia> qs = UltimateMortality([0.1,0.3,0.6,1]);

  julia> survival(qs,0)
  julia> survival(qs,1)

  julia> survival(qs,1,1)
  julia> survival(qs,1,2)

  julia> survival(qs,0.5,Uniform())

Other Repositories of Interest for Actuaries

Integration with R and Python

Julia integrates with other languages, allowing you to leverage existing scripts and packages in R via RCall and in Python via PyCall.


Help by contributing code, asking questions, or reporting issues.

Thank you for your interest in modern actuarial solutions, no matter how you participate in the community.

License and Usage

The packages in JuliaActuary are open-source and liberally licensed (MIT License) to allow wide private and commercial usage of the packages, like the base Julia language and many other packages in the ecosystem.

Pull Requests

JuliaActuary is open source; you are free to modify, use, or change your copy of the code - but if you make enhancements please consider opening a pull request (basic walkthrough here).


If you find issues, please open an issue on the relevant package's repository and we will try and address it as soon as possible.

Discussion and Questions

If you have other ideas or questions, feel free to also open an issue, or discuss on the community Zulip or Slack #actuary channel. We welcome all actuarial and related disciplines!

Other Inquiries

For more directed inquires, please send email to inquiry@JuliaActuary.org.


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