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

High-Performance Computing

Designed for speed and efficiency, enabling high-performance numerical computing, quantitative analysis, modeling, and simulation.

Domain-Specific Ecosystem

Extensive ecosystem of libraries and tools catering to technical computing, data science, machine learning, and domain-specific tasks in finance and actuarial science.

Productivity and Interoperability

Clean, readable syntax, comprehensive documentation, and seamless integration with existing languages and tools enhance productivity and accessibility for a wide range of users.

JuliaActuary is an ecosystem of packages that makes Julia the easiest language to get started for actuarial workflows.

Julia is an ideal language for Actuaries and other financial professionals.

It is free, open-source software and you can join the development on Github.

Code Examples

• Getting Mortality Tables

  julia> using MortalityTables
  
  julia> MortalityTables.table("2015 VBT Smoker Distinct Male Non-Smoker ALB")
  MortalityTable (Insured Lives Mortality):
     Name:
         2015 VBT Smoker Distinct Male Non-Smoker ALB
     Fields:
         (:select, :ultimate, :metadata)
     Provider:
         American Academy of Actuaries along with the Society of Actuaries
     mort.SOA.org ID:
         3269
     mort.SOA.org link:
         https://mort.soa.org/ViewTable.aspx?&TableIdentity=3269
     Description:
         2015 Valuation Basic Table (VBT) Smoker Distinct Table...

• Working with Yield Curves

  using FinanceModels
  
  maturities = [0.5, 1.0, 1.5, 2.0]
  rates = [5.0, 5.8, 6.4, 6.8] ./ 100
  quotes = ZCBYield.(rates, maturities)
  
  curve = fit(Spline.Linear(), quotes, Fit.Bootstrap())
  discount(curve, 2.0)          # discount factor to year 2
  zero(curve, 1.5)              # continuously compounded zero rate
  forward(curve, 1.0, 2.0)      # rate between years 1 and 2

• Financial Maths

  
  julia> using ActuaryUtilities
  
  julia> cashflows = [5, 5, 105]
  julia> discount_rate = 0.03
  
  julia> round(present_value(discount_rate, cashflows); digits=2)
  105.66
  
  julia> round(duration(Macaulay(), discount_rate, cashflows); digits=2)
  2.86
  
  julia> round(duration(discount_rate, cashflows); digits=2)
  2.78
  
  julia> round(convexity(discount_rate, cashflows); digits=2)
  10.63

• Life Contingencies

  using LifeContingencies
  using MortalityTables
  using FinanceCore
  
  # load mortality rates from MortalityTables.jl
  vbt2015 = MortalityTables.table("2015 VBT Smoker Distinct Male Non-Smoker ALB")
  
  issue_age = 30
  life = SingleLife(                       # The life underlying the risk
      mortality = vbt2015.select[issue_age],    # -- Mortality rates
  )
  
  yield = Periodic(0.05, 1)            # Using a flat 5% interest rate
  
  lc = LifeContingency(life, yield)        # combine the life with the yield
  ins = Insurance(lc)                      # alternate way to construct: Insurance(life, yield)
  
  # Summary Scalars
  present_value(ins)                       # The actuarial present value
  premium_net(lc)                          # Net whole life premium
  LifeContingencies.V(lc,5)                # Net premium reserve for whole life insurance at time 5

The life-contingency example uses the compatible LifeContingencies 2.5 / FinanceCore 2 / MortalityTables 2 environment. See package compatibility before combining it with the newer finance and mortality releases.

Packages

See the current releases and migration guides, including the Julia 1.10 minimum for the latest finance and mortality packages.

MortalityTables.jl - Easily work with standard mort.SOA.org tables and parametric models with common survival calculations.

LifeContingencies.jl - Insurance, annuity, premium, and reserve maths.

ActuaryUtilities.jl - Robust and fast calculations for internal_rate_of_return, duration, convexity, present_value, breakeven, and AD-based key rate sensitivities.

FinanceModels.jl - Composable contracts, models, and functions for financial instruments — including stochastic short-rate models and Monte Carlo simulation.

ExperienceAnalysis.jl - Meeting your exposure calculation needs.

EconomicScenarioGenerators.jl - Scenario generation for existing ESG workflows. For current short-rate models, see FinanceModels 7 and the compatibility notes.