OpenActuarial

Open-source actuarial tooling for Python — a small, composable ecosystem for experience analysis, pricing, loss modeling, and capital, organized around a shared core.

📚 Documentation: openactuarial.org · License: MIT

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The ecosystem

Package	What it does	Install
actuarialpy	Experience analysis and the shared core — per-exposure and loss-ratio metrics, trend, completion, seasonality, credibility, financial mathematics (time value of money), and exposure. ratingmodels builds directly on it.	pip install actuarialpy
ratingmodels	Group rate build-up and indication — manual and experience rating, an auditable build-up engine, GLM relativities, retention gross-up, and renewal.	pip install ratingmodels
lossmodels	Loss-distribution modeling — severity and frequency fitting, and aggregate loss.	pip install lossmodels
extremeloss	Extreme-value tail estimation — peaks-over-threshold / GPD and large-claim loading.	pip install extremeloss
risksim	Portfolio Monte Carlo simulation and risk measures.	pip install risksim

How they compose

Left to right, the packages trace one analysis — experience, pricing, loss, tail, and capital:

flowchart LR
    AP["actuarialpy<br/>experience"]:::core
    RM["ratingmodels<br/>pricing"]
    LM["lossmodels<br/>loss"]
    EL["extremeloss<br/>tail"]
    RS["risksim<br/>capital"]
    AP --> RM --> LM --> EL --> RS
    classDef core fill:#eaf2ff,stroke:#3a6ea5,stroke-width:2px,color:#1a1a1a

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The arrows are the analytical sequence, not install requirements. actuarialpy is the shared core — credibility, trend, financial math, and exposure live there once — and ratingmodels builds directly on it. lossmodels, extremeloss, and risksim install independently (extremeloss can optionally integrate lossmodels for severity splicing). Dependencies stay light throughout: numpy and pandas, with scipy where the loss and tail work needs it.

A workflow across the ecosystem

Credibility comes from the core; the rate build-up and indication come from ratingmodels — the two compose directly:

import actuarialpy as ap
import ratingmodels as rm

# core: credibility for the group's own experience
z = ap.limited_fluctuation_z(exposure=96_000, full_credibility_standard=120_000)

# pricing: build the manual rate, blend against experience, and indicate
manual = rm.ManualRate(base_loss_cost=480, factors={"area": 1.05, "industry": 0.97})

indication = rm.RateIndication(
    experience_loss_cost=512,
    manual_loss_cost=manual.loss_cost(),
    credibility=z,
    current_rate=560,
    target_loss_ratio=0.85,
)

indication.indicated_rate_change()       # the indicated rate change
indication.rate_change_decomposition()   # an auditable explanation of why it moved

Getting started

Install any package with pip (above). Each has its own guide and full API reference at openactuarial.org.

License

All packages are released under the MIT License.