OpenActuarial

OpenActuarial is a dependency-light Python ecosystem for general actuarial workflows: experience analysis, rating and pricing models, loss modeling, tail estimation, simulation, and portfolio capital. The packages are organized around a shared core, while each library installs and runs independently.

The ecosystem

actuarialpy

The core. Experience analysis on a tidy table, plus the shared primitives — credibility, trend, completion, seasonality, financial mathematics, exposure, and underwriting margin — that ratingmodels builds on directly.

actuarialpy

ratingmodels

Pricing. Manual and experience rate build-up, credibility blending, rate indication and decomposition, GLM relativities and model evaluation, renewal constraints, and pricing scenarios with margin targets.

ratingmodels

lossmodels

Loss modeling. Severity and frequency fitting — including under deductibles and limits — and aggregate loss distributions.

lossmodels

extremeloss

Tails. Extreme-value tail estimation — peaks-over-threshold / GPD and large-claim loading.

extremeloss

risksim

Capital. Portfolio Monte Carlo simulation and risk measures.

risksim

The workflow

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
    

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: numpy everywhere, pandas in the core and ratingmodels, scipy where the loss and tail work needs it.

A cross-package example

Blend an experience rate with a manual rate and read the indicated change:

import actuarialpy as ap
import ratingmodels as rm

# credibility from exposure (lives in actuarialpy; ratingmodels delegates to it)
z = ap.limited_fluctuation_z(exposure=96_000, full_credibility_standard=120_000)

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()        # blended, credibility-weighted change
indication.rate_change_decomposition()    # attribute the change to each driver

Install

pip install actuarialpy ratingmodels lossmodels extremeloss risksim

Each package installs independently; ratingmodels pulls in actuarialpy as a dependency.