OpenActuarial

OpenActuarial is a dependency-light Python ecosystem for general actuarial workflows, including experience analysis, rating and pricing models, loss modeling, tail estimation, simulation, and portfolio capital. The packages are modular and can be installed individually, with actuarialpy providing shared foundational components for packages that require them.

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 with diagnostics, frequency–severity models, validation splits and tables, renewal constraints, dislocation reporting, 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).

The full arc is runnable end to end — see the worked examples: Example 1: experience to a renewal rate, Example 2: pricing a book, in columns, Example 3: claims to capital, Example 4: censored payments to coverage terms, Example 5: triangle to indicated change, and Example 6: pricing the tail, with error bars.

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

Every numeric argument above also accepts a column — the same call prices a whole book; see Example 2: pricing a book, in columns.

Install

pip install actuarialpy ratingmodels lossmodels extremeloss risksim

Each package installs independently; ratingmodels pulls in actuarialpy and statsmodels as dependencies.