projectionmodels 0.6.4

Focused actuarial claim, premium, membership, and expense projections.

projectionmodels

The renewal cycle end to end: premium, claims, and expenses projected over a horizon.

Overview

projectionmodels projects premium, claims, and expenses over a monthly horizon on exposure you supply — by group, by claim type, and at cost levels that make the pipeline order (completion, then trend, then adjustments) explicit rather than implicit.

Assumptions are first-class objects: estimate them from history with actuarialpy through the built-in adapter, or state them directly and keep the projection fully reproducible either way.

Installation

pip install projectionmodels

Requires Python 3.10 or newer.

Quick start

import pandas as pd
import projectionmodels as pm

premium_data = pd.DataFrame({
    "group_id": ["A", "B"],
    "renewal_date": pd.to_datetime(["2027-03-01", "2027-07-01"]),
    "current_premium_rate": [100.0, 100.0],
    "rate_action": [0.10, 0.20],
})

periods = pd.period_range("2027-01", periods=12, freq="M").astype(str)
exposure = pd.DataFrame(
    {"group_id": g, "projection_period": p, "member_months": 1_000.0}
    for g in ("A", "B") for p in periods
)

results = pm.PremiumProjection(
    premium_data=premium_data,
    projection_keys=["group_id"],
    exposure=exposure,
    exposure_col="member_months",
    horizon=pm.ProjectionHorizon("2027-01-01", periods=12),
    recurring_rate_action_col="rate_action",
).project()

print(results.to_frame().head())

What's inside

• Premium — renewal-date-aware rate projection with recurring rate actions.
• Claims — projection by claim type with explicit cost levels and pipeline order (complete, trend, adjust).
• Expenses — fixed and variable expense projection alongside the claim stream.
• Assumptions — assumption objects estimated from history via the actuarialpy adapter or supplied directly.
• Book and group — the same machinery at single-group and whole-book level, with results tables by period, group, and component.
• Advanced — extension points for custom models and integrations.

The full API reference and end-to-end worked examples live at openactuarial.org/projectionmodels.html.

The OpenActuarial ecosystem

projectionmodels is one of seven packages that share conventions — tidy tables, explicit distribution parameterizations, reproducible random-number handling — and compose across package seams:

Package	Role
actuarialpy	Calculation primitives the workflow packages build on
experiencestudies	Experience reporting, actual-vs-expected, claimant and concentration analysis
projectionmodels	Claim, premium, and expense projection over a renewal horizon
ratingmodels	Manual and experience rating, credibility, indication, GLM relativities
lossmodels	Severity and frequency fitting, aggregate loss distributions
extremeloss	Extreme-value tails: POT/GPD, GEV, return levels, splicing
risksim	Portfolio Monte Carlo, dependence, reinsurance contracts, risk measures

Install everything at once with pip install openactuarial.

Development

git clone https://github.com/OpenActuarial/projectionmodels
cd projectionmodels
python -m pip install -e ".[dev]"
pytest
ruff check src tests

CI runs the same gate on Python 3.10–3.14 across Linux and Windows.

Versioning and stability

All ecosystem packages are pre-1.0: minor releases may change APIs, and every release is documented in CHANGELOG.md. Current per-package API stability is tracked at openactuarial.org/stability.html.

License

MIT — see LICENSE.