ratingmodels

Manual and experience rating, credibility blending, and rate indication with an audit trail.

Overview

ratingmodels implements the pieces of a pricing exercise as small, inspectable objects: an experience rate with pooling and trend, a manual rate with factor application, credibility blending, a rate indication that decomposes exactly into its drivers, renewal capping, and rate build-up with a line-by-line audit trail.

It also covers the modeling side of pricing — GLM relativities, a frequency–severity model with prediction intervals, on-leveling, dislocation, and scenario evaluation — all against tidy inputs.

Installation

pip install ratingmodels

Requires Python 3.10 or newer.

Quick start

import ratingmodels as rm

# experience side: pool large claims, trend, and target loss ratio
capped, excess = rm.pool_claims([612_000, 340_000, 128_000, 96_500],
                                pooling_point=250_000)
exp = rm.ExperienceRate(
    incurred_claims=4_200_000, exposure=9_600,
    trend_annual=0.075, trend_years=1.5,
    pooled_excess=excess, pooling_charge=38.00,
    target_loss_ratio=0.85,
)

# manual side, then blend by limited-fluctuation credibility
man = rm.ManualRate(base_loss_cost=480,
                    factors={"area": 1.05, "industry": 0.97, "tier": 1.10},
                    target_loss_ratio=0.85)
z = rm.limited_fluctuation_credibility(n=9_600, n_full=12_000)

ind = rm.RateIndication(
    experience_loss_cost=exp.loss_cost(), manual_loss_cost=man.loss_cost(),
    credibility=z, current_rate=520, target_loss_ratio=0.85,
    trend_total_factor=exp.trend_factor(),
    benefit_factor=1.00, demographic_factor=1.01,
)
print(f"indicated rate   : {ind.indicated_rate():.2f}")
print(f"indicated change : {ind.indicated_rate_change():+.2%}")

# why did the rate move, and what does a 5% cap do to it?
print(ind.rate_change_decomposition().to_frame())
action = rm.renew(current_rate=520, indicated_rate=ind.indicated_rate(), cap=0.05)
print(f"proposed (capped): {action.proposed_rate:.2f} ({action.proposed_change:+.2%})")

What's inside

• Experience rating — ExperienceRate with pooling, trend, and target-loss-ratio gross-up.
• Manual rating — ManualRate with multiplicative factor application.
• Credibility and blending — limited-fluctuation credibility and experience/manual blends.
• Indication — RateIndication with an exact rate-change decomposition; renewal capping via renew.
• Rate build-up — layered build-up with a line-by-line audit trail; base rates and retention by the off-balance method.
• Modeling — GLMRelativities, a frequency–severity model with prediction intervals, on-leveling, dislocation, scenarios, and evaluation utilities.

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

The OpenActuarial ecosystem

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

Install everything at once with pip install openactuarial.

Development

git clone https://github.com/OpenActuarial/ratingmodels
cd ratingmodels
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.