projectionmodels 0.1.0

Forward projection (budgeting) for a book of business: premium roll-forward and credibility-blended, trended, seasonalized claims. Part of the OpenActuarial ecosystem.

projectionmodels

Forward projection (budgeting) for a book of business — part of the OpenActuarial ecosystem.

ratingmodels builds a price from experience. projectionmodels does the opposite direction: it takes the book as it is — stored premiums, historical claims, and membership assumptions — and rolls it forward for planning and budgeting. It sits beside ratingmodels and lossmodels on top of the actuarialpy primitives layer, and depends only downward on actuarialpy — never sideways on another workflow package.

Install

pip install projectionmodels

Layers

object	role
PMPMProjection	credibility-blended, trended, plan-adjusted claims PMPM with a pooling load
PremiumRollforward	stored premium rolled forward by rate action and plan change
GroupProjection	one group: premium + claims + renewal weighting — the unit you loop over the book
BookProjection	aggregate in-force renewals + new business into the book budget

Each class computes on construction and exposes a frozen *Result dataclass via .result; a lowercase functional form (project_group, project_book, …) returns the result directly.

What it computes

Premium is a stored value rolled forward (not rebuilt from experience), level per member-month:

prem_pmpm* = (current_premium / current_member_months) · (1 + rate_action) · (1 + plan_change)

Claims are the actuarial piece — a credibility blend of the group's own PMPM and the book PMPM, trended, plan-adjusted, and seasonalised onto the given membership:

Z         = limited-fluctuation credibility from the group's claim count
blended   = Z · group_pmpm + (1 − Z) · book_pmpm
projected = blended · trend · plan_factor + pooling_pmpm · trend
claims_m  = projected · membership_m · seasonal_m

Renewal probability is supplied per group (e.g. from underwriting) via renewal_prob — it is an input, not something this package models. It weights premium and claims equally (a lapsed group books neither), so the projected loss ratio is unaffected. New business is the same GroupProjection with group_pmpm = book_pmpm, credibility = 0, and renewal_prob = close_ratio.

Quick start

import numpy as np, pandas as pd
from projectionmodels import GroupProjection, BookProjection

g = GroupProjection(
    prospective_membership=np.full(12, 1900.0), seasonal_factors=season_factors,
    current_premium=4_500_000, current_member_months=21_600,
    rate_action=0.06, plan_change=-0.02,
    book_pmpm=180.0, claim_trend=0.06,
    exp_midpoint=pd.Timestamp("2025-07-01"), prosp_midpoint=pd.Timestamp("2027-07-01"),
    group_claims=3_800_000, group_member_months=21_600, group_claim_count=6_000,
    pooling_pmpm=8.0, renewal_prob=0.90)   # renewal likelihood from underwriting

book = BookProjection([g, ...], labels=["GroupA", ...])
book.loss_ratio          # expected book loss ratio
book.by_group            # per-group expected premium / claims / LR
book.monthly             # book premium & claims by month

Built on actuarialpy

This package adds no primitives of its own — it composes existing actuarialpy primitives and depends only downward:

• credibility_weighted_estimate — the credibility blend, Z·group + (1−Z)·book
• midpoint_trend_factor — trend to the prospective midpoint
• seasonality_factors / apply_seasonality — monthly seasonality
• pure_premium — PMPM

pool_losses / excess_over_threshold in actuarialpy cap large claims when you derive the pooling PMPM upstream.