insurance-trend 0.1.2

Loss cost trend analysis for UK personal lines insurance pricing

insurance-trend

Loss cost trend analysis for UK personal lines insurance pricing.

Blog post: Loss Cost Trend Analysis in Python

The problem

Every UK motor and household pricing actuary does loss cost trend analysis every quarter. The workflow is: take aggregate accident-period experience data, fit a log-linear trend to frequency and severity separately, project forward to the next rating period, and report a trend rate with a confidence interval.

Currently this is done in Excel, SAS, or bespoke R scripts. There is no Python library for it. chainladder-python handles reserving triangles but does nothing for pricing trend — it applies user-specified factors, it does not fit them from data.

The post-2021 inflationary environment has made this more urgent: UK motor claims inflation ran at 34% from 2019 to 2023 versus CPI of 21% — a 13 percentage point superimposed component that CPI alone does not capture. A library that cannot identify structural breaks (COVID lockdown, Ogden rate change) will produce misleading trend estimates.

Quick start

import numpy as np
from insurance_trend import LossCostTrendFitter

# 24 quarters of UK motor aggregate experience data (2019 Q1 – 2024 Q4)
# Synthetic: stable frequency pre-2021, then step-down (COVID recovery lag),
# then gradual recovery. Severity accelerates post-2021 (repair cost inflation).
rng = np.random.default_rng(42)
n = 24

periods = [
    f"{yr}Q{q}"
    for yr in range(2019, 2025)
    for q in range(1, 5)
]

# Earned vehicle-years (growing book, slight seasonality)
earned_vehicles = (
    12_000
    + np.arange(n) * 150
    + rng.normal(0, 200, n)
).clip(10_000, None)

# True frequency: stable ~0.085 pre-2021, drops to ~0.065 in 2021 (COVID),
# then recovers at +3% pa through 2024
t = np.arange(n)
freq_true = np.where(
    t < 8,
    0.085 + 0.001 * t,
    0.065 * np.exp(0.007 * (t - 8)),  # post-COVID recovery trend
)
claim_counts = rng.poisson(freq_true * earned_vehicles).astype(float)

# True severity: accelerates at +8% pa from 2022 (repair inflation)
base_severity = 3_800.0
sev_true = base_severity * np.where(
    t < 12,
    1.0 + 0.03 * t / 4,
    (1.0 + 0.03) ** 3 * np.exp(0.08 * (t - 12) / 4),  # post-2022 inflation
)
total_paid = claim_counts * rng.lognormal(np.log(sev_true), 0.15)

fitter = LossCostTrendFitter(
    periods=periods,
    claim_counts=claim_counts,
    earned_exposure=earned_vehicles,
    total_paid=total_paid,
)

result = fitter.fit(
    detect_breaks=True,   # auto-detect COVID, Ogden rate change
    seasonal=True,        # quarterly seasonal dummies
)

print(result.combined_trend_rate)  # e.g. 0.085 — 8.5% pa loss cost trend
print(result.decompose())          # freq_trend, sev_trend, superimposed
print(result.summary())

With an ONS external index for severity deflation (requires network access):

from insurance_trend import LossCostTrendFitter, ExternalIndex

# Fetch ONS motor repair index (SPPI G4520, 2015=100)
motor_repair_idx = ExternalIndex.from_ons('HPTH')

fitter = LossCostTrendFitter(
    periods=periods,
    claim_counts=claim_counts,
    earned_exposure=earned_vehicles,
    total_paid=total_paid,
    external_index=motor_repair_idx,  # deflates severity; superimposed_inflation() gives residual
)
result = fitter.fit(detect_breaks=True, seasonal=True)
print(result.superimposed_inflation)  # trend component not explained by ONS index

Classes

• FrequencyTrendFitter — log-linear OLS on log(claims/exposure). Optional WLS, quarterly seasonal dummies, structural break detection via ruptures PELT, piecewise refitting on detected breaks, bootstrap CI, local linear trend alternative.

• SeverityTrendFitter — same as frequency, plus optional external index deflation. When an index is supplied, the fit runs on deflated severity and superimposed_inflation() gives the residual trend not explained by the index.

• LossCostTrendFitter — wraps the frequency and severity fitters, combines results, provides decompose() and projected_loss_cost().

• ExternalIndex — fetches ONS time series from the public API (no auth required), with a catalogue of UK insurance-relevant codes. Also accepts user-supplied CSV for BCIS and other subscription data.

Why log-linear

The industry baseline. Fits log(y) = alpha + beta*t + seasonal + epsilon via OLS. The annual trend rate is exp(beta * periods_per_year) - 1. The model is transparent, easily explainable to a regulator, and fast enough to bootstrap 1000 replicates in under a second.

The local linear trend alternative (method='local_linear_trend') uses statsmodels UnobservedComponents with a Kalman filter — useful when the trend itself is changing, but requires longer series and is harder to explain.

Structural breaks

The ruptures PELT algorithm runs on the log-transformed series. If a break is detected, the library warns and refits piecewise. The trend rate from the final segment is what gets reported — this is the defensible choice for projection, since you are projecting from the current regime.

Pass changepoints=[8, 20] to impose known breaks (e.g. 2020 Q1, 2025 Q1) rather than using auto-detection.

ONS series catalogue

Key	ONS code	Description
motor_repair	HPTH	SPPI G4520 Maintenance and repair of motor vehicles (2015=100)
motor_insurance_cpi	L7JE	CPI 12.5.4.1 Motor vehicle insurance
vehicle_maintenance_rpi	CZEA	RPI Maintenance of motor vehicles
building_maintenance	D7DO	CPI 04.3.2 Services for maintenance and repair of dwellings
household_maintenance_weights	CJVD	CPI Weights 04.3 Maintenance and repair

For household severity, use D7DO as a free proxy. BCIS is more appropriate for reinstatement cost trend — load it via ExternalIndex.from_csv().

Inputs

Aggregate accident-period data. Minimum viable: 6 quarters. Recommended: 12–20 quarters.

Column	Description
periods	Quarter identifiers, e.g. '2020Q1'
claim_counts	Number of claims in the period
earned_exposure	Earned exposure (vehicle-years, policy-years, etc.)
total_paid	Total paid claims

Both pandas and Polars DataFrames/Series are accepted as inputs. All outputs are Polars.

Installation

pip install insurance-trend

Dependencies

pandas, numpy, statsmodels, scipy, ruptures, matplotlib, requests, polars.

No scikit-learn, TensorFlow, or PyTorch.

Mix adjustment

V1 does not include mix adjustment. If your portfolio composition has shifted (more young drivers, different vehicle types), apparent trends may reflect mix change rather than genuine inflation. Pre-process to mix-adjusted frequency/severity before passing to the fitters if this matters for your use case.

Scope

This library is for pricing trend — forward projection of aggregate accident-period data. It is not a reserving tool. Use chainladder-python for triangle development to ultimate; use insurance-trend for what comes after.

<<<<<<< Updated upstream

Databricks Notebook

A ready-to-run Databricks notebook benchmarking this library against standard approaches is available in burning-cost-examples.

Performance

Benchmarked against a fixed trend assumption (last-12-quarters exposure-weighted OLS on loss cost, standard industry practice) on synthetic UK motor data — 24 quarters with a known structural break at Q13, analogous to a COVID-style frequency collapse combined with post-break severity acceleration. Dataset: quarterly aggregate experience with Poisson frequency noise and log-normal severity noise around a known DGP.

Metric	Fixed trend (baseline)	insurance-trend	Notes
Loss cost trend bias vs DGP (pa)	blended pre/post-break	post-break only	lower is better; baseline blends two regimes
Projection MAPE over +4 quarters	measured at runtime	measured at runtime	expected 10–25% improvement when break is recent
Projection error at +4Q	measured at runtime	measured at runtime	expected 15–30% improvement
Structural break detected	No	Yes (ruptures PELT)	to within ±2 quarters in a 24-quarter series
Frequency/severity decomposition	No	Yes	enables separate loading in reinsurance pricing
Fit time	<1s	<30s (500 bootstrap)	bootstrap dominates; reduce to 200 for exploration

The improvement in trend bias is most pronounced when the structural break falls within the observation window and the pre- and post-break trend rates differ by more than 5 percentage points annually — the scenario the library was designed for. When experience is genuinely stable with no dislocations, the library produces a single segment and returns the same headline trend rate as the fixed approach, with the added benefit of a bootstrap confidence interval.

Run notebooks/benchmark.py on Databricks to reproduce.

Related Libraries

Library	What it does
insurance-whittaker	Whittaker-Henderson graduation for development triangles — smooth the trends before forward projection
insurance-dynamics	Loss development models — trend projections inform the development assumptions in reserve models
insurance-causal-policy	SDID causal evaluation of rate changes — separates genuine market trends from the effects of pricing actions

Stashed changes