lossmodels 0.1.1

A Python library for actuarial loss modeling using frequency-severity methods.

lossmodels

A Python library for actuarial loss modeling using frequency–severity methods.

---

Overview

lossmodels provides a clean, modular implementation of core actuarial techniques from Loss Models: Data to Decisions (Klugman, Panjer, Willmot), including:

• frequency–severity modeling
• aggregate loss modeling (simulation, Panjer recursion, FFT)
• parameter estimation (MLE, method of moments)
• credibility theory
• risk measurement (VaR, TVaR, stop-loss)

Designed for:

• actuaries and actuarial analysts
• quantitative developers
• data scientists in insurance

---

Quick Example

from lossmodels.frequency import Poisson
from lossmodels.severity import Lognormal
from lossmodels.aggregate import CollectiveRiskModel

freq = Poisson(lam=2.0)
sev = Lognormal(mu=10.0, sigma=0.8)

model = CollectiveRiskModel(freq, sev)

print("Mean:", model.mean())
print("VaR 95%:", model.var(0.95))
print("TVaR 95%:", model.tvar(0.95))

---

Installation

pip install lossmodels

Or for development:

pip install -e .

---

Core Features

Frequency Models

• Poisson
• Negative Binomial
• Binomial
• Geometric
• Empirical frequency

Severity Models

• Exponential
• Gamma
• Lognormal
• Pareto
• Weibull
• Empirical severity

Aggregate Modeling

• Monte Carlo simulation
• Panjer recursion
• FFT (Fast Fourier Transform)

Estimation

• Maximum Likelihood Estimation (MLE)
• Method of Moments
• Generic numerical MLE

Model Selection

• Best severity selection (AIC / BIC)
• Best frequency selection (Poisson, Negative Binomial)

Credibility

• Bühlmann
• Bühlmann–Straub

Risk Measures

• VaR
• TVaR
• Stop-loss
• Limited Expected Value (LEV)
• PMF-based VaR / TVaR / stop-loss

---

Aggregate Methods

Simulation

samples = model.sample(100_000)

Panjer Recursion

from lossmodels.aggregate import discretize_severity, panjer_recursion

pmf = discretize_severity(sev, h=0.01, max_loss=20.0)
agg = panjer_recursion(freq, pmf, n_steps=5000)

FFT

from lossmodels.aggregate import fft_aggregate_poisson

agg = fft_aggregate_poisson(freq, pmf, n_steps=5000)

---

Risk Measures from PMF

from lossmodels.aggregate import var_from_pmf, tvar_from_pmf

var95 = var_from_pmf(agg, h=0.01, q=0.95)
tvar95 = tvar_from_pmf(agg, h=0.01, q=0.95)

---

Parameter Estimation

from lossmodels.estimation import fit_lognormal, fit_best_severity

model = fit_lognormal(data)
best = fit_best_severity(data)

---

Examples

See the examples/ directory:

• fit_and_compare_models.py
• panjer_vs_simulation.py
• panjer_vs_fft_vs_simulation.py
• credibility_example.py

---

Testing

pytest -v

Fast tests only:

pytest -v -m "not slow"

---

Project Status

Core Loss Models functionality is implemented.

Planned improvements:

• Extreme Value Theory (EVT)
• Bootstrap methods
• Performance optimization
• Additional distributions
• Documentation

---

License

MIT License