insurance-poisson-mixture-nn 0.1.0

Neural Poisson mixture model separating structural and stochastic zero-claimers for UK insurance pricing

insurance-poisson-mixture-nn

Neural Poisson mixture model that separates structural zero-claimers from stochastic zero-claimers in UK personal lines insurance.

The problem

Your telematics motor book has a lot of zero-claim policies. Some of those zeros are structural: the driver installed the black box for the discount but barely drives. These policyholders will never claim regardless of how long you cover them. Others are stochastic: active drivers who happened not to have an accident this year. A longer policy period or worse luck and they would have claimed.

Standard frequency models — Poisson GLM, Poisson GBM — treat all zeros the same. Zero-inflated Poisson (ZIP) separates them using a single inflation parameter, but ZIP cannot identify which zero-claim policyholders are structural vs stochastic at the individual level.

This matters for pricing. Charging a structural zero the same frequency load as a stochastic zero means you are systematically overcharging low-risk policyholders. Under FCA Consumer Duty, that is a problem.

The solution

A two-component Poisson mixture estimated end-to-end with gradient descent:

P(Y=k | x) = (1 - pi(x)) * Poisson(k; lambda_0(x) * t)
           +     pi(x)   * Poisson(k; lambda_1(x) * t)

• pi(x): probability the policyholder is in the risky (at-risk) group — estimated by a neural network
• lambda_0(x): claim rate for the safe/structural-zero group — kept near zero by the data
• lambda_1(x): claim rate for the risky group — always constrained above lambda_0
• t: exposure in policy years

The ordering constraint lambda_1 > lambda_0 is enforced via reparameterisation:

lambda_0 = softplus(a)
lambda_1 = lambda_0 + softplus(b)

This eliminates label-switching without any hard clipping.

The output pi(x) is a continuous structural zero score. A telematics driver with pi = 0.05 and zero claims is almost certainly a structural zero. A driver with pi = 0.8 and zero claims is a stochastic zero who was lucky this year.

Based on: Poisson Mixture Deep Learning Neural Network Models for the Prediction of Drivers' Claims with Excessive Zero Claims Using Telematics Data, North American Actuarial Journal (NAAJ), 2025.

Installation

pip install insurance-poisson-mixture-nn

With optional comparison baselines (requires statsmodels):

pip install insurance-poisson-mixture-nn[comparison]

Quick start

from insurance_poisson_mixture_nn import PoissonMixtureNN, PoissonMixtureTrainer, PoissonMixturePredictor
from insurance_poisson_mixture_nn.synthetic import SyntheticMixtureData

# Generate synthetic telematics data with known mixture structure
data = SyntheticMixtureData(n_policies=10_000, seed=42)
X_train, y_train, exp_train = data.training_split()
X_val, y_val, exp_val = data.validation_split()
X_test, y_test, exp_test = data.test_split()

# Build the model
model = PoissonMixtureNN(
    n_features=X_train.shape[1],
    hidden_sizes=[64, 64, 32, 32, 16],  # paper architecture
    dropout=0.1,
    batch_norm=True,
    activation='elu',
)

# Train
trainer = PoissonMixtureTrainer(
    model,
    lr=1e-3,
    batch_size=512,
    max_epochs=200,
    patience=15,
)
history = trainer.fit(X_train, y_train, exp_train, X_val, y_val, exp_val)
print(f"Best val NLL: {history.best_val_nll:.4f} at epoch {history.best_epoch}")

# Predict
predictor = PoissonMixturePredictor(model)

# Expected claim frequency per policy (the pricing output)
expected_freq = predictor.predict_expected(X_test, exp_test)

# At-risk probability (pi score — high = risky group)
pi_scores = predictor.predict_pi(X_test)

# Structural zero score (1 - pi — high = likely never-claimer)
sz_scores = predictor.predict_structural_zero_score(X_test)

# Hard classification: structural vs stochastic
labels = predictor.classify_zero(X_test, threshold=0.5)

Diagnostics

from insurance_poisson_mixture_nn.diagnostics import MixtureDiagnostics

diag = MixtureDiagnostics(predictor)

# Component separation: distributions of lambda_0, lambda_1, pi
fig = diag.component_separation(X_test, exp_test)

# Pi calibration by decile
fig = diag.pi_calibration(X_test, y_test, exp_test)

# For zero-claim policies: structural vs stochastic attribution
fig = diag.zero_decomposition(X_test, y_test, exp_test)

# Training curves
fig = diag.training_curves(history)

Model comparison

from insurance_poisson_mixture_nn.comparison import ModelComparison

comp = ModelComparison(model, verbose=True)
results = comp.compare(X_train, y_train, exp_train, X_test, y_test, exp_test)
df = comp.results_dataframe()
print(df)
# Compares: Poisson GLM, ZIP GLM, Poisson DNN, PM-DNN

Architecture

The shared trunk approach is a deliberate design choice. Three separate sub-networks for pi, lambda_0, and lambda_1 would have three times the parameters and would not share the feature representations learned from the shared training signal. The shared trunk learns a single latent representation; the three heads then specialise it.

Architecture by default:

• 5 hidden layers: [64, 64, 32, 32, 16]
• ELU activation (paper default — avoids dead neurons better than ReLU)
• BatchNorm + Dropout (0.1)
• Adam with ReduceLROnPlateau
• Gradient clipping (max_norm=1.0) for stability in early epochs

When to use this

Use it when:

• You have telematics data and believe some policies are near-zero-exposure structural zeros
• Your zero-claim fraction is high and you suspect a genuine mixture (not just overdispersion)
• You want a per-policy structural zero score for pricing or NCD ladder adjustment

Do not use it when:

• You have no telematics or occupancy data — the model cannot identify structural zeros without informative covariates
• Your excess zeros are driven by overdispersion rather than a genuine two-group structure (use Negative Binomial instead)
• You want an interpretable GLM-style model — this is a black-box neural network

Requirements

• Python >= 3.10
• PyTorch >= 2.0
• Polars >= 0.20
• NumPy >= 1.24
• scikit-learn >= 1.3

Licence

Apache 2.0