insurance-fairness

FCA Consumer Duty and Equality Act 2010 compliance auditing for UK insurance pricing models — proxy discrimination detection, exposure-weighted bias metrics, and FCA-ready Markdown audit reports.

Your postcode rating factor probably correlates with ethnicity. The FCA's Consumer Duty (PS22/9) requires you to demonstrate this is not producing indirect discrimination under Section 19 of the Equality Act 2010 — and the FCA's multi-firm review of Consumer Duty implementation (2024) found most insurers' Fair Value Assessments could not do this. insurance-fairness produces the documented, exposure-weighted audit trail your pricing committee can sign off.

Blog post: Your Pricing Model Might Be Discriminating

Every UK pricing team faces the same problem: postcode encodes protected-characteristic information without anyone ever modelling a protected attribute directly. Citizens Advice (2022) estimated a £280/year ethnicity penalty in UK motor insurance, totalling £213m per year, driven entirely through proxy factors. Proving — or disproving — this is happening in your book is what proxy detection is for.

The compliance risk is live. The FCA's multi-firm review of Consumer Duty implementation (2024) found most Fair Value Assessments were "high-level summaries with little substance". Six Consumer Duty investigations are now open, two of which directly involve insurers on fair value grounds.

This is a compliance audit tool, not a methodology library. It produces documented, evidenced, FCA-mapped analysis that a pricing committee can sign off and that will stand up to an FCA file review. Fairlearn and AIF360 help you satisfy a chosen fairness criterion — they do not answer the question: "Can I demonstrate to the FCA that this model does not constitute indirect discrimination under Section 19 of the Equality Act?"

Part of the Burning Cost toolkit

Takes a fitted pricing model and a dataset with protected characteristics. Feeds audit reports and proxy detection results into insurance-governance for pricing committee sign-off packs and FCA Consumer Duty documentation. → See the full stack

insurance-fairness vs doing it manually

Task	Manual approach	insurance-fairness
Proxy detection	Spearman correlation — misses non-linear relationships; postcode-ethnicity returns \|r\| ≈ 0.10, finds nothing	CatBoost proxy R² + mutual information; returns R² ≈ 0.62, unambiguously RED — 100% detection rate across 50 seeds
Exposure-weighted A/E by group	Custom code per model, often not exposure-weighted	`calibration_by_group()` — exposure-weighted, RAG status, 10-decile grid, Equality Act framing
Log-space demographic parity	Multiply predictions, take ratios — silently wrong for multiplicative models	`demographic_parity_ratio()` — log-ratio for GLM/GBM multiplicative worlds
Financial impact of discrimination	Not quantified	`ProxyVulnerabilityScore` — per-policyholder £ cost of proxy exploitation
Action vs outcome fairness (Consumer Duty Outcome 4)	Not possible with standard metrics	`DoubleFairnessAudit` — full Pareto front, Tchebycheff scalarisation, FCA evidence pack
Audit report for pricing committee	Manual Word document, no regulatory mapping	`report.to_markdown()` — FCA-mapped Markdown with PRIN 2A, FG22/5 cross-references and sign-off table

Quick start

import polars as pl
from insurance_fairness import FairnessAudit

# df: policy-level DataFrame with claims, exposure, rating factors,
# and a protected characteristic column (or ONS-derived area proxy)
audit = FairnessAudit(
    model=catboost_model,
    data=df,
    protected_cols=["gender"],
    prediction_col="predicted_rate",
    outcome_col="claim_amount",
    exposure_col="exposure",
    factor_cols=["postcode_district", "vehicle_age", "ncd_years", "vehicle_group"],
    model_name="Motor Model Q4 2024",
    run_proxy_detection=True,
)
report = audit.run()
report.summary()
# Overall RAG: RED
# Proxy detection: postcode_district — proxy R²=0.62 [RED], MI=0.41 [RED]
# Calibration by group (gender): max A/E disparity 0.081 [AMBER]
# Demographic parity log-ratio: +0.082 (ratio 1.085) [AMBER]
report.to_markdown("audit_q4_2024.md")  # FCA-ready Markdown with sign-off table

See examples/quickstart.py for a fully self-contained example including synthetic data generation and model fitting.

Installation

pip install insurance-fairness
# or
uv add insurance-fairness

For the Pareto optimisation subpackage (requires pymoo):

pip install "insurance-fairness[pareto]"

Dependencies: polars, catboost, scikit-learn, scipy, numpy, jinja2, pyarrow

Questions or feedback? Start a Discussion. Found it useful? A star helps others find it.

Features

Proxy detection — identifies which rating factors (postcode, vehicle group, occupation) are acting as protected-characteristic proxies using mutual information, CatBoost proxy R², partial correlation, and SHAP-linked price impact
Exposure-weighted bias metrics — calibration by group (A/E ratio), demographic parity ratio (log-space), disparate impact ratio, equalised odds, Gini by group, Theil index; all weight by earned car-years, not policy count
Counterfactual fairness — flip protected characteristics and measure premium impact; supports direct flip and LRTW marginalisation
Proxy vulnerability scoring (ProxyVulnerabilityScore) — per-policyholder £ difference between unaware premium and discrimination-free benchmark; TVaR overcharge, parity cost, fairness range
Indirect discrimination audit (IndirectDiscriminationAudit) — five benchmark premiums from Côté et al. (2025): aware, unaware, unawareness, proxy-free, parity-cost; no causal graph required
Multicalibration (MulticalibrationAudit, IterativeMulticalibrationCorrector) — audit and iteratively correct pricing models for multicalibration fairness (Denuit, Michaelides & Trufin, 2026)
Marginal fairness correction (MarginalFairnessPremium) — closed-form Stage 2 adjustment of Expected Shortfall, Wang transform, or any distortion risk measure to be marginally fair; no iterative solver (Huang & Pesenti, 2025)
Double fairness (DoubleFairnessAudit) — joint action (Delta_1) and outcome (Delta_2) Pareto optimisation via lexicographic Tchebycheff scalarisation; addresses Consumer Duty Outcome 4 directly (Bian et al., 2026)
Discrimination-insensitive reweighting (DiscriminationInsensitiveReweighter) — training-data weights that achieve X ⊥ A without removing the protected attribute; KL divergence minimisation (Miao & Pesenti, 2026)
Privatised audit (PrivatizedFairnessAudit) — fairness auditing when protected attributes are privatised via local differential privacy or estimated from proxies (Zhang, Liu & Shi, 2025)
Optimal transport (optimal_transport subpackage) — discrimination-free pricing via Lindholm marginalisation, causal path decomposition, Wasserstein barycenter correction
FCA-mapped audit reports — Markdown with PRIN 2A, FG22/5, Equality Act s.19 cross-references and a sign-off table; suitable for pricing committee packs and FCA file reviews

Expected performance

On a 20,000-policy synthetic UK motor portfolio with a known postcode-ethnicity proxy structure, replicating the Citizens Advice (2022) finding:

Metric	Manual Spearman (\|r\| > 0.25)	Library (proxy R² + MI)
postcode_area flagged as proxy	No (\|r\| ≈ 0.10)	Yes (proxy R² ≈ 0.62, RED)
Factors correctly flagged	0/6	1–2/6
Detection rate across 50 seeds	0%	100%
Non-linear proxy detection	No	Yes (CatBoost)
Financial impact quantified	No	Yes

High-diversity policyholders pay roughly £70–90 more per year than low-diversity policyholders, driven through the postcode area loading. The manual Spearman check returns |r| ≈ 0.10 and finds nothing. The library returns proxy R² ≈ 0.62 — unambiguously RED — because postcode encodes diversity non-linearly across London vs outer vs rural areas.

Task	Time (n=50,000 policies)	Notes
Calibration by group (10 deciles)	< 2s	Primary Equality Act metric
Demographic parity ratio	< 1s	Log-space, multiplicative model
Proxy R-squared (per factor, CatBoost)	15–60s	Per factor; subsample for large books
Mutual information scores	< 5s	Catches non-linear relationships
Full `FairnessAudit.run()` with proxy detection	2–10 min	Produces FCA-ready Markdown report

Run on Databricks

Modules

`FairnessAudit` and `FairnessReport`

The main entry point. FairnessAudit.run() returns a FairnessReport with:

report.summary() — plain-text console output
report.to_markdown(path) — Markdown report with regulatory mapping and sign-off section
report.to_dict() — JSON-serialisable dict for downstream processing
report.flagged_factors — list of factors with proxy concerns
report.overall_rag — 'green', 'amber', or 'red'
report.results["gender"] — per-characteristic ProtectedCharacteristicReport

`bias_metrics`

All metrics are exposure-weighted and work on Polars DataFrames.

from insurance_fairness import (
    calibration_by_group,
    demographic_parity_ratio,
    disparate_impact_ratio,
    equalised_odds,
    gini_by_group,
    theil_index,
)

# Calibration by group (sufficiency) — most defensible under Equality Act s.19
cal = calibration_by_group(
    df,
    protected_col="ethnicity_group",
    prediction_col="model_freq",
    outcome_col="n_claims",
    exposure_col="exposure",
    n_deciles=10,
)
print(f"Max A/E disparity: {cal.max_disparity:.4f} [{cal.rag}]")

# Demographic parity ratio (log-space, multiplicative model)
dp = demographic_parity_ratio(df, "gender", "predicted_premium", "exposure")
print(f"Log-ratio: {dp.log_ratio:+.4f} (ratio: {dp.ratio:.4f})")

# Theil index decomposition
theil = theil_index(df, "ethnicity_group", "predicted_premium", "exposure")
print(f"Between-group share: {theil.theil_between / theil.theil_total:.1%}")

`proxy_detection`

from insurance_fairness import detect_proxies

result = detect_proxies(
    df,
    protected_col="ethnicity_proxy",
    factor_cols=["postcode_district", "vehicle_age", "ncd_years", "driver_age_band"],
    run_proxy_r2=True,
    run_mutual_info=True,
    run_partial_corr=True,
)
print(result.flagged_factors)  # ['postcode_district']
print(result.to_polars())      # Polars DataFrame, sorted by proxy R-squared

`counterfactual`

from insurance_fairness import counterfactual_fairness

cf = counterfactual_fairness(
    model=model,
    df=df,
    protected_col="gender",
    feature_cols=["gender", "postcode_district", "vehicle_age", "ncd_years"],
    exposure_col="exposure",
    flip_values={"M": "F", "F": "M"},
    method="direct_flip",
)
cf.summary()
# "Counterfactual premium impact: +8.2%"

For models that do not use the protected characteristic directly, use method="lrtw_marginalisation".

`double_fairness`

v0.6.0. Action fairness (Delta_1) and outcome fairness (Delta_2) are not the same obligation and they can conflict. Minimising premium disparity does not minimise loss ratio disparity. On a 20,000-policy synthetic UK motor portfolio, the policy with the most equal premiums produced the most unequal loss ratios — a compliance gap the FCA's multi-firm review of Consumer Duty implementation (2024) identified.

DoubleFairnessAudit recovers the full Pareto front via lexicographic Tchebycheff scalarisation and selects the value-maximising Pareto point as the recommended operating policy:

from insurance_fairness import DoubleFairnessAudit

audit = DoubleFairnessAudit(n_alphas=20)
audit.fit(
    X_train,       # features excluding protected attribute
    y_premium,     # primary outcome: pure premium
    y_loss_ratio,  # fairness outcome: claims / premium
    S_gender,      # binary protected group indicator
)
result = audit.audit()
print(result.summary())
fig = audit.plot_pareto()
print(audit.report())   # FCA Consumer Duty Outcome 4 evidence section

Run the benchmark on Databricks

`marginal_fairness`

v0.5.0. Closed-form Stage 2 correction for distortion risk measure premiums — Expected Shortfall, Wang transform, or any custom distortion — to be marginally fair with respect to protected attributes. No iterative solver (Huang & Pesenti, 2025).

from insurance_fairness import MarginalFairnessPremium

mfp = MarginalFairnessPremium(distortion='es_alpha', alpha=0.75)
mfp.fit(Y_train, D_train, X_train, model=glm, protected_indices=[0])
rho_fair = mfp.transform(Y_test, D_test, X_test)

report = mfp.sensitivity_report()
print(f"Baseline ES0.75: {report.rho_baseline:.4f}")
print(f"Fair ES0.75:     {report.rho_fair:.4f}")

`indirect`

v0.6.4. End-to-end partition-based audit of indirect discrimination. Computes five benchmark premiums from Côté et al. (2025): aware, unaware, unawareness, proxy-free, and parity-cost. Proxy vulnerability = mean |h_U(x) - h_A(x)| quantifies how much the unaware model exploits proxies. No causal graph required.

from insurance_fairness import IndirectDiscriminationAudit

audit = IndirectDiscriminationAudit(
    protected_attr="gender",
    proxy_features=["postcode_district", "occupation"],
    exposure_col="exposure",
)
result = audit.fit(X_train, y_train, X_test, y_test)
print(f"Proxy vulnerability: {result.proxy_vulnerability:.2f}")
print(result.segment_report)

`multicalibration`

v0.3.7+. Audit and correct pricing models for multicalibration fairness — the model should be well-calibrated not just in aggregate, but within every protected-group × risk-decile cell (Denuit, Michaelides & Trufin, 2026). IterativeMulticalibrationCorrector (v0.6.7) applies iterative recalibration across cells.

from insurance_fairness import MulticalibrationAudit, IterativeMulticalibrationCorrector

audit = MulticalibrationAudit(n_bins=10, alpha=0.05)
report = audit.audit(y_true, y_pred, protected, exposure)
corrected = audit.correct(y_pred, protected, report, exposure)

`discrimination_insensitive`

v0.6.3. Training-data reweighting that achieves X ⊥ A without removing the protected attribute from the training data. Weights integrate with any sklearn sample_weight parameter (Miao & Pesenti, 2026).

from insurance_fairness import DiscriminationInsensitiveReweighter

rw = DiscriminationInsensitiveReweighter(protected_col="gender")
weights = rw.fit_transform(X_train)
model.fit(X_train.drop("gender", axis=1), y_train, sample_weight=weights)
diag = rw.diagnostics(X_train)

`optimal_transport` subpackage

Discrimination-free pricing via Lindholm marginalisation, causal path decomposition, and Wasserstein barycenter correction:

from insurance_fairness.optimal_transport import (
    CausalGraph,
    DiscriminationFreePrice,
    FCAReport,
)

`diagnostics` subpackage

Proxy discrimination diagnostics with D_proxy scalar, Shapley attribution, and per-policyholder vulnerability scores:

from insurance_fairness.diagnostics import ProxyDiscriminationAudit

Limitations

The proxy detection thresholds are not FCA-prescribed. Proxy R-squared thresholds (amber: >0.05, red: >0.10) are operationally derived, not regulatory requirements. A factor below the red threshold may still constitute indirect discrimination under Section 19 — the statistical test is a trigger for investigation, not a compliance safe harbour.
Proxy detection requires a protected characteristic column, which most insurers do not hold. Joining ONS 2021 Census LSOA ethnicity proportions to postcode data is an area-level proxy, not individual-level. Factor correlations with this proxy are correlations with area demographics, not individual ethnicity.
Calibration by group is not counterfactual fairness. Equal calibration (A/E = 1.0) within each protected group means the model is correctly pricing the proxy-contaminated risk profile — not that it is pricing independent of the protected characteristic.
The Markdown audit report is evidence, not a compliance determination. It does not constitute legal advice or a regulatory safe harbour. Whether a specific model constitutes indirect discrimination depends on the proportionality justification for the rating factor, which requires expert legal and actuarial judgement outside the scope of this library.

References

Regulatory instruments

FCA. (2023). Consumer Duty: Final rules and guidance (PS22/9). Financial Conduct Authority. www.fca.org.uk/publications/policy-statements/ps22-9-new-consumer-duty
FCA. (2023). Guidance for firms on the fair treatment of vulnerable customers (FG21/1). Financial Conduct Authority.
FCA. (2022). General insurance pricing practices (PS21/5). Financial Conduct Authority.
FCA. (2023). Algorithmic pricing (DP23/3). Financial Conduct Authority.
Equality Act 2010, s.19 (indirect discrimination). UK Parliament. www.legislation.gov.uk/ukpga/2010/15

Fairness metrics and methodology

Hardt, M., Price, E. & Srebro, N. (2016). "Equality of Opportunity in Supervised Learning." Advances in Neural Information Processing Systems 29 (NeurIPS 2016). arXiv:1610.02413
Chouldechova, A. (2017). "Fair prediction with disparate impact: A study of bias in recidivism prediction instruments." Big Data, 5(2), 153–163. doi:10.1089/big.2016.0047
Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C. & Venkatasubramanian, S. (2015). "Certifying and Removing Disparate Impact." KDD 2015. arXiv:1412.3756

Insurance-specific fairness literature

Lindholm, M., Richman, R., Tsanakas, A. & Wüthrich, M.V. (2022). "Discrimination-Free Insurance Pricing." ASTIN Bulletin, 52(1), 55–89. doi:10.1017/asb.2021.23
Lindholm, M., Richman, R., Tsanakas, A. & Wüthrich, M.V. (2026). "Sensitivity-Based Measures of Discrimination in Insurance Pricing." European Journal of Operational Research.
Denuit, M., Michaelides, M. & Trufin, J. (2026). "Multicalibration in Insurance Pricing." arXiv:2603.16317
Zhang, Y., Liu, Y. & Shi, P. (2025). "Discrimination-Free Insurance Pricing with Privatized Sensitive Attributes." arXiv:2504.11775
Côté, O., Côté, M.-P. & Charpentier, A. (2025). "A Scalable Toolbox for Exposing Indirect Discrimination in Insurance Rates." CAS Working Paper.
Miao, K.E. & Pesenti, S.M. (2026). "Discrimination-Insensitive Pricing." arXiv:2603.16720

Related libraries

Library	What it does
insurance-causal-policy	SDID and Doubly Robust Synthetic Controls — establishes whether a rate change caused an outcome shift, not just correlated with one
insurance-governance	Pricing committee sign-off packs and FCA Consumer Duty documentation
insurance-monitoring	Post-bind drift detection — flags when protected-group A/E ratios are shifting in the live book
insurance-optimise	Constrained rate change optimisation with FCA PS21/5 compliance

Part of the Burning Cost open-source insurance analytics toolkit. → See all libraries

Questions or feedback? Start a Discussion. Found it useful? A GitHub star helps others find it.

Need help implementing this in production? Talk to us.

Related Libraries

Library	Description
`insurance-governance`	Model governance and validation reports — document your fairness audit in a PRA-compliant validation framework
`insurance-causal`	DML causal inference — isolate genuine causal price effects from proxy correlation before auditing for discrimination
`insurance-monitoring`	Monitor fairness metrics post-deployment — PSI and A/E ratios on protected characteristic proxies

Name		Name	Last commit message	Last commit date
Latest commit History 158 Commits
.github/workflows		.github/workflows
benchmarks		benchmarks
examples		examples
notebooks		notebooks
src/insurance_fairness		src/insurance_fairness
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
run_tests_databricks.py		run_tests_databricks.py
uv.lock		uv.lock

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insurance-fairness

Part of the Burning Cost toolkit

insurance-fairness vs doing it manually

Quick start

Installation

Features

Expected performance

Modules

`FairnessAudit` and `FairnessReport`

`bias_metrics`

`proxy_detection`

`counterfactual`

`double_fairness`

`marginal_fairness`

`indirect`

`multicalibration`

`discrimination_insensitive`

`optimal_transport` subpackage

`diagnostics` subpackage

Limitations

References

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insurance-fairness

Part of the Burning Cost toolkit

insurance-fairness vs doing it manually

Quick start

Installation

Features

Expected performance

Modules

FairnessAudit and FairnessReport

bias_metrics

proxy_detection

counterfactual

double_fairness

marginal_fairness

indirect

multicalibration

discrimination_insensitive

optimal_transport subpackage

diagnostics subpackage

Limitations

References

Related libraries

Related Libraries

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`FairnessAudit` and `FairnessReport`

`bias_metrics`

`proxy_detection`

`counterfactual`

`double_fairness`

`marginal_fairness`

`indirect`

`multicalibration`

`discrimination_insensitive`

`optimal_transport` subpackage

`diagnostics` subpackage

Packages