updatesupport-finance 0.1.1

Financial model-risk extensions for updatesupport

updatesupport-finance

Financial model-risk extensions for updatesupport.

updatesupport-finance audits whether a public risk segmentation is stable enough to support a reported portfolio metric.

The core question is:

If a model report only shows risk by coarse public buckets such as product x region x FICO band x LTV band, could the reported expected-loss estimate materially change if the hidden mix inside those buckets shifted?

This is a segmentation adequacy check for reported risk metrics. It is designed for model-review and portfolio-monitoring artifacts, not as a replacement for model validation, calibration, backtesting, or statistical uncertainty analysis.

Install directly:

pip install updatesupport-finance
uv add updatesupport-finance

Or through the core package extra:

pip install "updatesupport[finance]"
uv add "updatesupport[finance]"

The package provides finance-oriented row metrics, Q preset aliases, portfolio compilation, and a model-risk report profile while keeping financial vocabulary out of the core updatesupport package.

Conic concentration presets require the core CVXPY extra when solved:

pip install "updatesupport[cvxpy]" updatesupport-finance
uv add "updatesupport[cvxpy]" updatesupport-finance

Why This Is Useful

Financial analysts already monitor model performance, population drift, calibration, overrides, and scenario sensitivity. Those checks usually ask whether the model or portfolio changed.

updatesupport-finance asks a different question:

Is the reporting segmentation itself adequate for the metric being reported?

For example, a validation pack may report expected loss by:

• product
• region
• fico_band
• ltv_band

But inside those public buckets, hidden composition may vary by:

• broker channel
• employment type
• vintage
• hardship history
• documentation type
• local housing market
• borrower cashflow pattern

If those hidden subgroups have different expected-loss rates, the public segmentation may not fully support the reported aggregate. The report quantifies that hidden-composition ambiguity and identifies which hidden variables would most improve the public segmentation.

What The Report Separates

The package is intentionally narrow. It separates:

• reported risk estimate: the supplied metric, such as expected loss or default rate
• statistical uncertainty: confidence intervals or model uncertainty supplied by other workflows, plus optional hidden-cell metric standard errors
• hidden-composition ambiguity: how far the reported metric can move when hidden mix shifts inside fixed public buckets
• concentration-stress ambiguity: the same ambiguity translated into factor-exposure or regional-concentration stress language when those Q presets are used
• refinement recommendations: hidden fields that would make the public representation more stable
• dual diagnostics and data diagnostics: solver-sensitivity signals and pre-solve data warnings that reviewers can attach to validation evidence
• limitations and reviewer notes: explicit boundaries around what the report does and does not validate

This is not a confidence interval and not a full model-risk-management system. It is a reviewable control for one practical question: whether the reporting representation is stable enough for the risk metric.

Analyst Workflow

1. Choose public buckets from the model report.
2. Choose hidden refinements that are available internally but not shown in the public segmentation.
3. Choose the target risk metric.
4. Choose a plausible hidden-mix shift preset.
5. Set a review threshold for hidden-composition ambiguity.
6. Attach the generated Markdown report to a model-review or monitoring pack.

The review status is deliberately simple:

• pass: ambiguity and public adequacy checks are within the chosen thresholds
• attention required: the public segmentation may need refinement or explicit acceptance of the ambiguity band

Example

import updatesupport_finance as usf

report = usf.model_risk_report(
    portfolio,
    public=["product", "region", "fico_band", "ltv_band"],
    hidden=[
        "product",
        "region",
        "fico_band",
        "ltv_band",
        "broker_channel",
        "employment_type",
        "vintage",
    ],
    metric=usf.expected_loss(pd="pd", lgd="lgd"),
    exposure="ead",
    metric_standard_error=usf.expected_loss_standard_error(
        pd="pd",
        lgd="lgd",
        pd_standard_error="pd_se",
        lgd_standard_error="lgd_se",
    ),
    q=usf.q_portfolio_mix_shift(radius=0.25),
    model_id="EL_RETAIL_2026Q2",
    portfolio_name="Retail credit portfolio",
    as_of_date="2026-06-30",
    intended_use="Expected-loss segmentation model review",
    ambiguity_limit=0.0025,
    public_adequacy_required=False,
    statistical_interval=(0.018, 0.024),
    statistical_confidence_level=0.95,
    statistical_method="validation bootstrap",
    composition_uncertainty_draws=500,
    composition_uncertainty_seed=123,
    composition_uncertainty_confidence_level=0.90,
    reviewer_notes=[
        "Review portfolio-mix stress with the portfolio monitoring owner.",
    ],
)

print(report.to_markdown())

This keeps four uncertainty notions separate:

• observed estimate: the exposure-weighted portfolio metric from the retained data
• supplied statistical/model uncertainty: an external interval or standard error from validation, bootstrap, survey, or model-estimation workflows
• hidden-composition ambiguity: the fixed-public-law transport interval under the selected Q stress test
• hidden-cell estimation uncertainty: optional hidden-cell metric standard errors, such as delta-method PD/LGD uncertainty from expected_loss_standard_error(...)

composition_uncertainty_draws=... adds a model-assisted posterior/bootstrap summary over hidden composition. It uses the core hidden_composition_uncertainty(...) layer, preserving public bucket masses by default and resampling hidden composition inside each public fiber.

You can also run that layer directly:

uncertainty = usf.model_assisted_portfolio_uncertainty(
    portfolio,
    public=["product", "region", "fico_band", "ltv_band"],
    hidden=[
        "product",
        "region",
        "fico_band",
        "ltv_band",
        "broker_channel",
        "employment_type",
        "vintage",
    ],
    metric=usf.expected_loss(pd="pd", lgd="lgd"),
    exposure="ead",
    draws=500,
    seed=123,
    q=usf.q_portfolio_mix_shift(radius=0.25),
    ambiguity_limit=0.0025,
)

Structured exports are available for downstream model-risk systems:

json_payload = report.to_json()
tables = report.to_tables()
frames = report.to_dataframes()  # Requires pandas.

The finance wrapper exposes finance-named tables that are intended to feed validation packs, governance dashboards, model inventory systems, and evidence archives:

• finance_model_risk: one-row review summary with metadata, status, reported estimate, ambiguity, adequacy flag, and Q preset
• finance_review_reasons: threshold breaches or adequacy failures
• finance_concentration_stress: concentration-stress interpretation of the active Q preset
• finance_statistical_uncertainty: supplied statistical/model uncertainty, when provided
• finance_estimator_uncertainty: hidden-cell standard-error adjustment, when provided
• finance_model_assisted_summary, finance_model_assisted_metric_summaries, finance_model_assisted_draws, and finance_model_assisted_joint_cells: posterior/bootstrap hidden-composition uncertainty outputs, when requested
• finance_refinement_recommendations: candidate public refinements ranked by ambiguity reduction
• finance_dual_diagnostics: largest CVXPY dual multipliers, when available
• finance_data_diagnostics: pre-solve data diagnostics
• finance_limitations and finance_reviewer_notes: review boundaries and analyst notes

Core updatesupport tables are also included with a core_ prefix, such as core_summary, core_worst_fibers, and core_refinements, so finance users can keep both the domain summary and the underlying audit evidence.

The report answers:

• What is the reported portfolio risk estimate?
• What statistical or model uncertainty was supplied separately?
• What range is still possible under hidden mix shifts?
• How should the ambiguity be interpreted under concentration-stress presets?
• Does the ambiguity exceed the review threshold?
• Which public buckets drive the instability?
• Which hidden fields are most valuable as public refinements?
• Which solver duals and data diagnostics should reviewers inspect?
• Which small public segmentation sits on the stability frontier, and why did it beat nearby alternatives?

A synthetic portfolio example is available in examples/model_risk_portfolio.py in the source repository:

uv run --package updatesupport-finance python \
  packages/updatesupport-finance/examples/model_risk_portfolio.py

The example prints both the finance model-risk report and a core public_representation_frontier(...) report for the same expected-loss metric. The frontier section compares baseline versus selected ambiguity, close dominated alternatives, and any screened-out refinement fields.

Colab Demo Notebooks

Interactive Colab demos are available under examples/notebooks:

• Portfolio model-risk walkthrough: expected-loss segmentation audit, hidden-cell risk plots, refinement recommendations, and public-representation frontier search.
• Model-assisted portfolio uncertainty: PD/LGD estimator uncertainty, posterior/bootstrap hidden-composition draws, and decision-threshold invariance.

Both notebooks use seaborn plus ipywidgets controls so analysts can adjust Q radii, ambiguity limits, draw counts, and decision thresholds in the browser.

Finance Sensitivity Profiles

finance_sensitivity_grid(...) builds an opinionated Q grid for portfolio model-risk review:

q_presets = usf.finance_sensitivity_grid(
    portfolio,
    hidden=[
        "product",
        "region",
        "fico_band",
        "ltv_band",
        "broker_channel",
        "employment_type",
        "vintage",
    ],
    exposure="ead",
    factors={
        "macro_beta": "macro_beta",
        "rate_sensitivity": "rate_sensitivity",
    },
)

The default credit_expected_loss profile includes:

• saturated hidden-composition stress
• bounded portfolio-mix shift
• exposure-weighted total-variation shift
• factor-exposure shift, when factors=... is supplied
• regional concentration shift
• observed no-shift baseline

Portfolio Concentration Stress Presets

Use concentration presets when independent hidden-bucket movement is too coarse for a model-risk review. These helpers constrain portfolio-level exposure drift while preserving the observed public segmentation.

Factor exposure drift:

q = usf.q_factor_exposure_shift(
    0.20,
    portfolio,
    hidden=[
        "product",
        "region",
        "fico_band",
        "ltv_band",
        "broker_channel",
        "employment_type",
    ],
    factors={
        "macro_beta": "macro_beta",
        "rate_sensitivity": "rate_sensitivity",
        "house_price_beta": "house_price_beta",
    },
    exposure="ead",
)

Regional concentration drift:

q = usf.q_regional_concentration_shift(
    0.10,
    portfolio,
    hidden=[
        "product",
        "region",
        "fico_band",
        "ltv_band",
        "broker_channel",
        "employment_type",
    ],
    region="region",
    exposure="ead",
)

Both helpers compile exposure-weighted hidden-cell moments and route through the core q_covariate_balance(...) preset:

|| standardized_factor_or_concentration_shift ||_2 <= radius

In model-review language, this asks:

If the public risk buckets stay fixed, but hidden portfolio factor exposure or regional concentration can drift within this L2 tolerance, how much can the reported risk metric move?

This maps naturally to expected loss, default rate, LGD, delinquency, approval benefit, and capital review where shifts are governed by portfolio exposure profiles rather than arbitrary independent hidden-cell movement.

Portfolio Segmentation Certificate

Use certify_portfolio_segmentation(...) when the output should be a pass/fail/inconclusive artifact for a model-review pack:

certificate = usf.certify_portfolio_segmentation(
    portfolio,
    public=["product", "region", "fico_band", "ltv_band"],
    hidden=[
        "product",
        "region",
        "fico_band",
        "ltv_band",
        "broker_channel",
        "employment_type",
        "vintage",
    ],
    metric=usf.expected_loss(pd="pd", lgd="lgd"),
    exposure="ead",
    candidate_refinements=["broker_channel", "employment_type", "vintage"],
    factors={"macro_beta": "macro_beta"},
    ambiguity_limit=0.0025,
    bucket_budget=80,
    search="exhaustive",
    model_id="EL_RETAIL_2026Q2",
    portfolio_name="Retail credit portfolio",
    intended_use="Expected-loss segmentation review",
)

print(certificate.to_markdown())

The returned FinanceStabilityCertificate keeps the underlying core RepresentationStabilityCertificate at certificate.core, while adding finance metadata and model-risk interpretation language.

To write the Markdown report:

uv run --package updatesupport-finance python \
  packages/updatesupport-finance/examples/model_risk_portfolio.py \
  --output data/finance_model_risk_report.md