updatesupport-finance

Financial model-risk extensions for updatesupport.

updatesupport-finance audits what financial summaries and disclosures support. It bounds hidden allocations, tests claims against supplied evidence, and shows how conclusions depend on measurement definitions and assumptions. It also audits whether a public risk segmentation supports a reported portfolio metric.

The workflows below are available in updatesupport-finance>=0.1.6 with updatesupport>=0.1.7. Source retrieval, accounting interpretation and policy selection remain explicit inputs. No provider credentials or proprietary captures are needed to use these APIs.

Disclosure analysis for analysts

The disclosure workflow supports questions such as how much segment revenue must come from a disclosed customer group, whether cash measures treat advances consistently, and what funding is required under an explicitly chosen plan.

1. Declare MeasurementDefinition and DisclosureRequirement contracts; use compile_disclosure_evidence to quarantine missing, stale or incomplete inputs. Numerical normalization preserves original facts and source precision.
2. Compile amount and percentage disclosures with allocation_table, AllocationMargin and AllocationShareMargin. share_target tests a threshold with a positive, explicitly named denominator.
3. Use CashMeasure and cash_bridge_constraint for declared cash definitions; use time_window_allocation for dated flow partitions. Choose conversion schedules explicitly; stocks and lifetime commitments are not annual flows.
4. Label ConstraintPolicy groups as reported evidence, accounting relationships, management expectations or analyst policies. Named DisclosureStressCase replacements re-solve the complete model. break_even_analysis bounds a decision variable under supplied conditions and reports infeasible cases.
5. Save capture_disclosure_snapshot(..., context=...) with normalization maps and a DisclosureScope. Create an AnalystDecisionBrief from audit packs, arithmetic baselines, decision impacts and concrete missing evidence. Comparison guards flag different windows and anonymous customer cohorts.

See the synthetic analyst example and workflow documentation.

Customer advances and alternative funding explanations

AdvanceMovement and CustomerAdvanceBridge compile reviewed customer-advance roll-forwards, separating net cash, recognition, unpaid bills and signed noncash movements. Incomplete mappings retain an unclassified residual. An optional liability / indirect cash-flow gap remains a separate diagnostic.

minimum_advance_explanation calculates the least net noncash addition required under an explicit hypothetical cash ceiling. It preserves unbounded and infeasible results and labels the condition as an analyst policy. Accounting regime, recognition basis, completeness and measurement scope remain explicit. See the workflow guide and offline example.

Compare explanations and choose the next evidence

compare_disclosure_explanations crosses named economic explanations with explicit accounting mappings. Its matrix retains financial bounds, checked witnesses, conflicts and conclusions that depend on the mapping. plan_disclosure_evidence finds inclusion-minimal bundles of hypothetical measurement or scope-review outcomes that exclude selected explanations across the retained mappings. Contradictory outcomes and empty or uncovered mappings cannot certify separation. JSON exports preserve the basis and outcome witnesses. evaluate_disclosure_evidence shows what selected hypothetical answers leave standing, without an elimination goal, including optional financial requirements. This supports competing-accounts briefs with explicit answer implications. See the workflow guide and synthetic offline example. Compatibility does not identify an actual cause or assign probabilities to the supplied explanation catalog.

Multi-metric portfolio reports

compile_portfolio_metrics validates named measurements on one supplied universe. joint_portfolio_report exposes a shared complexity frontier with common-book and eligible-scope uncertainty, retaining known facts outside the intersection. Criteria are optional. Core ConditionalRefinement objects and suggest_conditional_refinements support portable local bucket drills.

plan_portfolio_repairs compares finite report/evidence combinations under fixed nonjoint weights, preserving source availability and cost uncertainty. JointPortfolioSnapshot replays offline; FrozenJointPortfolioContract monitors a chosen schema/Q/measurement contract without inventing historical calibration. See the workflow guide and offline example.

API organization

Import the supported API from updatesupport_finance. The finance API map groups the APIs by evidence, accounting models, explanation analysis, portfolio reporting and offline replay. Imports from updatesupport_finance.explanations remain supported.

Report types with tables share the core JSON/DataFrame exporter and define their own tables. PortfolioHeadlineReport.to_tables() includes headline truth and summary support alongside the full coverage ledger, refinements and transfers; PortfolioCoverageReport.to_tables() retains the supplied and eligible totals.

Internally, explanation comparison and evidence planning have separate engines. Snapshot formats share serialization and provenance helpers, and finite planning workflows share subset enumeration and frontier selection. Files beginning with _ are implementation details; snapshot schemas remain owned by each workflow.

Segmentation and model review

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.

The strongest use case is retail-credit or insurance model review where the institution has richer internal cells but reports risk at a coarser governance segmentation. In that setting, "hidden" means internally retained but not shown in the public or validation-pack segmentation.

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.

It also provides a disclosure-triangulation front end over the core updatesupport named-linear feasibility solver. That surface is generic: unknown variables, linear constraints, target expressions, and tiered assumption sets are user supplied. The finance package only adds disclosure vocabulary, provenance, declared measurement contracts and modeling helpers.

Conic concentration presets require the core CVXPY extra when solved:

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

Portfolio Evidence and Headline Audits

These APIs are available in updatesupport-finance>=0.1.6 with updatesupport>=0.1.7.

The provider-neutral workflow is:

1. Declare a PortfolioUniverse: original positions, currency, holdings cutoff, and eligible security types. Position values must be nonnegative; gross/net short-portfolio conventions and currency conversion need an explicit adapter.
2. Supply FundamentalObservation objects wrapping DisclosureFact, plus TaxonomyAssignment labels and a PortfolioMetricPolicy.
3. Call compile_portfolio_evidence. It selects the latest compatible fiscal period and latest version available by the cutoff, quarantines ambiguous versions, and records every position as covered, missing, unmapped, or excluded.
4. Call portfolio_headline_report with a declared decision and scope. Inspect observed truth, summary support, coverage, refinements, and breaking transfers.
5. Save a PortfolioSnapshot for offline recompilation/replay and comparison.

See the complete synthetic example. It uses a portfolio with 80 units of covered equity, 20 of missing equity, and 10 of cash:

compiled = usf.compile_portfolio_evidence(
    universe, observations=observations, taxonomy=taxonomy,
    policy=usf.PortfolioMetricPolicy(
        concept="OperatingCashFlow", unit="currency", period_kind="FY",
        transform="below", threshold=0,
    ),
)
hidden = ["sector", "industry", "issuer_id", "target_status"]
report = usf.portfolio_headline_report(
    compiled, headline="Less than 30% negative annual operating cash flow",
    decision=us.threshold_decision("<", .30),
    public=["sector"], hidden=hidden, scope="eligible",
    candidate_refinements=["industry", "target_status"],
    direct_target_refinements=["target_status"],
)
print(report.to_markdown())

The example's covered-book share is 25%; the eligible observed share is bounded by 20%–40%. Cash is explicitly excluded from that denominator. Missing equity is retained as unknown rather than dropped or assigned zero. scope="covered" asks only about the observed book; scope="eligible" includes missing eligible positions. Core provided-row retention and upstream universe coverage remain separate fields.

actual_headline and summary_support each return supported, contradicted, or inconclusive. They describe whether the declared pass condition holds, rather than core's pass status, which means a decision is invariant even when the invariant decision is failure. Eligible summary bounds hold unknown weights fixed and let their missing metrics vary over the policy's target_bounds. Binary below metrics use [0, 1]; continuous value metrics require an explicit currency and optional domain bounds. Without domain bounds, unknown eligible values make the headline inconclusive. An empty covered book is supported as an evidence/coverage artifact without a core solve.

The below transform tests the reported base-unit value, not an inferred unrounded economic amount. Fact units, dimensions, period kind, and optional currency must match the policy. max_age_days can reject stale periods. Fact IDs and source metadata, derivation inputs, original fiscal ends, and normalized fiscal labels remain in evidence. An adapter must explicitly provide issuer mappings and period/source semantics; the compiler does not guess them.

Taxonomy labels carry a scheme, optional version, availability, and effective timestamp. Undated current labels produce taxonomy_as_of_unknown diagnostics. Set strict_taxonomy_as_of=True to require all three historical fields for required labels. Future classifications are unavailable. Availability fields do not independently establish the integrity of a provider's historical archive.

Refinements are marked independent or direct_target. target_status is a direct disclosure of the tested classification. Other direct-target labels must be declared explicitly. Each refinement is evaluated in the headline's scope: disclosing covered status cannot remove missing whole-book uncertainty.

Breaking witnesses refer to the covered book, preserve its public-bucket totals, and show issuer/state transfers in covered percentage points, eligible percentage points, and currency amounts. They do not impute missing values or certify an eligible-scope breaking distance. The same forward Q is respected by default (respect_q=True); set it false only for an explicitly unrestricted inverse comparison.

Mandate Constraints

q = usf.q_portfolio_mandate(
    compiled, hidden=hidden,
    mandate=usf.PortfolioMandate(
        issuer_caps={"issuer-B": .28},
        locked_issuers=["issuer-A"],
        preserve_columns=["industry"],
    ),
    base_q=us.q_tv_budget(.05),
)

Caps are shares of the normalized covered book. Locked issuers fix their total weights; preserved columns fix every category total and permit transfers within those groups. Restrictions must be represented in hidden, and the observed book must satisfy its caps. Unknown issuers are errors. These policies compile to the core q_moment_bounds preset and can intersect other convex Q presets. Forward bounds and inverse witnesses use the same constraints; no second mandate solver is introduced. Non-saturated constrained witnesses need CVXPY. Trade execution and liquidity constraints are outside this model.

Offline Portfolio Snapshots

snapshot = usf.capture_portfolio_snapshot(
    report, capture_references={"holdings-response": raw_sha256},
)
saved = snapshot.to_json(indent=2)
restored = usf.PortfolioSnapshot.from_json(saved)
replayed = restored.replay()
changes = usf.compare_portfolio_snapshots(snapshot, restored)
runtime_changes = restored.runtime_differences()

Bundles embed normalized input evidence, the scope policy and ledger, compiled rows, claim/Q configuration (including tuple-keyed moments), result digest, optional raw capture hashes, and runtime/source versions. Loading checks the result digest, input/result consistency, and deterministic recompilation. Replay makes no provider calls. Built-in portable Q values are required; callable environments are not serialized. SHA256 checks detect corruption and identify supplied captures; they do not authenticate a vendor archive. Compare periods only after reviewing scope, policy, evidence, and taxonomy changes.

Declarative Allocation Tables

allocation_table compiles declared leaf/subtotal axes and disclosed margins into the existing named-linear feasibility engine:

table = usf.allocation_table(
    rows=[usf.AllocationMember("Phone"), usf.AllocationMember("Other")],
    columns=[usf.AllocationMember("US"), usf.AllocationMember("Outside US")],
    margins=[
        usf.AllocationMargin("company", total_fact),
        usf.AllocationMargin("phone", phone_fact, rows=["Phone"]),
        usf.AllocationMargin("us", us_fact, columns=["US"]),
    ],
    measure="Revenue", unit="USD", as_of="2026-10-06T00:00:00Z",
    exhaustive=True,
)
target = table.target("phone_outside_us", rows=["Phone"], columns=["Outside US"])
problem = table.problem([target])
result = usf.triangulate_disclosure(problem)
snapshot = usf.capture_disclosure_snapshot(
    problem, facts=table.facts, as_of=table.as_of,
    target=target.name, tier="reported", assumptions=table.assumptions,
)

Leaves form caller-declared exhaustive, nonnegative partitions. Add explicit Other leaves when needed. A subtotal declares its children and creates no extra cells; overlapping selections and cyclic hierarchies raise errors. structural_zeros=[("Phone", "US")] is an explicit exclusion assumption. Literal qualifiers are preserved in the compiled problem; jurisdiction meaning is never inferred from a label. Margins must share entity, period, unit, currency, and measure. A margin whose source concept differs from the declared measure requires measure="Revenue" on that AllocationMargin to assert equivalence while retaining the original fact concept.

Fact precision drives rounding intervals. Missing precision requires an explicit exact=True; it is never silently treated as exact. Existing triangulation, conflict detection, evidence validation, attribution, attainable allocations, and disclosure snapshots consume the compiled problem directly. For signed financial reconciliations use reconciliation_constraint with explicit signed residual variables instead of a nonnegative allocation table.

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.

That is narrower than a full model-risk-management workflow, but it maps to a real review artifact: a validation pack can state whether the reported segmentation pins down the expected-loss, default-rate, LGD, delinquency, or approval-benefit metric under a declared composition stress test.

Recommended Positioning

Lead with the saturated fixed-public-law audit:

q = "saturated"

Saturated stress keeps the reported public segment masses fixed and allows any retained hidden cell inside each public segment to receive that segment's mass. It is conservative, easy to explain, and does not require defending a hand-chosen radius. It answers the first control question:

Does this public segmentation pin down the metric at all on the retained support?

Radius-based presets are still useful, but they should be treated as secondary sensitivity scenarios:

q = usf.q_portfolio_mix_shift(radius=0.25)
q = usf.q_exposure_weighted_tv(radius=0.10)
q = usf.q_factor_exposure_shift(...)
q = usf.q_regional_concentration_shift(...)

Those scenarios require a governance rationale: historical drift, challenger monitoring, an approved sensitivity grid, or another documented benchmark. A validator can reasonably ask why a radius is acceptable, so the report should state that rationale in reviewer notes or model-review documentation.

Model-Risk Boundaries

This plugin is evidence for one control: reported-segmentation adequacy. It does not validate the underlying PD, LGD, EAD, prepayment, delinquency, capital, or approval-benefit model. It also does not replace:

• calibration and backtesting;
• discrimination or ranking performance;
• population stability / drift monitoring;
• override, policy, or use-test review;
• documentation of assumptions and limitations;
• independent validation of model design, implementation, and governance.

The report is meant to sit beside those controls. It says whether the segmentation used to communicate or govern a supplied metric is stable under declared hidden-composition stress.

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.

Disclosure Triangulation

Some finance questions are not hidden-composition audits. They are feasibility questions over overlapping disclosures:

Given several reported totals, component containments, rounded growth rates, anchors, and analyst assumptions, what interval remains possible for an undisclosed scalar quantity?

Use triangulate_disclosure(...) for that shape. It delegates to the core updatesupport named-linear feasibility API, so there is no separate finance solver and no issuer-specific logic.

import updatesupport_finance as usf

growth_constraints = usf.rounded_growth_constraints(
    "component_growth",
    current="component_current",
    previous="component_previous",
    growth_percent=55.0,
    rounding=5.0,
    provenance="Example rounded growth disclosure",
    verified=True,
)

spec = usf.disclosure_triangulation_spec(
    variables=[
        usf.disclosure_variable("component_previous"),
        usf.disclosure_variable("component_current"),
        usf.disclosure_variable("total_previous"),
        usf.disclosure_variable("total_current"),
    ],
    constraints=[
        usf.exact_disclosure_constraint(
            "reported_total_previous",
            "total_previous",
            100.0,
        ),
        usf.exact_disclosure_constraint(
            "reported_total_current",
            "total_current",
            200.0,
        ),
        usf.containment_constraint(
            "previous_containment",
            child="component_previous",
            parent="total_previous",
        ),
        usf.containment_constraint(
            "current_containment",
            child="component_current",
            parent="total_current",
        ),
        *growth_constraints,
        usf.interval_disclosure_constraint(
            "current_anchor",
            "component_current",
            lower=90.0,
            category="assumption",
        ),
    ],
    targets=[
        usf.disclosure_target(
            "previous_component",
            "component_previous",
            label="Previous-period component",
        )
    ],
    tiers=[
        usf.disclosure_tier(
            "T0 containment",
            [
                "reported_total_previous",
                "reported_total_current",
                "previous_containment",
                "current_containment",
            ],
        ),
        usf.disclosure_tier(
            "T1 + growth + anchor",
            [
                "reported_total_previous",
                "reported_total_current",
                "previous_containment",
                "current_containment",
                "component_growth_lower",
                "component_growth_upper",
                "current_anchor",
            ],
        ),
    ],
)

report = usf.triangulate_disclosure(spec)
print(report.to_markdown())

The output is a tiered feasibility interval report with active-constraint tables, endpoint assignments, binding constraints, provenance metadata, JSON exports, and DataFrame exports. Rounded-growth helpers assume the previous period variable is nonnegative and encode rounded percentages as inclusive linear relaxations.

Use attribute_disclosure_constraints(...) to rank which active disclosures actually narrow a target interval. It removes one constraint or constraint group at a time, re-solves the interval, and reports the resulting width increase. The underlying named-linear report also includes side-specific binding constraints and HiGHS marginal diagnostics for each solved endpoint.

Use disclosure_claim(...) when the review question is an assertion rather than an interval:

claim = usf.disclosure_claim(
    target="component_previous",
    tier="T2 + anchor disclosure",
    lower_at_least=50.0,
)

audit = claim.audit(report)
print(audit.to_markdown())

The audit returns pass, fail, or inconclusive, plus the feasible interval, margin to failure when certified, relevant attribution rows, endpoint dual diagnostics, and structured exports.

For an analyst-facing artifact, use disclosure_audit_pack(...) to bundle the source disclosures, active constraints, headline interval, claim verdict, constraint attribution, endpoint diagnostics, assumptions, reviewer notes, and limitations:

pack = usf.disclosure_audit_pack(
    report,
    claim=claim,
    sources=[
        {
            "label": "Reported total",
            "value": "200.0",
            "url": "https://example.com/filing",
            "description": "Source disclosure used as an equality constraint.",
        }
    ],
    assumptions=[
        "The undisclosed component is nonnegative.",
        "The component cannot exceed the disclosed containing total.",
    ],
)

print(pack.to_markdown())

The pack is the preferred review shape when the output needs to be attached to an analyst note, disclosure QA memo, model-review pack, or evidence archive.

A complete generic example is available in examples/disclosure_triangulation.py:

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

The Exxon Mobil examples demonstrate the same API on public SEC disclosures:

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

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

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

The capex example also runs a public-representation frontier over candidate disclosure refinements, showing that segment disclosure stabilizes the Upstream-share claim while geography alone does not.

Analyst Workflow

For feed-backed disclosure analysis, the package also provides:

• DisclosureFact, precision-aware fact constraints, and explicit evidence links;
• compatibility, filing-version, availability, and derivation-lineage diagnostics;
• claim-aware supporting/opposing allocation tables with residual checks;
• rounded-amount, reconciliation, and signed stock-flow helpers;
• source-linked irreducible conflict diagnosis;
• versioned offline snapshots, replay, and structured before/after comparisons.

See the disclosure evidence guide and examples/disclosure_evidence_workflow.py for a complete offline example. Vendor retrieval and historical filing-version selection belong in the adapter.

Portfolio review

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. Start with saturated fixed-public-law stress.
5. Add radius-based mix, TV, factor, or concentration scenarios only when the radius has a documented review rationale.
6. Set a review threshold for hidden-composition ambiguity.
7. 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="saturated",
    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 saturated segmentation adequacy 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="saturated",
    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, saturated and radius-based stress scenarios, 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 inspect the flagship saturated audit and adjust sensitivity 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 as the conservative benchmark
• bounded portfolio-mix shift as a documented sensitivity scenario
• exposure-weighted total-variation shift
• factor-exposure shift, when factors=... is supplied
• regional concentration shift
• observed no-shift baseline

Use the grid to show whether a conclusion depends only on the saturated worst-case benchmark or also appears under narrower governance-approved scenarios.

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