Attribute commercial dependency tail loss

Calculate expected loss, VaR and CVaR for commercial value concentrated in shared technical components, then allocate every modeled tail-loss dollar exactly once across components with normalized negative-log survival hazard rather than overlapping leave-one-out sensitivities.

Forecasting & survival Estimate CTOs Tech CEOs IT managers Team leads Investors

What it's for

Shows executives and investors which shared components truly concentrate commercial downside—and reconciles the component story to the same tail-loss total finance sees.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

Field Type Role Required
commercial_sources array of objects (3 fields) ≥ 1 item Evidence Yes
components array of objects (2 fields) ≥ 1 item Evidence Yes
exposure_edges array of objects (5 fields) ≥ 1 item Evidence Yes
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_component_tail_loss_share number ≥ 0, ≤ 1 Your calibration Optional
maximum_cvar_loss_fraction number ≥ 0, ≤ 1 Your calibration Optional
maximum_source_tail_loss_share number ≥ 0, ≤ 1 Your calibration Optional
scenarios array of objects (4 fields) ≥ 2 items Evidence Yes
tail_probability number > 0, ≤ 0.5 Your calibration Optional

Each exposure_edges record

Field Type Required
commercial_source_id string (non-empty) Yes
component_id string (non-empty) Yes
evidence_verified boolean Yes
id string (non-empty) Yes
loss_fraction_if_failed number (> 0, ≤ 1) Yes
Example input 
{
  "commercial_sources": [
    {
      "evidence_verified": true,
      "id": "enterprise-contract",
      "value_at_risk": 100
    },
    {
      "evidence_verified": true,
      "id": "usage-margin",
      "value_at_risk": 200
    }
  ],
  "components": [
    {
      "evidence_verified": true,
      "id": "api"
    },
    {
      "evidence_verified": true,
      "id": "database"
    }
  ],
  "exposure_edges": [
    {
      "commercial_source_id": "enterprise-contract",
      "component_id": "api",
      "evidence_verified": true,
      "id": "contract-api",
      "loss_fraction_if_failed": 1
    },
    {
      "commercial_source_id": "enterprise-contract",
      "component_id": "database",
      "evidence_verified": true,
      "id": "contract-database",
      "loss_fraction_if_failed": 0.5
    },
    {
      "commercial_source_id": "usage-margin",
      "component_id": "database",
      "evidence_verified": true,
      "id": "margin-database",
      "loss_fraction_if_failed": 1

Truncated for display — the full payload is 80 lines.

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

Example output 
{
  "assumptions": [
    "Commercial sources are unique and additive across sources; simultaneous component failures combine within each source as a multiplicative survival union, so one source can never lose more than its governed value at risk.",
    "Normalized negative-log survival hazard is an exact, order-independent full allocation of each modeled source loss among failed exposed components. Unlike leave-one-out sensitivities it reconciles exactly, but it is an allocation convention rather than causal blame or a discrete Shapley value.",
    "Joint scenarios preserve common causes, failure dependence and the decision horizon. Scenario omission, recovery timing, liquidity, parameter uncertainty and unobserved architecture links require separate stress tests and prospective backtesting.",
    "Component and source concentration are aggregate structural risk signals, never worker, vendor or customer rankings and never automatic authority to re-architect, terminate, investigate, insure or invest."
  ],
  "commercial_source_diagnostics": [
    {
      "commercial_source_id": "usage-margin",
      "component_dependency_count": 1,
      "expected_loss": 60,
      "tail_conditional_loss": 200,
      "tail_loss_share": 0.6667,
      "value_at_risk": 200
    },
    {
      "commercial_source_id": "enterprise-contract",
      "component_dependency_count": 2,
      "expected_loss": 40,
      "tail_conditional_loss": 100,
      "tail_loss_share": 0.3333,
      "value_at_risk": 100
    }
  ],
  "component_diagnostics": [
    {
      "component_id": "database",
      "expected_loss_attribution": 70.1967,
      "exposed_commercial_source_count": 2,
      "failure_scenario_count": 2,
      "tail_loss_attribution": 201.9674,
      "tail_loss_share": 0.6732
    },
    {
      "component_id": "api",
      "expected_loss_attribution": 29.8033,
      "exposed_commercial_source_count": 1,
      "failure_scenario_count": 2,
      "tail_loss_attribution": 98.0326,
      "tail_loss_share": 0.3268
    }
  ],
  "configuration": {

Truncated for display — the full payload is 117 lines.

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

  1. 1 Build one sparse commercial-source/component exposure graph and evaluate every coherent joint failure scenario with a multiplicative survival union that caps each source loss at its value at risk.
  2. 2 Identify the probability-weighted CVaR tail, calculate source contributions, and allocate each source-scenario loss across its failed exposed components in proportion to negative log retained value.
  3. 3 Reconcile component attribution exactly to scenario loss and portfolio CVaR, then apply source, component and total-value concentration gates without converting overlapping sensitivities into additive blame.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Assumptions & guardrails

  • Training examples precede their outcomes, censoring and unresolved work are represented, and deployment populations remain comparable to validation cohorts.
  • Source identities are unique, conditional loss fractions share a horizon, and scenarios cover dependence, common causes and recovery effects material to the decision.
  • A predictive interval or risk estimate is not a deadline promise, causal explanation, or individual-performance judgment.
  • Hazard allocation is an exact accounting convention, not causal Shapley credit, fault attribution, customer ranking or authority to change architecture.

Minimum evidence

  • commercial_sources: at least 1 rows/items
  • components: at least 1 rows/items
  • exposure_edges: at least 1 rows/items
  • scenarios: at least 2 rows/items

How to validate it

Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • point-in-time sparse commercial exposure graph and frozen scenario matrix whose shared source identities remain stable across components, contracts, products and failure states
  • value/currency/horizon perimeter, source uniqueness, component and dependency boundary, loss-fraction meaning, common-cause/recovery scenario coverage, probability calibration, CVaR tail and concentration gates, hazard-allocation convention and accountable finance/risk/architecture/product/legal owners

Calibration workflow

  1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
  2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
  3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
  4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
  5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
  6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own. 

gitrevio_capabilities_search
  { "q": "calculate expected loss var and cvar" }
  → finds "attribute_commercial_dependency_tail_loss"

gitrevio_capability_describe
  { "capability_id": "attribute_commercial_dependency_tail_loss" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "attribute_commercial_dependency_tail_loss", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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