Calculate technology economic capital

What it's for

Turns technology tail risk into the finance language boards and investors already use—expected loss, economic capital, diversification, capital charge and risk-adjusted return—while making shared platform risk impossible to count twice.

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.

{
  "capital_charge_rate": 0.1,
  "confidence_level": 0.9,
  "minimum_portfolio_raroc": 0.1,
  "scenarios": [
    {
      "id": "ordinary",
      "probability": 0.6
    },
    {
      "id": "adverse",
      "probability": 0.3
    },
    {
      "id": "severe",
      "probability": 0.1
    }
  ],
  "shared_loss_groups": [
    {
      "evidence_verified": true,
      "id": "cloud-provider",
      "loss_by_scenario": {
        "adverse": 20,
        "ordinary": 0,
        "severe": 200
      },
      "member_unit_ids": [
        "platform",
        "data"
      ]
    }
  ],
  "units": [
    {
      "direct_loss_by_scenario": {
        "adverse": 30,
        "ordinary": 5,
        "severe": 100
      },
      "evidence_verified": true,
      "gross_profit_by_scenario": {
        "adverse": 80,
        "ordinary": 100,

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.

{
  "allocation": {
    "detail_truncated": false,
    "economic_capital_reconciliation_error": 0,
    "effort": 8,
    "method": "exact_shapley",
    "unit_allocations": [
      {
        "allocated_economic_capital": 165,
        "allocated_expected_loss": 35,
        "capital_diversification_effect": 87,
        "expected_gross_profit": 89,
        "interpretation": "positive_risk_capital",
        "raroc": 0.3273,
        "risk_adjusted_value": 37.5,
        "standalone_economic_capital": 252,
        "unit_id": "platform"
      },
      {
        "allocated_economic_capital": 141.6,
        "allocated_expected_loss": 28.4,
        "capital_diversification_effect": 87,
        "expected_gross_profit": 62.3,
        "interpretation": "positive_risk_capital",
        "raroc": 0.2394,
        "risk_adjusted_value": 19.74,
        "standalone_economic_capital": 228.6,
        "unit_id": "data"
      },
      {
        "allocated_economic_capital": 31.2,
        "allocated_expected_loss": 8.8,
        "capital_diversification_effect": 0,
        "expected_gross_profit": 35.6,
        "interpretation": "positive_risk_capital",
        "raroc": 0.859,
        "risk_adjusted_value": 23.68,
        "standalone_economic_capital": 31.2,
        "unit_id": "product"
      }
    ]
  },
  "configuration": {
    "capital_charge_rate": 0.1,

How it works

Decision analysis — Turn uncertainty, cost and risk appetite into a defensible choice, with the reasoning left inspectable.

1. 1 Align every aggregate unit's gross profit and direct loss to one probability-weighted scenario set, add each verified shared-loss group once when any member is present, and calculate expected net profit plus exact probability-mass VaR/CVaR.
2. 2 Define economic capital transparently as loss CVaR minus expected loss, apply the finance-owned capital charge and RAROC hurdle, and preserve zero-capital and negative marginal-capital cases instead of forcing unstable ratios.
3. 3 Allocate expected loss and economic capital by the coalition game induced by direct and shared losses: enumerate exact Shapley coalitions through twelve units and use deterministic seeded permutations beyond that, reconciling allocations back to the portfolio total.

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.

Calibrating it to your org

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 loss varcvar unexpectedloss" }
  → finds "calculate_technology_economic_capital"

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

gitrevio_capability_run
  { "capability_id": "calculate_technology_economic_capital", "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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