Optimize technology risk limit allocation

What it's for

Moves technology risk appetite from one enterprise ceiling to an investable operating system: which units receive scarce risk capacity, what value it enables, and how diversification changes the true capital consumed.

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.

{
  "cvar_loss_limit": 140,
  "economic_capital_limit": 110,
  "expected_loss_limit": 40,
  "scenarios": [
    {
      "id": "base",
      "probability": 0.8
    },
    {
      "id": "stress",
      "probability": 0.15
    },
    {
      "id": "severe",
      "probability": 0.05
    }
  ],
  "shared_loss_groups": [
    {
      "evidence_verified": true,
      "id": "cloud-common",
      "loss_by_scenario": {
        "base": 0,
        "severe": 20,
        "stress": 10
      },
      "member_option_ids": [
        "platform-grow",
        "data-grow"
      ]
    }
  ],
  "total_nominal_limit": 100,
  "unit_limit_options": [
    {
      "dependency_option_ids": [],
      "evidence_verified": true,
      "exclusion_option_ids": [],
      "gross_value_by_scenario": {
        "base": 0,
        "severe": 0,
        "stress": 0
      },

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.

{
  "capital_allocation": {
    "effort": 4,
    "method": "exact_shapley",
    "reconciliation_error": 0
  },
  "configuration": {
    "capital_charge_rate": 0.1,
    "confidence_level": 0.95,
    "cvar_loss_limit": 140,
    "economic_capital_limit": 110,
    "expected_loss_limit": 40,
    "minimum_portfolio_raroc": 0,
    "option_count": 4,
    "scenario_count": 3,
    "total_nominal_limit": 100,
    "unit_count": 2
  },
  "decision": "risk_limits_allocated",
  "failed_gates": [],
  "finding": "risk_adjusted_limit_allocation_identified",
  "governance": [
    "Limits apply to aggregate technology risk units; they are not budgets, targets or scores for named people or teams.",
    "Finance and risk own loss/value scenarios, appetite, capital charge and RAROC; engineering owners supply executable operating envelopes and dependency evidence.",
    "Shapley allocation preserves negative diversification contributions and reconciles to portfolio economic capital; it does not imply causality or accounting ownership.",
    "A beam result is explicitly incomplete and requires challenger allocations before approval."
  ],
  "method": "risk_budget_value_cvar_capital_exact_or_beam_v1",
  "pareto_frontier": [
    {
      "economic_capital": 0,
      "expected_gross_value": 0,
      "expected_loss": 0,
      "expected_net_value": 0,
      "loss_conditional_value_at_risk": 0,
      "loss_value_at_risk": 0,
      "option_ids": [
        "platform-low",
        "data-low"
      ],
      "raroc": null,
      "risk_adjusted_value": 0,
      "total_nominal_limit": 0
    },

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 For each aggregate technology unit, admit only evidence-verified operating envelopes with a nominal risk limit, coherent gross-value/direct-loss vectors and complete option dependencies/exclusions; activate each shared loss once for any selected member option.
2. 2 Enumerate the Cartesian allocation when the governed state count permits, otherwise use a deterministic unit-wise beam; reject allocations that breach nominal, expected-loss, CVaR, economic-capital, RAROC or relation constraints and retain the nondominated value/risk frontier.
3. 3 Select the highest value after the finance-owned capital charge, then allocate expected loss and economic capital to chosen units with exact Shapley through twelve units or seeded permutations beyond that, preserving diversification contributions and reconciliation.

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": "allocate scarce aggregate technology risk limits" }
  → finds "optimize_technology_risk_limit_allocation"

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

gitrevio_capability_run
  { "capability_id": "optimize_technology_risk_limit_allocation", "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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