Optimize insurance retention

What it's for

Compares cyber and operational insurance by total downside economics—not premium alone—so boards can see when a cheaper policy merely transfers tail risk back to the company.

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_penalty_weight": 0.5,
  "loss_scenarios": [
    {
      "id": "severe",
      "loss": 1500,
      "probability": 0.1
    },
    {
      "id": "material",
      "loss": 500,
      "probability": 0.2
    },
    {
      "id": "ordinary",
      "loss": 50,
      "probability": 0.7
    }
  ],
  "policies": [
    {
      "id": "high-retention",
      "policy_limit": 1000,
      "premium": 80,
      "retention": 500
    },
    {
      "id": "balanced",
      "policy_limit": 1300,
      "premium": 160,
      "retention": 200
    },
    {
      "id": "low-retention",
      "policy_limit": 1450,
      "premium": 300,
      "retention": 50
    }
  ],
  "tail_probability": 0.1
}

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.

{
  "assumptions": [
    "Gross loss scenarios include dependence, aggregation, defense expense, exclusions, waiting periods, counterparty performance, taxes, and claim timing relevant to each policy comparison.",
    "Premium, retention, and limit quotes share one coverage wording and horizon; non-price policy differences are either encoded or separately governed.",
    "Risk-adjusted cost reflects the declared CVaR preference and does not replace broker, legal, regulatory, or counterparty review."
  ],
  "configuration": {
    "cvar_penalty_weight": 0.5,
    "maximum_tail_cost": null,
    "tail_probability": 0.1
  },
  "decision": "insurance_retention_policy_supported",
  "method": "insurance_retention_expected_cvar_optimization_v1",
  "policy_diagnostics": [
    {
      "cvar_total_cost": 360,
      "expected_insurer_recovery": 190,
      "expected_total_cost": 255,
      "policy_id": "balanced",
      "policy_limit": 1300,
      "premium": 160,
      "retention": 200,
      "risk_adjusted_cost": 307.5,
      "tail_constraint_feasible": true
    },
    {
      "cvar_total_cost": 350,
      "expected_insurer_recovery": 235,
      "expected_total_cost": 350,
      "policy_id": "low-retention",
      "policy_limit": 1450,
      "premium": 300,
      "retention": 50,
      "risk_adjusted_cost": 350,
      "tail_constraint_feasible": true
    },
    {
      "cvar_total_cost": 580,
      "expected_insurer_recovery": 100,
      "expected_total_cost": 265,
      "policy_id": "high-retention",
      "policy_limit": 1000,
      "premium": 80,
      "retention": 500,

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 Freeze comparable policy wordings and quotes plus gross joint loss scenarios that include aggregation, dependence, claim timing, exclusions, expenses, and counterparty performance.
2. 2 Apply each policy's retention and limit to every gross loss, add premium, and compute expected total cost, CVaR total cost, insurer recovery, and the declared risk-adjusted objective.
3. 3 Reject policies violating the tail-cost constraint, choose the lowest-cost feasible option, and separately review coverage wording, counterparty, legal, tax, and regulatory differences.

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": "select an insurance retention and limit" }
  → finds "optimize_insurance_retention"

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

gitrevio_capability_run
  { "capability_id": "optimize_insurance_retention", "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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