Forecast receivables collection and liquidity

What it's for

Turns receivables from a static aging table into a right-censored cash, dispute, default and liquidity distribution with explicit support and evidence refusal.

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.

{
  "current_receivables": [
    {
      "age_periods": 1,
      "evidence_verified": true,
      "face_amount": 100,
      "id": "open-portfolio",
      "risk_class": "standard-commercial",
      "state": "open"
    },
    {
      "age_periods": 2,
      "evidence_verified": true,
      "face_amount": 50,
      "id": "disputed-portfolio",
      "risk_class": "standard-commercial",
      "state": "disputed"
    }
  ],
  "current_unrestricted_cash": 100,
  "historical_state_episodes": [
    {
      "duration_periods": 2,
      "evidence_verified": true,
      "id": "receivable-episode-0",
      "outcome": "paid",
      "risk_class": "standard-commercial",
      "start_age_periods": 0,
      "state": "open"
    },
    {
      "duration_periods": 2,
      "evidence_verified": true,
      "id": "receivable-episode-1",
      "outcome": "disputed",
      "risk_class": "standard-commercial",
      "start_age_periods": 1,
      "state": "open"
    },
    {
      "duration_periods": 2,
      "evidence_verified": true,
      "id": "receivable-episode-2",
      "outcome": "defaulted",

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.

{
  "configuration": {
    "horizon_periods": 3,
    "maximum_age_bucket": 12,
    "minimum_state_exposure": 0,
    "prior_strength": 5,
    "seed": 3,
    "simulations": 500
  },
  "decision": "receivables_collection_forecast_supported",
  "failed_gates": [],
  "guardrails": [
    "Risk classes must be aggregate, lawful operating cohorts and never inferred protected or personal traits.",
    "Right-censored episodes remain exposure; unresolved invoices are not defaults or zero cash.",
    "Forecasted collections are conditional planning distributions, not audited cash, credit scores or permission for customer coercion."
  ],
  "method": "empirical_bayes_age_state_competing_risk_receivables_simulation",
  "state_support_diagnostics": [
    {
      "period_exposure": 60,
      "risk_class": "standard-commercial",
      "state": "disputed"
    },
    {
      "period_exposure": 60,
      "risk_class": "standard-commercial",
      "state": "open"
    }
  ],
  "summary": {
    "collected_cash_p10": 2.5,
    "collected_cash_p50": 15,
    "collected_cash_p90": 105,
    "current_face_amount": 150,
    "current_receivable_count": 2,
    "defaulted_face_amount_p90": 150,
    "disputed_face_amount_p90": 100,
    "liquidity_probability": 1,
    "minimum_cash_p10": 90
  },
  "truncation": {
    "support_rows_omitted": 0
  },
  "unsupported_risk_states": []

How it works

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

1. 1 Convert resolved and censored open/disputed state episodes into period exposures and paid, disputed, defaulted or survived outcomes without relabeling censoring as default.
2. 2 Estimate age-state transition probabilities with risk-class pooled empirical-Bayes shrinkage, including prior-only age cells inside the governed range, and gate current states on total historical exposure.
3. 3 Draw common market scenarios, simulate each current receivable through open/disputed/paid/defaulted states, credit paid and default-recovery cash at event time, add coherent fixed cash flows, and report collection/default/dispute and minimum-cash distributions.

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": "forecast cash collection disputes defaults and" }
  → finds "forecast_receivables_collection_and_liquidity"

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

gitrevio_capability_run
  { "capability_id": "forecast_receivables_collection_and_liquidity", "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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