Audit cash flow timing consistency

What it's for

Finds when a business case is economically correct in amount but misleading in timing—before NPV, runway, or liquidity decisions inherit shifted cash dates.

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.

{
  "cash_flows": [
    {
      "amount_scenarios": [
        -50,
        -50
      ],
      "cash_period_scenarios": [
        0,
        0
      ],
      "category": "cost",
      "economic_period_scenarios": [
        0,
        0
      ],
      "id": "investment",
      "maximum_allowed_lag_periods": 0,
      "minimum_allowed_lag_periods": 0,
      "project_id": "platform"
    },
    {
      "amount_scenarios": [
        100,
        100
      ],
      "cash_period_scenarios": [
        1,
        3
      ],
      "category": "benefit",
      "economic_period_scenarios": [
        1,
        1
      ],
      "id": "benefit",
      "maximum_allowed_lag_periods": 1,
      "minimum_allowed_lag_periods": 0,
      "project_id": "platform"
    }
  ],
  "discount_rate_per_period": 0.1,
  "maximum_npv_timing_distortion": 5,
  "scenarios": [

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.

{
  "cash_flow_diagnostics": [
    {
      "breaches_timing_rule": true,
      "cash_flow_id": "benefit",
      "category": "benefit",
      "expected_absolute_npv_timing_distortion": 7.8888,
      "expected_lag_periods": 1,
      "maximum_observed_lag_periods": 2,
      "minimum_observed_lag_periods": 0,
      "probability_lag_outside_rule": 0.5,
      "project_id": "platform"
    },
    {
      "breaches_timing_rule": false,
      "cash_flow_id": "investment",
      "category": "cost",
      "expected_absolute_npv_timing_distortion": 0,
      "expected_lag_periods": 0,
      "maximum_observed_lag_periods": 0,
      "minimum_observed_lag_periods": 0,
      "probability_lag_outside_rule": 0,
      "project_id": "platform"
    }
  ],
  "configuration": {
    "discount_rate_per_period": 0.1,
    "minimum_liquidity_buffer": 0,
    "tail_probability": 0.1
  },
  "decision": "material_cash_flow_timing_inconsistency",
  "failed_gates": [
    "timing_rule",
    "npv_timing_distortion"
  ],
  "guardrails": [
    "Economic-event, recognition, invoice, commitment, and cash-settlement dates are different facts; this audit compares only the submitted economic and cash timing rule and does not replace accounting policy.",
    "A timing exception may be a valid contract term, prepayment, deposit, refund, tax event, collection delay, or data error; it is not fraud, manipulation, vendor blame, or employee-performance evidence.",
    "Scenario amounts and dates must be frozen together before analysis; shifting only adverse cash dates or reconstructing historic forecasts after settlement creates hindsight and selection bias.",
    "Liquidity exposure is conditional on the submitted complete cash-flow perimeter and available starting liquidity; treasury restrictions, facilities, covenants, currency, tax, and working capital require explicit inclusion."
  ],
  "method": "scenario_cash_flow_timing_consistency_audit_v1",
  "project_diagnostics": [
    {

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Freeze signed scenario cash amounts together with their economic-event period, cash-settlement period, project/category identity, and category-specific permitted lag interval.
2. 2 Calculate flow-level lag exception probability and economic-versus-cash NPV distortion, aggregate project diagnostics, and reconstruct the complete scenario cash ledger into cumulative liquidity paths.
3. 3 Apply governed exception, absolute NPV-distortion, and liquidity-shortfall gates; report tail deficit and retain bounded flow, project, and scenario evidence for accounting and treasury review.

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": "audit whether economicevent and cashsettlement timing" }
  → finds "audit_cash_flow_timing_consistency"

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

gitrevio_capability_run
  { "capability_id": "audit_cash_flow_timing_consistency", "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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