LegalLitigation

Legal Claim Economics

Use when users say "model claim economics", "litigation funding waterfall", "portfolio economics", "funder MOIC", "DBA/CFA economics", "Monte Carlo", "ATE/adverse costs", or need legal claim recoverie…

LLegalQuants·Legal·Apache-2.0

Library skill — the default version is maintained in GitHub; edits you make live in your own clone.

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legal-claim-economics

When to Use

This skill models economics. It does not recommend whether to bring, settle, fund, or abandon a claim.

Audience and Work Shape

Audience: litigation partners, litigation-finance counsel, funding analysts, and legally supervised team members with enough quantitative fluency to review assumptions and formulas.

Work shape: bounded transactional calculation in calculation_mode; accretive judgment in specification_mode. The skill models assumptions and scenarios, but the responsible lawyer or funder decides what action to take.

Legal Failure Modes

Access Modes

This skill works in three modes:

  1. Calculation mode - use supplied assumptions and an available calculator, spreadsheet, code execution environment, or arithmetic worked in-session.
  2. User-supplied model mode - review an existing spreadsheet, calculator output, term sheet, budget, damages model, or funding proposal supplied by the user.
  3. Specification mode - build the model structure, assumptions list, and formulas, but do not invent calculated outputs.

If key inputs or a calculation method are unavailable, produce an assumptions register and model specification. Do not invent IRRs, percentiles, waterfalls, or Monte Carlo outputs.

How It Works

1. Define the scenario

Capture:

If a figure is unknown, ask whether to use a range, placeholder, or scenario variable.

Every input must be labelled as user_supplied, document_sourced, calculated, or assumption_to_verify. Keep that label through the output so the reader can separate facts from modelling assumptions.

Also record currency, VAT/tax inclusion or exclusion, nominal vs real basis, date of valuation, time unit, discount-rate assumption if used, and the formula used for each calculated output. Do not present calculated figures without these modelling metadata.

2. Model revenue and costs

Include:

Flag internally inconsistent inputs and keep them out of headline outputs unless the user expressly asks to model the inconsistent scenario. Label any such output inconsistent_input_scenario.

3. Model funder economics

Support common return structures:

State clearly who receives what under each layer.

4. Apply recourse and waterfall

Build the waterfall:

  1. Recoveries received.
  2. Costs and disbursements.
  3. ATE or insurer position.
  4. Funder entitlement.
  5. Firm entitlement.
  6. Client residual.

Handle pari passu tiers, caps, floors, and shortfall allocation.

5. Run scenarios and sensitivity

At minimum, produce:

If stochastic modelling is requested, specify distributions, correlations if any, iteration count, random seed if used, tool/calculation method, and outputs including P5, P50, and P95. If you cannot actually run the calculation, do not invent percentiles; provide the model specification or pseudocode instead.

6. Report decision-useful outputs

Recommended outputs:

Distinguish calculated values from user-supplied assumptions and legal assumptions. Do not opine on merits, funding enforceability, tax treatment, regulatory capital, or professional conduct unless the user supplies the rule or counsel-approved assumption to model.

Confidence Bands

Out of Scope and Routing

Escalation

Stop and route when the user asks for a legal/tax/enforceability conclusion, when a novel jurisdiction is involved, when funder/firm/client interests conflict, when DBA/CFA/PACCAR questions affect the economics, or when inputs are internally inconsistent and the user has not expressly asked to model the inconsistent scenario.

Example

Model this claim portfolio under a CFA plus third-party funding structure. Show base, downside, and upside cases, then run sensitivity on damages, win probability, ATE, and time to resolution.

For a compact output pattern, see examples/output.md. For waterfall, recourse, return-family, Monte Carlo, and audit-bundle conventions, see references/engine-model.md.

Limitations