Build a Distressed Capital Recovery Scenario Planner

A restructuring analyst comparing negotiated recovery scenarios.

Translate supplied enterprise recovery scenarios into claim-level recoveries. Expose priority assumptions, costs, and contested claims before a restructuring analyst reviews the result.

Key: claim_id + scenario_id.

Fields: face_amount, assumed_priority, collateral_pool, disputed_status, distributable_value, costs.

Keep legal priority assumptions separate from observed accounting balances.

Reconcile claims and available distributable value.

Apply the explicitly supplied simplified priority waterfall.

Stress costs, contested claims, and recovery values.

Export recovery ranges and legal-review exceptions.

A recovery waterfall with disputed-claim controls and claim-level scenario comparisons.

A conserved-value recovery allocation engine.

A review packet separating assumed priority from verified claim documentation.

Runnable local application, setup commands, and a dependency lockfile.

Versioned input data, data dictionary, acquisition instructions, and rights notes.

Calculation traces, source references, assumption records, and exported review artifacts.

A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases.

A recorded complete workflow with a reproducible manifest and explicit limitations.

Aggregate distributions cannot exceed available value.

A claim cannot recover more than its supplied cap.

Do not convert accounting seniority into a legal conclusion.

Contested priority blocks a single definitive recovery result.

An additional unit of available value goes to the first unsatisfied tier, not proportionately to every claimant.

The attached starter files are prototype inputs. Expected starter answers are visible. They are not independent holdouts.

Full target: Use fictional senior and junior claims with supplied priorities and recovery scenarios. Public filings provide disclosure structure, not adjudicated legal priority.

Complete target contracts and expert review remain pending. Public observations are not historical release vintages.

NumPy, Pydantic, Plotly

Use an available model appropriate to the task. Record provider, model identifier, version or retrieval date, settings, cost, and latency. Use deterministic code for calculations. Models may extract, retrieve, or explain evidence. They must not invent missing records, override approval rules, or claim professional authority.

This is a fictional recovery model, not legal advice or an insolvency distribution order.

No fabricated records, source citations, professional endorsements, or expert-adjudication claims.

No future-data leakage into dated decisions, model fitting, or historical valuation inputs.

No unauthorized payments, account restrictions, regulatory filings, or production changes.

No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation.

No concealed uncertainty or silently invented inputs required for a numerical conclusion.

Correct domain calculations and justified methods with documented assumptions. Weight: 30.

Source lineage, temporal integrity, rights checks, and explicit synthetic-data labels. Weight: 25.

A working application with review, export, error handling, and reproducible state. Weight: 20.

Adversarial cases, uncertainty, missing-data behavior, and sensitivity analysis. Weight: 15.

Evidence-linked conclusions that distinguish facts, assumptions, and unresolved judgments. Weight: 10. Score each dimension from zero to two. Weighted score = sum(weight * score / 2). A failed safety gate blocks acceptance.