Model Municipal Bond Financing with Vercel AI SDK and Claude 4 Sonnet
Financing post-disaster residential rebuilds through municipal bond issuance requires rigorous pro-forma debt coverage and cash flow modeling. Build a Vercel AI SDK application powered by Claude 4 Sonnet to generate municipal bond waterfall schedules with zero calculation errors across test scenarios.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a financial modeling platform using Vercel AI SDK and Claude 4 Sonnet to model municipal bond debt service structures for housing rebuild programs.
How work is evaluated
Tests mathematical precision and covenant compliance flags of generated bond waterfall schedules.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Covenant Flag Test
Ensures DSCR covenant warnings trigger if DSCR drops below 1.20.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Financial Accuracy Error
Percentage variance from theoretical bond amortisation schedule. • target: 0 • range: 0-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Use Vercel AI SDK streamText and generateObject functions to build structured financial responses.
Leverage Claude 4 Sonnet for precise financial debt service waterfall calculations.
Construct dynamic pro-forma cash flow tables for municipal bond repayment schedules.
Reference links and supporting material
Municipal debt issuance dataset featuring post-disaster rebuild metrics, interest rate schedules, and municipal underwriting guidelines.
Challenge setup
Tests mathematical precision and covenant compliance flags of generated bond waterfall schedules.
Challenge input
JSON with bond_principal, annual_coupon_rate, total_homes, average_home_price, term_years
AI SDK
Assigned Vercel framework for structured generation and streaming.
Claude 4 Sonnet
High-precision model selected for numerical and financial logic.
Evaluated output
JSON with annual_ds_payment, projected_dscr, full_payoff_year, covenant_met
- Ensures DSCR covenant warnings trigger if DSCR drops below 1.20.
- Percentage variance from theoretical bond amortisation schedule. • target: 0 • range: 0-10
- Benchmark: Financial_Modeling_Eval
- Financial Accuracy Error target: 0
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Vercel AI SDK · Optional
- Martian · Optional
- Claude 4 Sonnet · Required
- Vercel AI SDK · Optional
Evaluation contract
- Covenant Flag Test · Weight 1
- Financial Accuracy Error · Weight 1
Metrics
- Financial Accuracy Error · Target 0
Benchmarks
- Financial_Modeling_Eval
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with bond_principal, annual_coupon_rate, total_homes, average_home_price, term_years
Output: JSON with annual_ds_payment, projected_dscr, full_payoff_year, covenant_met
Execution harness: JavaScript
Evaluation tools
- AI SDK · TypeScript AI SDK
- Claude 4 Sonnet · LLM Inference Engine
Recipe freeze marker
No recipe freeze time is recorded.