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Build Residential Loan Portfolio Stress-Tester using Pydantic AI

Prudential's target $3 billion home improvement portfolio exposes investors to macroeconomic fluctuations in mortgage interest rates and home equity levels. Developers will build a typed stress-testing agent using Pydantic AI. The agent subjects a portfolio of home improvement loans to various economic shock scenarios (e.g., +200 bps rate hikes, 10% home price drops) and generates structured risk reports detailing expected default loss rates.

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What you are building

Construct a type-safe residential loan portfolio stress-testing agent using Pydantic AI.

How work is evaluated

Evaluation

Evaluates accuracy of expected loss calculations under specific rate/hpi shock scenarios.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Loss Amount Calculation

Ensures projected_loss_amount matches total_portfolio_value * projected_cdr_pct

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Precision Metric

Fidelity of stress calculation • target: 1 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Define strict Pydantic schemas for macro-stress test inputs and portfolio output metrics

  • Implement multi-scenario financial loss projection logic inside Pydantic AI agents

  • Calculate portfolio Cumulative Default Rate (CDR) and Loss Given Default (LGD)

  • Output structured compliance reports for institutional risk oversight committees

Resources

Dataset notes

Dataset of 20 portfolio stress-test scenarios with varying interest rate shocks and HPI movements.

Challenge setup

Evaluates accuracy of expected loss calculations under specific rate/hpi shock scenarios.

Challenge input

JSON with total_portfolio_value, avg_cltv, rate_shock_bps, hpi_drop_pct

Pydantic AI

Required framework for typed outputs.

Evaluated output

JSON with projected_cdr_pct, projected_loss_amount, stress_rating

Evaluation checks
  • Ensures projected_loss_amount matches total_portfolio_value * projected_cdr_pct
  • Fidelity of stress calculation • target: 1 • range: 0-1
Configured evaluation evidence
  • Benchmark: CreditStressBench
  • Precision Metric target: 1
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Coplay AI · Optional
  • StackAI · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • Loss Amount Calculation · Weight 1
  • Precision Metric · Weight 1

Metrics

  • Precision Metric · Target 1

Benchmarks

  • CreditStressBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with total_portfolio_value, avg_cltv, rate_shock_bps, hpi_drop_pct

    Output: JSON with projected_cdr_pct, projected_loss_amount, stress_rating

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build Residential Loan Portfolio Stress-Tester using Pydantic AI