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Challenge

Build an Actuarial Climate Risk Evaluation Engine with Mastra AI and Claude 4 Sonnet

Insurance actuaries need advanced climate scenario models to underwrite regional infrastructure risks accurately. Build a TypeScript workflow using Mastra AI and Claude 4 Sonnet to extract property exposure metrics, calculate climate loss projections, and format actuarial risk reports.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a TypeScript workflow using Mastra AI and Claude 4 Sonnet to perform structured climate risk assessment for insurance portfolios under MAS risk guidelines.

Delivery guide

How work is evaluated

Evaluation

Evaluates Mastra AI workflow output quality against standard actuarial climate loss projection benchmarks.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

risk_tier_classification

Ensures property with elevation < 1.5m and high SLR scenario is classified HIGH risk

Binary check

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

Dimension 2

eal_accuracy

Calculated EAL within 5% tolerance of reference physics model • target: 0.95 • 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

What you should walk away with

  • Build TypeScript agentic workflows using Mastra AI framework

  • Leverage Claude 4 Sonnet for quantitative actuarial reasoning and analysis

  • Integrate geospatial climate flood hazard layers into underwriting steps

  • Generate MAS-compliant climate risk disclosure summaries

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic coastal asset inventory and climate vulnerability curves for Southeast Asian ports.

How this agent runs

Evaluates Mastra AI workflow output quality against standard actuarial climate loss projection benchmarks.

Challenge input

JSON object with asset_location, structural_value_usd, sea_level_rise_scenario_m

Mastra AI

Provides TypeScript workflow engine with state control

Claude 4 Sonnet

Delivers superior mathematical and technical document reasoning

Evaluated output

JSON object with risk_tier, expected_annual_loss_usd, and mas_compliance_summary

Checks for
  • Ensures property with elevation < 1.5m and high SLR scenario is classified HIGH risk
  • Calculated EAL within 5% tolerance of reference physics model • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: ActuaryBench-2026
  • Eal Accuracy target: 0.95
  • 1 public reference case
Runtime evidence
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Mastra AI · Required
Observation
  • Langfuse · Optional
Reward / Eval
  • Langfuse · Optional
Policy Serving
  • Claude 4 Sonnet · Optional
Orchestration
  • Mastra AI · Required

Evaluation contract

  • risk_tier_classification · Weight 1
  • eal_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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