Global Reinsurance ILS Risk Model using Claude Agents SDK and Azure
Reinsurance treaties and Casualty ILS require complex risk aggregation and exposure profiling across global portfolios. Build an automated underwriting agent using Claude Agents SDK on Azure to evaluate counterparty exposure and catastrophe risk concentration. Achieve a 92% calibration accuracy score against historical loss benchmarks.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an automated reinsurance ILS risk modeling agent with Claude Agents SDK deployed on Azure infrastructure for complex portfolio loss aggregation.
How work is evaluated
Evaluates risk aggregation and calibration accuracy on casualty ILS portfolio loss scenarios.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
valid_risk_metrics
Expected Loss must be non-negative and within valid mathematical bounds.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
calibration_score
Model risk calibration score against historical loss distributions • target: 0.92 • range: 0.7-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build Claude Agents SDK structured workflow with extended thinking capabilities
Deploy agent pipeline on Azure AI infrastructure with secure data connections
Aggregate risk exposure for Casualty ILS bonds and reinsurance treaties
Measure risk calibration score against benchmark disaster loss scenarios
Reference links and supporting material
Anonymized global reinsurance loss portfolio dataset containing 1,000 catastrophe and casualty treaty structures.
How this agent runs
Evaluates risk aggregation and calibration accuracy on casualty ILS portfolio loss scenarios.
Challenge input
JSON representing portfolio treaty limits, attachment points, and underlying perils
Claude Agents SDK
Provides deep reasoning and tool integration for complex financial treaty parsing.
Azure
Enterprise cloud hosting environment for secure agent model orchestration.
Evaluated output
JSON containing expected_loss_pct (float), tvar_99 (float), and calibration_score (float)
- Expected Loss must be non-negative and within valid mathematical bounds.
- Model risk calibration score against historical loss distributions • target: 0.92 • range: 0.7-1
- Benchmark: ReinsuranceRiskBench
- Calibration Score target: 0.92
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Azure · Required
- DeepSeek · Optional
- Lepton AI · Optional
Evaluation contract
- valid_risk_metrics · Weight 1
- calibration_score · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.