Automate Agritech Underwriting via Claude Agents SDK and Azure
Agritech platforms struggle to rapidly assess smallholder farmer creditworthiness due to fragmented crop yields and financial records. Build an automated underwriting agent using the Claude Agents SDK and Azure AI infrastructure to synthesize satellite imagery logs and Account Aggregator statements. Achieve a 90% accuracy rating on credit risk classifications.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an intelligent agritech underwriting system with the Claude Agents SDK hosted on Azure AI services to automate rural credit scoring.
How work is evaluated
Evaluates underwriting risk classifications against ground-truth agricultural loan portfolios.
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.
regulatory_bounds_check
Ensures credit recommendation strictly stays within allowable limits
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
underwriting_accuracy
Accuracy in matching ground-truth risk tiers • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Utilize Claude Agents SDK extended thinking capabilities for multi-step financial risk assessment
Integrate Azure OpenAI and Document Intelligence services for document parsing
Process Account Aggregator financial statements combined with crop yield telemetry
Generate compliant underwriting summary reports adhering to RBI lending guidelines
Reference links and supporting material
Agricultural yield logs combined with synthetic Account Aggregator cash flow reports across Indian districts.
How this agent runs
Evaluates underwriting risk classifications against ground-truth agricultural loan portfolios.
Challenge input
JSON object with crop harvest history and AA cashflow metrics
Claude Agents SDK
Provides multi-step reasoning capabilities for financial document processing.
Azure
Hosts enterprise models and enterprise security environments.
Evaluated output
JSON object with risk tier, max credit limit, and explanation
- Ensures credit recommendation strictly stays within allowable limits
- Accuracy in matching ground-truth risk tiers • target: 0.9 • range: 0-1
- Benchmark: Agritech Risk Scoring Benchmark
- Underwriting Accuracy target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Azure · Required
- Mindset AI · Optional
- Retell AI · Optional
Evaluation contract
- regulatory_bounds_check · Weight 1
- underwriting_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.