Build a Credit Risk Underwriting Agent with LangChain & LangGraph
Managing over $589m in export credit limits requires precise counterparty default risk scoring. Build an automated trade credit evaluation agent using LangChain and LangGraph to analyze balance sheets, geopolitical trade risk, and issue credit decisions.
What you are building
The core problem, expected build, and operating context for this challenge.
Utilize LangGraph stateful graph workflows to process Omani export credit applications and verify buyer financial health.
How work is evaluated
Evaluates accuracy of default risk scoring and compliance with max credit exposure caps.
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.
decision_check
Verify approval decision matches financial health thresholds
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
underwriting_precision
Accuracy of financial ratio calculations and credit limit decisions • target: 0.98 • range: 0.9-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct multi-stage LangGraph graphs for financial statement analysis
Implement credit scoring tools based on Credit Oman underwriting guidelines
Evaluate regional buyer default probabilities using historical macro trade signals
Generate export credit insurance approval recommendations automatically
Reference links and supporting material
Export credit insurance applications, buyer financial audits, and regional trade payment default logs.
How this agent runs
Evaluates accuracy of default risk scoring and compliance with max credit exposure caps.
Challenge input
JSON containing buyer financials
LangChain
Provides LangGraph for stateful financial graph modeling
Evaluated output
JSON underwriting decision
- Verify approval decision matches financial health thresholds
- Accuracy of financial ratio calculations and credit limit decisions • target: 0.98 • range: 0.9-1
- Benchmark: CreditRiskBench
- Underwriting Precision target: 0.98
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- LangChain · Required
- LangGraph · Optional
- Arcee AI · Optional
- LangChain · Required
- LangGraph · Optional
Evaluation contract
- decision_check · Weight 1
- underwriting_precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.