Build MSME Credit Underwriter with Claude Agents SDK and o3 Reasoning
MSME credit decisioning requires accurate risk assessment across non-standardized bank statements and tax filings. Build an autonomous underwriting agent using Claude Agents SDK and o3 deep reasoning models to calculate risk scores and credit limits with complete regulatory transparency.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an MSME credit assessment engine combining Claude Agents SDK tools and o3 model reasoning for Account Aggregator financial records.
How work is evaluated
Evaluates underwriting decision accuracy and transparency against benchmark MSME loan histories.
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.
audit_trail_exists
Checks that complete audit trail reasoning was generated
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
underwriting_accuracy
Accuracy relative to gold standard credit outcomes • target: 0.92 • 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
Construct multi-turn financial document reasoning workflows with Claude Agents SDK
Leverage OpenAI o3 model reasoning capabilities for complex cash flow analysis
Generate explainable credit score outputs aligned with RBI lending standards
Reference links and supporting material
Anonymized MSME financial profiles and Account Aggregator transaction summaries.
How this agent runs
Evaluates underwriting decision accuracy and transparency against benchmark MSME loan histories.
Challenge input
JSON containing annual turnover, debt service ratio, and GST reconciliation delta
Claude Agents SDK
Provides agent orchestration and robust tool calling
o3
Delivers deep reasoning for financial auditing
Evaluated output
JSON containing decision, sanctioned limit, credit score, and reasoning breakdown
- Checks that complete audit trail reasoning was generated
- Accuracy relative to gold standard credit outcomes • target: 0.92 • range: 0-1
- Benchmark: MSME-Credit-Bench-v2
- Underwriting Accuracy target: 0.92
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Writer · Optional
- Zed · Optional
- o3 · Required
Evaluation contract
- audit_trail_exists · Weight 1
- underwriting_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.