> For the complete documentation index, see [llms.txt](https://docs.cournot.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cournot.ai/use-cases/verticals-what-cournot-enables/credit-intelligence.md).

# Credit Intelligence

Oracle layers: 3.0 + 4.0 | Status: In Development

Credit assessment is inherently a judgment problem, not a data problem. Determining creditworthiness requires synthesizing:

* Financial statements and balance sheet data
* Market conditions and sector trends
* News sentiment and management signals
* Regulatory filings and compliance history
* On-chain behavior (for DeFi protocols and DAOs)

Oracle 1.0 can deliver individual data points. Oracle 3.0 can synthesize them into a structured credit opinion with Proof of Reasoning producing an auditable judgment trail rather than an opaque score.

Oracle 4.0 takes this further: persistent credit agents maintain rolling assessments that update as new evidence emerges, producing **verifiable credit signals** for:

* Undercollateralized DeFi lending (where the lender needs more than just collateral ratios)
* Institutional DeFi (where counterparty risk assessment is a regulatory requirement)
* RWA credit markets (where on-chain credit assessment can unlock new lending verticals)