Credit analytics built for modern lending teams

Collaborative portfolio analysis, dynamic scenario modeling, and structured context that makes AI actually useful.

Work together, not in parallel

Shared pools, team-based access, and a single source of truth. Everyone sees the same portfolio, the same assumptions, the same results.

Scenarios that move with you

Adjust prepayment curves, default assumptions, and pricing in real time. See how changes ripple through your portfolio instantly.

Context for your AI agent

Structured portfolio data, an API-first architecture, and MCP integration give AI agents the context they need to do real analytical work.

From raw data to insight

credkit gives your team a shared workspace for the full credit analytics workflow.

1

Build your pool

Upload loan data or define representative lines. credkit structures it into a clean portfolio model your whole team can work from.

2

Layer in assumptions

Apply prepayment curves, default scenarios, and pricing at the pool or rep-line level. Adjust and compare without rebuilding from scratch.

3

Analyze and iterate

Expected yield, required APR, weighted average life -- run the numbers, share the results, and let AI help you explore the edges of your analysis.

Built on open-source foundations

credkit cloud is powered by the credkit Python library -- an open-source toolkit for consumer loan modeling with immutable primitives, behavioral curves, and portfolio analytics.

View on GitHub →

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