Credit Garden
Credit Scoring for
the Invisible World.
1.7 billion people are credit-invisible. Not because they lack financial history - but because no model was built with their data. Credit Garden scores SUSU records, remittances, and mobile money. 302-point average uplift. Zero increase in default rate.
Scores What
Others Ignore
SUSU contributions, rotating savings circles, remittance consistency, mobile money velocity, market stall transaction patterns. These are not alternative data - they are the primary financial signal for 1.7 billion people. Credit Garden was built to read them with the same precision that FICO reads a credit card statement.
Built for Kingston,
Kigali, and Karachi
Every scoring model that exists today was calibrated on North American or European payment behaviour. Credit Garden was trained on Caribbean, West African, and South Asian financial patterns from the ground up. The model that scores a Jamaican small business owner was built with Jamaican financial data - not adapted from a US template.
Zero Default
Rate Increase
The 302-point average score uplift does not come at the cost of portfolio quality. Credit Garden's validation protocol runs against 36 lending corridors. Lenders using Credit Garden scores see equivalent or lower default rates compared to their existing scoring models while extending credit to populations that were previously unreachable.
Regulatory
Alignment Built In
Credit Garden was built with CARICOM regulatory frameworks, GDPR equivalents, and emerging Latin American data protection legislation integrated at the architecture stage - not retrofitted. Each jurisdiction's data constraints are modelled into the scoring pipeline, so financial institutions operating across borders do not inherit compliance risk from the data they did not collect.
API First,
Integration Fast
Credit Garden integrates with existing loan origination systems, digital banking platforms, and microfinance management software via API. No proprietary hardware. No full platform migration. A lender in Barbados and a fintech in Lagos can both connect to the same scoring engine and receive jurisdiction-appropriate outputs in under 200ms.
The Data Moat
Nobody Can Copy
The underlying training data for Credit Garden was collected by Maestro AI Labs field teams across 36 corridors over five years. 92% of this data exists in no other AI training set. Every new Credit Garden deployment enriches the model further. The accuracy advantage compounds with each new market entered - and none of it can be replicated from a data centre in San Francisco.
1.7 billion people.
$380 billion in annual lending.
The pipeline is waiting.