Build an AI Credit Bureau for Informal Merchants

1. 1

   Choose one supply chain, not one country

   Fast moving consumer goods to neighbourhood shops, pharmaceutical distribution to independent chemists, building materials to small contractors, agricultural inputs to smallholders. Each has its own paperwork, payment terms and seasonality. One vertical gives you comparable data and a defensible score. Several give you a pile of unrelated observations.

2. 2

   Sign two or three distributors as data partners first

   A wholesaler supplying several hundred shops already has the ledger you want and already has a receivables problem you can solve. Approach them with the collections product, not the bureau vision. Agree in writing what data you may retain and use for scoring, because retrofitting that permission later is close to impossible.

3. 3

   Build the document reader before the model

   Purchase orders on letterhead, handwritten delivery books, photographs of invoices taken in bad light, payment confirmations pasted into chat. Getting structured line items, amounts, dates and merchant identity out of that reliably is most of the technical work and most of the moat. Measure your extraction accuracy against hand-checked samples and keep measuring it.

4. 4

   Solve merchant identity before you solve anything else

   The same shop appears as three names across two distributors with a different phone number on each invoice. Without a resolved identity there is no file, no score and no bureau. Combine phone number, location, tax or trading identifier where one exists, and owner name, and accept that some resolution will need a human in the loop early on.

5. 5

   Ship the collections agent and get paid for it

   Automated reminders before due date, on due date and after, over the messaging channel that merchant actually reads, in their language, with a tone that protects the supplier relationship rather than damaging it. Log every promise to pay and every payment. This is the product that produces revenue in month one and the behavioural record everything else rests on.

6. 6

   Build the score only once you have outcomes

   You need paid and unpaid outcomes across at least a few seasonal cycles before a score means anything. Start with transparent, explainable features (days beyond terms, order consistency, recovery behaviour after a missed payment) rather than an opaque model, because your first buyers will interrogate every input and regulators will too.

7. 7

   Sell reports and monitoring to the people already exposed to these merchants

   Suppliers extending trade credit, microlenders, asset finance companies, insurers and other distributors in the same chain. Ongoing monitoring with alerts when a merchant's behaviour deteriorates is worth more, and recurs better, than a one-off report at the point of application.

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Best for: Founders who have worked in lending, distribution or collections in an emerging market and can read a repayment ledger without needing it explained

Not for: This is not for someone who wants to ship in a quarter. Credit data compounds slowly, the licensing questions are real, and the score only becomes valuable after enough merchants have had the chance to pay or fail to pay across a full cycle of seasons.