Data Infrastructure Behind TrustLoop Africa's AI Credit Scoring

The Challenge

Millions of Kenyans Are Creditworthy — Without a Credit File to Prove It

Traditional credit scoring depends on formal credit histories that most SACCO members and mobile-money users simply don't have. Lenders were stuck choosing between slow, paperwork-heavy manual underwriting or turning away creditworthy applicants who had no traditional file to evaluate.

TrustLoop set out to replace that gap with something Kenya already had at scale: rich, real transaction history from M-Pesa mobile money — if it could be turned into a reliable, fast, and defensible credit signal.

The Approach

Turning Raw Mobile Money Statements Into a Structured Credit Signal

Precise Analytics works alongside TrustLoop's team on the data engineering layer that ingests M-Pesa statement data submitted through TrustLoop's application flow, cleans and structures it, and prepares it for the AI scoring models that produce a credit decision.

That means building reliable parsing and validation for inconsistent statement formats, handling the data securely end-to-end, and keeping the pipeline fast enough to support TrustLoop's under-two-hour turnaround promise to applicants.

The Result

A Credit Score in Under Two Hours — No Branch Visit, No Paperwork

TrustLoop's SACCO and lender partners can now issue AI-backed credit decisions from mobile money data alone, in a fraction of the time traditional underwriting takes — opening credit access to members who were previously locked out by a lack of formal credit history.