Cohort: 3

Members: Mercedes Bidart, Ana Lucía Escobar Velásquez, Juan Constain, Eduardo Carrasquilla, Michael Moreno

Problem: Over 80% of informal workers in Latin America - corner shops, street vendors, and delivery drivers - have no access to formal credit because traditional scoring relies on past borrowing history. Without a record, you get rejected. The alternative is loan sharks run by criminal organizations, charging up to 800% interest. 

Approach: A multimodal AI scoring engine that reads financial SMS notifications (which capture real-time income and spending patterns), combined with business videos, product photos, and social media activity; treating all of these as credit signals. The system extracts over 535 variables and uses visual embeddings to assess business viability from images alone, adding 25% predictive power. 

Result: The platform has processed more than 300,000 individual scores and supported around 49,000 loans. It reports an 80.1% renewal rate, a reduction in clients’ use of informal “gota-a-gota” lenders from 38.6% to 28.5%, and 13.11% of clients reporting at least 15% income growth.