Alternative Credit Scoring: Serving the Thin-File Customer

Across emerging markets, billions of people lack access to traditional financial services—not because they're not creditworthy, but because they don't have conventional credit histories.

The Thin-File Challenge

Traditional credit scoring relies on data from previous loans, credit cards, and banking relationships. For the estimated 1.4 billion adults who are "credit invisible," this creates a catch-22: you need credit to build credit.

Alternative Data Sources

Mobile Phone Data

Call patterns, app usage, and recharge behavior can reveal financial discipline and stability.

Utility Payments

Regular payment of electricity, water, and internet bills demonstrates reliability.

E-commerce Activity

Online shopping patterns and payment history provide insights into spending behavior.

Social Connections

Network analysis can identify patterns associated with financial stability.

Machine Learning for Credit Assessment

Advanced ML models can process these diverse data sources to generate accurate credit scores. Our models achieve default prediction rates comparable to traditional scoring while serving previously underserved populations.

The Impact

Alternative credit scoring isn't just about business opportunity—it's about financial inclusion. By enabling credit access for thin-file customers, we're helping people start businesses, afford education, and improve their lives.