Bank BRI implemented AI-driven credit scoring models leveraging agricultural and alternative data to assess credit risk for rural borrowers without formal credit histories. The system integrates crop yield patterns, market prices, and environmental data to provide accurate risk assessments, enabling faster loan approvals and better fraud detection.
- Reduced loan processing time from 2 weeks to under 2 days
- 40% reduction in fraud rates
- Millions of rural borrowers served
- 62% increase in loan volume via digital apps
- 45% weekly loan disbursement growth on digital platforms
Traditional credit scoring excluded rural borrowers due to lack of formal financial data, limiting financial inclusion and causing slow loan processing.
Developed localized AI credit scoring models incorporating agricultural data (crop yields, market prices), environmental risks, and alternative data sources via mobile lending apps (Pinang, Ceria).
- Expanded microloan access to millions of rural borrowers
- Maintained low default rates
- Drastically reduced loan approval times
- Enhanced fraud detection and risk management
- Increased digital loan application volumes and disbursements
