ML-Powered Credit Assessment Expanding Financial Inclusion

A prominent East African microfinance institution wanted to expand lending to underbanked segments but struggled with high default rates and slow manual credit assessments. Traditional credit scoring didn't work for customers with limited credit history. We developed a comprehensive credit risk assessment platform that processes alternative data sources including M-Pesa statements, mobile money transactions, and behavioral data. The system uses advanced parsing to handle diverse transaction formats, normalizes data from multiple sources, calculates income and expense patterns, detects fraud indicators, and produces risk-tiered recommendations with complete audit trails. Our machine learning models were trained on historical loan performance data, identifying patterns that predict default risk more accurately than traditional methods while enabling the lender to serve previously excluded segments.