Traditional credit scoring models often fail to capture the true creditworthiness of millions, particularly underserved MSMEs and individuals who have thin credit files or lack of formal history. Utilizing real-time cash flow data -using real-time checking, savings, and transaction data - offers a more holistic view of a borrower’s financial health.

Leveraging machine learning (ML) helps identify risk patterns that traditional linear models overlook, significantly expanding access without compromising institutional safety. This evolution represents a critical junction where technological innovation meets regulatory necessity, offering a scalable pathway to reduce the credit gap and foster a more equitable financial ecosystem.