For nearly four decades as an operational banker, I was closely involved in evaluating loan proposals across retail, MSME, and business segments. One challenge stood out repeatedly: identifying genuinely creditworthy borrowers who had little or no formal credit history. Many applicants who approached the bank were hardworking entrepreneurs, salaried individuals beginning their careers, or small business owners with stable incomes and sound business prospects. They possessed the intent and capacity to repay, yet they lacked one crucial element—a documented credit history. 

Traditional credit appraisal relied heavily on information such as credit bureau reports, financial statements, banking transactions, collateral, and documented repayment records. While these tools served the banking industry well, they often fell short when assessing first- time borrowers or businesses operating largely outside the formal financial system. 

As a credit officer, there were numerous occasions when my professional judgment suggested that an applicant was deserving of credit. Personal interactions, knowledge of the local market, business potential, and the borrower’s character inspired confidence. However, lending decisions could not rely solely on intuition. Banks are required to make objective, evidence-based decisions that comply with regulatory standards and internal risk policies. This created a persistent dilemma. Rejecting such applications meant denying deserving customers access to formal finance. Approving them without adequate supporting data meant exposing the bank to unnecessary credit risk. Consequently, many potentially successful borrowers remained outside the formal lending ecosystem. 

The emergence of Artificial Intelligence in credit assessment is fundamentally changing this landscape. Modern AI-powered credit scoring models enable lenders to evaluate........

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