Is CIBIL Score a Myth? A Study on Scope for Exclusion of Human Interference and Inclusion of AI

Abstract

The Credit Information Bureau (India) Limited (CIBIL) score has traditionally functioned as the primary benchmark for credit decision-making within the Indian financial system. While it has introduced standardization and reduced discretionary lending, the rapid growth of fintech, digital transactions, and artificial intelligence (AI) has raised questions regarding its continued effectiveness and inclusiveness. This study critically evaluates the predictive power of CIBIL-based credit scoring models and examines whether reliance on historical bureau data contributes to systematic exclusion of thin-file borrowers, women, and rural populations. Using a mixed-method research design, the paper compares traditional CIBIL-driven assessments with AI-enabled credit scoring models that integrate alternative data sources such as mobile usage behavior, digital transaction patterns, and utility payment histories. The findings indicate that while CIBIL scores offer consistency, their ability to predict future credit performance is limited in dynamic economic environments. AI-based models demonstrate higher predictive accuracy and broader financial inclusion, though they introduce new governance, transparency, and ethical challenges. The study concludes that a hybrid credit evaluation framework, combining AI-driven insights with human oversight, is essential for achieving sustainable and fair credit decision-making in India.