This article explores the transformative role of credit and predictive analytics in advancing financial inclusion worldwide. It examines how alternative data sources and machine learning architectures are enabling more nuanced creditworthiness assessments beyond traditional metrics, particularly benefiting previously excluded populations in underserved markets. The article details the technological foundations underpinning these innovations, including explainable AI frameworks and mobile money transaction analytics, while documenting their empirical impact across diverse geographical contexts. Case studies from agricultural communities, microenterprises, and retail sectors demonstrate how contextually calibrated credit analytics can significantly expand access to formal financial services while maintaining sustainable risk profiles. The article further addresses critical ethical considerations, including algorithmic bias detection, privacy governance models, and evolving regulatory frameworks that balance innovation with consumer protection. By mapping the intersection of technology, market impact, and ethical governance, the article presents a comprehensive framework for understanding how data-driven credit assessment can contribute to broader economic development goals.
Parda Saradhi Chalamalasetty (Fri,) studied this question.