Predictive Analytics in Credit Scoring: Leveraging AI for Fair and Accurate Lending Decisions

Abstract

Credit scoring has long been central to lending decisions, yet traditional models often rely on limited variables and may exhibit systemic bias. This paper explores how artificial intelligence (AI) and machine learning techniques enhance credit scoring accuracy while promoting fairness and inclusivity in financial services. By integrating structured and unstructured data sources such as transaction histories, behavioral signals, and alternative data, AI-driven models can deliver more comprehensive risk assessments. Case studies demonstrate how ensemble learning methods and gradient boosting significantly outperform legacy scoring systems in predicting default risks. The paper also highlights ethical challenges including bias mitigation, explainability of AI models, and regulatory compliance. Findings reveal that AI-enabled credit scoring not only improves loan approval efficiency but also supports responsible and inclusive finance.