Artificial Intelligence Driven Credit Scoring: Opportunities and Risk Implications for Financial Institutions

Abstract

This study employs a literature review to examine how artificial intelligence and machine learning are transforming credit scoring and credit risk management in financial institutions. It synthesizes evidence on artificial intelligence model performance, the role of alternative data for “thin file” and unbanked borrowers, and implications for explainability, fairness, and risk governance. The findings show that neural networks, gradient boosting, random forests, and other techniques consistently outperform traditional logistic regression scorecards in predicting default and loss, while alternative data such as digital footprints, transactional records, and platform activity help expand access to credit and support more inclusive lending. At the same time, high-dimensional “black box” models raise concerns around model opacity, privacy, and data governance, and recent work documents “predictably unequal” outcomes across demographic groups. The review concludes that artificial intelligence-driven credit scoring generates an efficiency inclusion risk trade-off and highlights the need for explainable artificial intelligence tools, fairness-aware modelling, and robust regulatory and governance frameworks to ensure that benefits do not come at the expense of consumer protection and prudential stability.