Data-driven models introduced by Artificial Intelligence (AI) have fundamentally transformed credit scoring, makingthe process more accurate, faster, and fairer. AI-based credit scoring leverages machine learning, deep learning, and hybrid modelsto uncover complex patterns across diverse data sources, including alternative and behavioral data, thereby surpassing traditionalmethods that rely solely on limited historical financial data. Explainable AI (XAI) adds transparency and interpretability,addressing the long-standing “black box” issue in automated credit decisions. However, AI-powered systems still face challengesrelated to algorithmic bias, data privacy, and security concerns. Recent research advocates adopting fairness-aware frameworksand bias-mitigation techniques, such as adversarial debiasing and continuous validation, to ensure equitable credit evaluations.Furthermore, global regulatory standards like GDPR, ECOA, and FCRA promote ethical AI practices and safeguard consumerrights. The success stories of fintech companies such as Tala and Lenddo illustrate AI’s transformative potential to promotefinancial inclusion for the unbanked and underbanked, while highlighting the need for responsible, explainable systems.Ultimately, integrating fairness-by-design principles ensures that lending decisions remain unbiased and sustainable, marking AIbasedcredit scoring as a pivotal advancement toward a transparent, inclusive, and ethically governed financial ecosystem.