7bcd623519e6e8509c72c4a 20260529070052327 chitu:chitu chitkarauniversitypublications@chitkara.edu.in WEB-FORM Journal of Technology Management for Growing Economies JTMGE 0976545X 24563226 10.15415/jtmge https://tmg.chitkara.edu.in/ 03 13 2026 03 13 2026 16 2 10.15415/jtmge/2025.162 https://tmg.chitkara.edu.in/2025/volume-16-issue-2-2025/ The Emergence of Artificial Intelligence in Credit Ratings: A Systematic Review Falak . Research Scholar, Kurukshetra University, Kurukshetra, Haryana, India. Pooja Malhotra Dyal Singh College, Karnal, Haryana, India. Purpose: This paper analyzes systematic literature focused on technological innovation in credit rating and credit rating agencies (CRAs), particularly emphasizing developments in artificial intelligence (AI), innovative models, and machine learning (ML) models. Credit ratings play a vital role in financial markets by evaluating the creditworthiness of various entities, which in turn influences investor decisions. Methods: The research methodology employs a systematic analysis of the literature, utilizing the Web of Science database, from which pertinent literature was extracted, refined, and analyzed using Biblioshiny in R Studio. Key research inquiries encompass publication trends, prominent authors, and institutional affiliations that contribute to the domain of technological innovation in CRAs. Findings: This study examines the evolution of AI applications in credit rating, exploring models such as ANNs, SVMs, and ensemble methods. Key research inquiries also encompass publication trends, prominent authors, and institutional affiliations that contribute to the domain of technological innovation in CRAs. Implications: The integration of AI-based models by CRAs has led to enhanced efficiency and greater predictive accuracy, outpacing traditional techniques such as logistic regression and discriminant analysis. Originality: The present systematic review provides a comprehensive understanding of how artificial intelligence is redefining the landscape of credit rating systems, marking a decisive shift from traditional, analyst-driven assessments toward data-intensive, automated, and highly accurate predictive frameworks. 03 13 2026 03 13 2026 47 57 10.15415/jtmge/2025.162004 https://tmg.chitkara.edu.in/2025/the-emergence-of-artificial-intelligence-in-credit-ratings-a-systematic-review/