The Emergence of Artificial Intelligence in Credit Ratings: A Systematic Review

Abstract

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.

  • Page Number : 47-57

  • Published Date : 2026-03-13

  • Keywords
    Technological innovation, Credit rating agencies, Artificial intelligence, Predictive analytics, Credit risk assessment, Credit ratings

  • DOI Number
    10.15415/jtmge/2025.162004

  • Authors
    Falak and Pooja Malhotra

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