Credit Rating in the Age of Artificial Intelligence

Abstract

The advent of artificial intelligence (AI) has revolutionized financial systems, particularly in the domain of credit rating, where traditional methodologies are being reshaped by data-driven innovation. This manuscript explores the transformative impact of AI on credit assessment, focusing on its ability to enhance accuracy, efficiency, and inclusivity while raising critical ethical and regulatory challenges. Historically, credit rating has relied on standardized models using limited datasets, such as payment histories and debt-to-income ratios, often excluding underserved populations lacking formal financial records. AI, with its capacity to process vast, unstructured datasets—spanning social media activity, transaction patterns, and alternative financial indicators—offers a paradigm shift toward more dynamic and personalized risk profiling. Machine learning algorithms can identify subtle patterns invisible to conventional approaches, promising improved predictive power and reduced default rates. However, this evolution is not without pitfalls. The opacity of AI models, often described as "black boxes," complicates accountability, while biases embedded in training data risk perpetuating or exacerbating inequalities. Furthermore, the integration of non-traditional data sources raises privacy concerns, necessitating robust governance frameworks. This manuscript evaluates case studies of AI-driven credit rating systems, contrasts their performance with legacy methods, and analyzes emerging regulatory responses globally. It argues that while AI holds the potential to democratize access to credit and refine risk assessment, its deployment must balance innovation with transparency, fairness, and consumer protection. By addressing these tensions, stakeholders can harness AI to build a more equitable and resilient financial ecosystem in the digital age.

Received: 17 March 2025 | Revised: 19 May 2025 | Accepted: 09 September 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Milad Shahvaroughi Farahani: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Supervision, Project administration. Gholamreza Mahmoudi: Conceptualization, Writing – review & editing. Ghazal Ghasemi: Conceptualization, Validation, Investigation.