Impact of AI and ML in reducing Credit-Risk with Alternative Data: A Bibliometric Analysis

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) with alternative data has emerged as an important approach for improving credit-risk assessment, particularly in addressing the limitations of conventional credit-scoring systems. This study aims to examine the intellectual structure, research trends, influential studies, and emerging themes in the application of AI and ML for credit-risk reduction using alternative data. A bibliometric analysis was conducted using Scopus-indexed literature, with the search conducted on 15 September 2026. After applying the defined inclusion and exclusion criteria, 2,674 relevant articles were considered for analysis. The study employs citation analysis, co-citation analysis, bibliographic coupling, keyword co-occurrence, and co-authorship analysis to identify major research clusters and developments in the field. The findings indicate a substantial growth in scholarly research linking AI, ML, alternative data, credit assessment, and financial decision-making. The analysis identifies major intellectual clusters around credit-risk prediction, machine-learning-based credit scoring, alternative data, financial technology, and explainable and responsible AI. Bibliographic coupling further highlights interconnected research streams and influential contributions that have shaped the development of the field. The study demonstrates that alternative data combined with AI and ML can broaden the information base available for credit assessment and support more data-driven risk-management practices. However, issues related to data quality, privacy, transparency, bias, explain ability, and regulatory compliance remain important areas for future research. The study contributes to the existing literature by mapping the evolution and intellectual structure of AI- and ML-based credit-risk research and identifying directions for future investigation.