Abstract: This study investigates the role of big-data credit scoring in shaping micro, small, and mediumsized enterprise (MSME) credit access within Indonesia’s fintech lending market, with particular attention to predictive performance and algorithmic fairness. Using a survey-based dataset of 427 MSME borrowers and a parsimonious SEM-PLS framework, the study examines whether big-data–driven credit assessment enhances access to finance and whether perceived algorithmic bias constrains this effect. The results show that big-data credit scoring has a positive and statistically significant impact on MSME credit access, indicating that automated and data-intensive assessment mechanisms reduce informational and procedural barriers in digital lending. In contrast, perceived algorithmic bias neither significantly affects MSME credit access nor mediates the relationship between credit scoring technology and inclusion outcomes. These findings suggest that, at the user level, the inclusionary benefits of fintech lending are driven primarily by efficiency and accessibility rather than fairness perceptions. This study contributes to the fintech and financial inclusion literature by providing user-level evidence from an emerging market and by highlighting the distinction between normative concerns about algorithmic fairness and the practical determinants of credit access. The results imply that while algorithmic governance remains essential from a regulatory perspective, improvements in big-data credit scoring effectiveness are central to expanding MSME access to finance in fintech-enabled credit markets.

Keywords: Fintech lending, Big-data credit scoring, MSMEs, Algorithmic bias, Financial inclusion

Pages: 66-81

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