<![CDATA[The rapid advancement of Artificial Intelligence (AI) has transformed the financial technology (fintech) sector, particularly Buy Now, Pay Later (PayLater) services, through the adoption of automated credit decision systems utilizing machine learning, alternative data, and credit scoring models. While AI improves the efficiency, consistency, and speed of credit evaluation, its implementation also raises concerns regarding transparency, algorithmic fairness, and user trust in automated credit decisions. Previous studies have primarily focused on improving prediction accuracy and algorithm performance, whereas PayLater research has mainly examined user behavior, technology adoption, and platform trust. Consequently, studies integrating these two perspectives remain limited. This study aims to examine the relationship between fairness perception and trust in AI-based credit decisions within PayLater services through a Systematic Literature Review (SLR). The review analyzed open-access literature published between 2021 and 2025, including scholarly articles, policy reports, and official documents, using a thematic analysis approach. The findings indicate that AI system characteristics, including transparency, explainability, Responsible AI principles, and Human-Centered AI design, play a significant role in shaping users' fairness perceptions, which subsequently influence trust in AI-based credit decisions. This study integrates the Information Systems perspective through AI-enabled Information Systems with the management perspective through Organizational Justice and Trust theories, providing a conceptual foundation for future empirical research on AI implementation in Indonesia's digital financial services.]]>
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