Mia Mucia Desda S. Si. M. M Pengarang

This study aims to systematically synthesize the literature on Financial Technology (FinTech) lending and credit risk management in Small and Medium Enterprises (SMEs). The method used is a Systematic Literature Review (SLR) with the PRISMA 2020 approach, using data sources from the Scopus database. The literature selection process resulted in 14 included articles published between 2021 and 2025. The results of the analysis show that FinTech lending plays an important role in increasing SME access to financing through the use of alternative data and machine learning-based analytical approaches, which can reduce information asymmetry and improve the accuracy of credit risk predictions. However, the literature also identifies significant challenges in the form of model risk, algorithmic unfairness, limited transparency of credit decisions, and governance and regulatory issues. Thematic synthesis reveals six main themes, namely SME information asymmetry, alternative data usage, machine learning application, explainability and fairness, unbalanced data challenges, and FinTech lending governance and sustainability. This study concludes that the potential of FinTech lending in supporting SME financing can only be realized sustainably if it is balanced with strengthened credit risk management, algorithm transparency, and an adaptive regulatory framework. These findings provide conceptual and practical contributions to policy development, risk models, and future research agendas in the field of SME FinTech lending. Keywords : Financial Technology Lending; Credit Risk Management; Small and Medium Enterprises (SMEs); Peer-to-Peer Lending; Machine Learning; Alternative Data; PRISMA; Systematic Literature Review.