Artificial Intelligence (AI)-driven Financial Technology (FinTech) systems have emerged as transformative tools for enhancing financial inclusion and strengthening risk management in financial institutions. In developing economies such as Nepal, Microfinance Institutions (MFIs) play a critical role in poverty alleviation and access to finance but continue to face challenges related to credit risk, fraud, operational inefficiencies, and limited outreach to underserved populations. This systematic review synthesizes existing empirical and theoretical literature on AI-enabled credit scoring, fraud detection, explainable AI, and regulatory governance frameworks in financial services, with a specific focus on applicability to microfinance contexts. Following PRISMA-based screening and thematic synthesis, 42 peer-reviewed and institutional studies were analyzed. Findings indicate that machine learning models significantly outperform traditional statistical approaches in credit risk prediction and fraud detection, while explainable AI techniques such as SHAP and LIME enhance transparency and regulatory trust. However, substantial gaps remain regarding ethical governance, bias mitigation, and deployment in low-resource microfinance environments. The paper proposes a Nepal-specific conceptual framework aligned with Nepal Rastra Bank (NRB) policies and highlights research directions for responsible AI-driven FinTech adoption in microfinance sectors.
Bajgai et al. (Thu,) studied this question.