AI Fraud Detection and Financial Trust in Nepal’s SME Payment Ecosystem: A Readiness Framework

Abstract

Nepal’s rapid transition to digital payments has outpaced the development of cybersecurity and defensive infrastructure, leading to a significant increase in cyber-enabled fraud and a pronounced trust deficit among small and medium enterprises (SMEs). While financial institutions worldwide have increasingly adopted AI-driven fraud detection systems, these technologies are generally developed for mature, data-rich environments and are predicated on the availability of robust cloud infrastructure, regulatory clarity, and high levels of digital literacy. This study examines the applicability of AI-based fraud detection within Nepal’s fragmented, infrastructure-constrained, and trust-sensitive SME payment environment. Employing a structured integrative literature review, we synthesized 15 peer-reviewed and institutional sources to map global AI capabilities against Nepal’s localized technological and regulatory realities. The analysis reveals a pronounced transferability gap: supervised machine learning models, behavioral analytics, and graph-based detection frameworks cannot be directly transplanted due to data fragmentation, reactive fraud reporting, weak XAI enforcement, and acute psychological barriers among merchants. To address this mismatch, we propose a five-stage conceptual readiness framework that prioritizes data interoperability, unsupervised anomaly detection, economic triage, and Explainable AI compliance before advanced model deployment. This study contributes a trust-centered, infrastructure-aware AI adoption pathway specifically designed for emerging economies, offering policymakers, fintech developers, and financial institutions a pragmatic roadmap for responsible AI-enabled fraud management in Nepal.