An SVNS-PT-DEA methodological framework for assessing Fintech business models in Vietnam under data scarcity

Fintech is driving financial innovation, expanding access to services, and supporting digital transformation in the banking sector in Vietnam. However, evaluating fintech business models remains challenging due to the lack of publicly available and comparable enterprise-level data such as investment capital, revenue, operating costs, number of users, and transaction volume. In this context, this study proposes an SVNS-PT-DEA methodological framework to support the relative assessment of fintech business models based on expert data under data scarcity conditions. The framework integrates single-valued neutrosophic sets (SVNS) to handle uncertainty and hesitation in linguistic evaluations, Data Envelopment Analysis (DEA) to model input–output relationships, and Prospect Theory to capture perceptions of gains, losses, and risk aversion. The framework is illustrated using eight representative fintech models in Vietnam, evaluated based on three input and three output indicators and assessments from 15 experts. The results show that digital credit scoring, e-wallets/digital payments, and open API/banking-as-a-service models exhibit the most favorable input–output relationships. However, these results are not interpreted as evidence of actual economic efficiency but as an illustration of the framework’s operational capability and decision-support value under limited data conditions.