AI driven Framework for Real-Time Risk Assessment & Transaction Management in Modern Banking System

The rapid digital transformation of banking has accelerated the adoption of Financial Technology (FinTech) applications, particularly in emerging economies. Despite significant growth in FinTech services, public sector banks continue to face challenges related to customer adoption, trust, security, and real-time transaction risk management. This study develops and validates an integrated FinTech Adoption and Risk Assessment Framework by combining constructs from the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Risk-Benefit Theory. A descriptive research design was employed, and data were collected from 750 FinTech users of major public sector banks in India using a structured questionnaire. Results reveal that perceived ease of use, perceived usefulness, performance expectancy, social influence, facilitating conditions, hedonic motivation, and perceived benefits significantly and positively influence user attitude, while perceived risk negatively affects attitude. The integrated model demonstrated superior fit indices (RMSEA = 0.036, CFI = 0.960, TLI = 0.954) compared with standalone TAM and UTAUT models. Based on empirical findings, a FinTech Risk Assessment and Transaction Management Framework is proposed to enhance adoption, security, and sustainable digital banking growth.