Risk Management Model for Compliance and Security in Blockchain Powered Payment Platforms - Novalita Savitri, Agnes; Hardini, Marviola; Yao, Goh (Pandawan, 2025)
Blockchain technology has revolutionized financial services by enabling decen- tralized, transparent, and tamper-resistant payment platforms. However, these innovations bring significant challenges related to regulatory compliance and security management, which threaten platform adoption and user trust. This study aims to develop and empirically validate a comprehensive risk management model that integrates both regulatory oversight and security auditing dimensions specific to blockchain-powered payment systems. A cross-sectional survey was conducted among 215 industry practitioners involved in blockchain payment platforms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study tested hypothesized relationships among regulatory over- sight, smart contract auditing, perceived compliance and security risks, risk mit- igation intent, and platform adoption intention. The results demonstrate that regulatory oversight and smart contract auditing significantly increase perceived compliance and security risks. These heightened risk perceptions positively in- fluence intentions to mitigate risks, which in turn significantly drive platform adoption. The model explains 58% and 42% of the variance in risk mitigation intent and platform adoption intention, respectively, confirming its strong ex- planatory power. This research contributes a validated, unified risk manage- ment framework that guides policymakers, platform operators, and auditors in addressing intertwined compliance and security risks. The findings support the advancement of safer, more trustworthy blockchain payment systems, fostering broader adoption and aligning with evolving regulatory landscapes.