This study addresses the growing cyber risks of banks by proposing an innovative, end-to-end dual-layer blockchain-based cyber fraud (CF) response system that integrates Safeguard (SG) and Block guard (BG) mechanisms. The comprehensive solution offers an actionable framework for bank managers to mitigate CFs by prioritizing fraud detection, leveraging early warning signals (EWS), and implementing tailored, need-based control measures before, during, and after a fraud event.
The study uses a multi-method approach, beginning with an extensive literature review on fraud identification, assessment, and prevention strategies. A theoretical framework is constructed to support the proposed SG and BG measures. Machine learning-based data analysis, using Artificial Neural Networks, is employed to dynamically assess the severity of CFs in real time. A managerial action plan for each phase of the fraud lifecycle is presented.
The research underscores the necessity for an adaptable, dual-layered response system that transitions from reactive to proactive and predictive mitigation strategies. The study introduces a novel approach incorporating SG and BG mitigation measures, enabling managers to detect early warning signals and implement robust post-fraud interventions.
The dual-layer approach enhances the sector's resilience to CFs by providing a robust, adaptive framework for fraud prevention and mitigation. This approach helps maintain stability, SG the bank's reputation, and improve overall risk management practices.
This study is unique in its development of an integrated SG and BG response system, combining machine learning, blockchain technology, early warning signals, and a structured before-during-after fraud control model. The research also highlights the critical role of bank managers in implementing and overseeing this innovative response system.
