A Data-Driven Study on Blockchain's Security and Privacy Impact in Financial Sectors Using Machine Learning

Purpose - This research investigates the impact of blockchain technology on security and privacy within the financial sector, analysing the views of diverse stakeholders and the obstacles hindering its implementation. Design/Methodology/Approach – The study utilizes a combination of various analytical techniques, including ANOVA to compare the impact of blockchain knowledge on security, Principal Component Analysis (PCA) to identify concerns about privacy, Nonlinear Matrix Factorization (NMF) to outline problems of adoption, decision tree classification to make rapid identification, and Canonical Correlational Analysis (CCA) to study the relationship between job roles and perspectives on blockchain. Findings - ANOVA results do not have a significant effect of blockchain knowledge on security perceptions, while PCA identifies four major privacy concerns. NMF identifies the major adoption barriers: technical complexity, regulatory ambiguity, and resistance to change. Decision trees offer clear and efficient analysis, while CCA confirms significant links between job titles and views regarding the possibility of using blockchain in financial firms. Practical Implications - The implementation of blockchain technology in financial institutions would pose technical, regulatory, and organizational challenges. This research provides insight into the need for education initiatives and a clear clarification on the rules. Originality/Value Proposition - This study improves understanding of blockchain implementation through a combination of different analytical approaches to provide a comprehensive security, privacy, and organizational impact assessment within the financial realm. The findings emphasize the importance of these entities in the broader direction of blockchain within the finance sector.