An Analytical Study on Central Bank Digital Currencies (CBDCs) and Their Disruptive Effects on Traditional Financial Infrastructures

Central Bank Digital Currencies (CBDCs) represent a fundamental shift in the design and operation of modern financial infrastructures. This paper presents a comprehensive analytical and AI-assisted framework to examine the disruptive impact of CBDCs on traditional banking systems, settlement mechanisms, and liquidity networks. The study systematically evaluates retail and wholesale CBDC architectures, focusing on their implications for scalability, interoperability, cybersecurity, and institutional roles. To address the limitations of existing descriptive and macroeconomic analyses, a hybrid Autoencoder-Isolation Forest (AE-IF) model is employed to detect transaction-level anomalies and forecast systemic risk within CBDC transaction streams. Experimental evaluation using simulated CBDC settlement data demonstrates that the proposed hybrid approach achieves superior detection accuracy, reduced false alarms, and millisecond-level inference latency compared to standalone models. The findings highlight that while CBDCs enhance transaction efficiency and transparency, they also introduce new challenges related to operational resilience, data concentration, and bank disintermediation. This work contributes an integrated, technically grounded framework that bridges policy analysis, financial infrastructure design, and AI-driven monitoring, offering central banks actionable insights for resilient CBDC deployment.