STATISTICAL ANALYSIS OF CYBER RISK EXPOSURE AND FRAUD DETECTION IN CLOUD-BASED BANKING ECOSYSTEMS

Abstract

This study presented a comprehensive statistical examination of the interrelationships between cyber risk exposure, control maturity, and fraud detection efficiency within cloud-based banking ecosystems. The research aimed to quantify how exposure dimensions—specifically identity and access control, encryption and data security, network segmentation, monitoring and incident response, and governance compliance—affect the frequency and severity of fraudulent transactions across financial institutions operating in hybrid and public cloud environments. Drawing upon an extensive review of 127 empirical and theoretical papers published in the domains of cybersecurity analytics, cloud computing, and financial risk management, this study developed a robust quantitative framework to assess and predict the probability of fraud events. The dataset incorporated multi-institutional secondary data derived from transaction logs, cyber incident reports, and fraud management systems covering a 30-month observation period. Descriptive statistics, correlation matrices, and multivariate regression analyses were employed to identify the statistical strength, direction, and significance of relationships among variables, while hierarchical regression and reliability testing ensured model accuracy and construct consistency. The findings revealed that higher levels of cyber risk exposure were significantly associated with increased fraud frequency and greater financial loss severity, whereas greater control maturity exerted a strong negative influence, effectively mitigating exposure-induced vulnerabilities. The regression models exhibited high explanatory power (adjusted R² exceeding 0.60), confirming that exposure and control constructs jointly predict the likelihood and magnitude of fraud with strong statistical reliability. Furthermore, the study demonstrated that certain exposure subdimensions, particularly authentication integrity and encryption quality, contributed most prominently to fraud probability, emphasizing the operational importance of technical rigor and governance maturity. The comprehensive synthesis of prior research and empirical validation highlighted that risk management in cloud-based financial systems must transition from qualitative compliance-based practices toward quantitative, data-driven governance. The results offered both theoretical and practical implications for cybersecurity strategists, policy regulators, and financial institutions by providing a replicable model capable of evaluating cyber risk exposure and fraud dynamics through statistical inference. Overall, this study provided a measurable, evidence-based foundation for enhancing cyber resilience and fraud prevention in modern, cloud-enabled banking ecosystems.