Journal of Information Organization Ezendu Ariwa 16 2 2026 https://doi.org/10.6025/jio/2026/16/2/59-78 https://www.dline.info/jio/fulltext/v16n2/jiov16n2_2.pdf Cybersecurity incidents have emerged as a major source of financial disruption for organizations across industries. The increasing frequency and sophistication of cyberattacks necessitate quantitative approaches to evaluate operational exposure, financial volatility, and systemic cyber risk. This study presents a comprehensive analytical framework for cyber-financial risk assessment using a structured cybersecurity incident dataset containing 1,902 observations. The research integrates correlation analysis, predictive regression modeling, and Monte Carlo simulation to evaluate relationships among operational variables, predict financial damage, and estimate tail-risk exposure under uncertain cyberattack conditions. Pearson, Spearman, and Kendall Tau correlations were applied to identify linear and non-linear associations among incident response metrics, recovery costs, downtime duration, and reputational impact indicators. Multiple regression techniques, including Linear Regression, Ridge Regression, Random Forest Regressor, and Gradient Boosting Regressor, were employed to predict financial damage and identify the most influential predictors of cyber-financial loss. Furthermore, a stochastic compound risk framework and Monte Carlo simulation were implemented to estimate expected annual loss, Value at Risk (VaR), and Conditional Value at Risk (CVaR). The findings reveal substantial heavy-tailed financial exposure, significant predictive importance of reputational impact severity, and catastrophic loss scenarios that conventional averagebased risk metrics fail to capture. The study contributes a practical and scalable quantitative framework for cyber risk management, cyber insurance planning, and strategic financial resilience.