Quantifying the Economic Impact of Ransomware: Cyber Risk Modeling with Gamma Regression

Abstract

Ransomware has evolved into one of the most disruptive forms of cybercrime, resulting in severe financial and operational losses for organizations across various sectors. Despite its increasing prevalence, quantitative models for evaluating the drivers of ransomware losses remain underdeveloped, limiting both academic insight and practical risk management. This study addresses this gap by developing and empirically validating a statistical framework for quantifying the financial impact of ransomware incidents. Drawing on ransomware cases extracted from the Advisen Cyber Loss Database, we employ a generalized linear model (GLM) with Gamma regression and log link to estimate how socio-technical factors shape loss severity. Our analysis examines four categories of predictors: (1) Technology, operationalized via database server involvement in incidents; (2) Preparedness, captured through insurance coverage ratios; (3) Settlement Length, reflecting negotiation and resolution timelines; and (4) Multi-entity Connections, representing the number of affected organizations in an incident. The results indicate that each factor influences the anticipated magnitude of losses. Insufficient preparedness is correlated with greater financial damages, whereas incidents involving database servers and prolonged settlement periods result in disproportionately substantial losses. Moreover, multi-entity connections exacerbate losses due to cascading effects across organizational networks. To assess the robustness of the model, bootstrapping techniques are employed, confirming the stability of the coefficient estimates and underscoring the model’s reliability under resampling. By providing empirical evidence of the drivers of ransomware loss severity, this study contributes to both academic research and practical cybersecurity governance. For scholars, it demonstrates the utility of Gamma regression in modeling highly skewed cyber loss distributions. For practitioners, it highlights quantifiable indicators that can inform cybersecurity investment and organizational preparedness strategies. More broadly, the findings highlight the importance of interdisciplinary approaches that integrate cybersecurity management with socio-technical dimensions of cyber risk. This work lays a foundation for future studies that extend to newer datasets and explore AI-enhanced risk prediction, thereby advancing both theoretical understanding and applied resilience against ransomware in the evolving cyber threat landscape.