With the ever-increasing reliance on technology, such as Zoom meeting, online purchase, and even national security for sensitive research, the importance of proper cyber risk management is evident, especially during the time of COVID-19 when everyone works remotely. Learning from the cyber incident history is crucial in order to build risk management and pricing models. Therefore, all the risk management concerning cyber risk should be based on data-driven evidence.

This project is a continuation of the IRisk Lab projects in Fall 2019 and Spring 2020, where a set of given cyber data is used to construct multivariate frequency and treebased models. This project aims to take a step back to fundamentally understand the cyber data and investigate the potential pitfalls when employing traditional property and casualty actuarial models to fit the cyber data.

Students: Ramsha Ahmed, Shaowen Chang, Ishaan Khanna, Evelyn Lai Jia Yi, Emmelyn Luveta, Carina Su, Yao Xiao

Supervisors: Alfred Chong, Daniel Linders, Zhiyu (Frank) Quan

Graduate Supervisor: Linfeng Zhang