Title: Modeling Malicious Hacking Data Breach Risks

Report Abstract:
Malicious hacking data breach has caused millions of dollars of financial losses each year, and more and more companies seek for the cyber insurance coverages. The lack of suitable statistical approaches for scoring the breach risks has become an obstacle in the insurance industry. We propose a novel frequency-severity model for analyzing the hacking breach risks from the individual company level which would be valuable for the underwriting purpose. We discover that the breach frequency can be modeled by a hurdle-Poisson model which is different from the negative binomial model used in the literature, and the breach severity shows a heavy tail which can be captured by a nonparametric-GPD model. We further discover a positive nonlinear dependence between the frequency and severity, which is also accommodated in our model. Both the in-sample and out-of-sample studies show that the proposed frequency-severity model by accommodating the nonlinear dependence has satisfactory performances, and is superior to the other models including the independence frequency-severity and Tweedie models.

Speaker: Maochao Xu

Date/Time: 09:30 – 10:30AM, July 13, 2019

Location: Room 706, College of Economics and Management Building, Jiangjun Rd. Campus

Speaker Biography:
Professor Maochao Xu received his PhD in statistics from Portland State University in the United States in 2010. He is currently an associate professor of mathematics at Illinois State University and serves as a network insurance consultant for CloudCover Company. His research interests include application statistics, network risk management, and network security insurance. In recent years, relevant research results have been published on internationally renowned journals of statistics and insurance actuarial fields, such as Technometrics, IEEE Transactions on Information Forensics and Security, IISE Transactions, Insurance: Mathematics and Economics.