Presentation Abstracts
Registration and Summit Schedule
| Speaker | Presentation Details |
|---|---|
| Petar Jevtic | Title: Structural Probabilistic Modeling for Cyber Risk: Random
Graph and Bond Percolation Approach
Abstract: In the last decade, the accelerated use of IoT devices and their integration into new systems and business applications, coupled with the advent of blockchain technology and digital assets, have created novel landscapes of cyber risk whose pricing escapes traditional insurance methods. That is why with several instructive examples, we will demonstrate a new flexible approach for cyber risk characterization in novel settings. First, we'll discuss hospital IoT infrastructure, as hospital networks have been a target of cyberattacks for over a decade. As a result of a cyber attack, a healthcare facility may experience millions of dollars in losses from various types of resulting damage. Second, we showcase the cyber risk model for client-server network architecture found across various businesses in different industries and sectors. The use case examples in this context include implantable medical devices in healthcare, smart buildings infrastructure, application for ride-sharing services such as Uber and Lyft, and application of vehicle-to-vehicle cooperation in traffic management. Finally, in the third example, we will showcase an application of smart contract risk modeling. |
| Frank Chang | Title: Actuaries in Non-Traditional, Tech Roles Abstract: Actuaries are uniquely qualified to help many companies quantify and manage a host of new and emerging risks in industries beyond insurance. This transition from being in a function that is integral to a company's mission to playing important but supporting roles to a company also has its own challenges, rewards, and focus. We'll cover how the work differs for actuaries in insurance and actuaries in tech as well as some of the tools and problem-solving techniques used. |
| Pavel Shevchenko | Title: Cyber risk frequency, severity and insurance viability
Abstract: In this study an exploration of insurance risk transfer is undertaken for the cyber insurance industry in the United States of America, based on the leading industry dataset of cyber events provided by Advisen. We seek to address two core unresolved questions. First, what factors are the most significant covariates that may explain the frequency and severity of cyber loss events and are they heterogeneous over cyber risk categories? Second, is cyber risk insurable in regards to the required premiums, risk pool sizes and how would this decision vary with the insured companies industry sector and size? We address these questions through a combination of regression models based on the class of GeneralizedAdditive Models for Location Shape and Scale (GAMLSS) and a class of ordinal regressions. These models will then form the basis for our analysis of frequency and severity of cyber risk loss processes. We investigate the viability of insurance for cyber risk using a utility modelingframework with premiums calculated by classical certainty equivalence analysis utilizingthe developed regression models. Our results provide several new key insights into the nature of insurability of cyber risk and rigorously address the two insurance questions posed in a real data driven case study analysis. Based on: P.V. Shevchenko, J. Jang, M. Malavasi, G.W. Peters, G. Sofronov, S. Trück (2023). The Nature of Losses from Cyber-Related Events: Risk Categories and Business Sectors. Journal of Cybersecurity 9(1). M. Malavasi, G. W. Peters, P.V. Shevchenko, S. Trück, J. Jang, G. Sofronov (2022). Cyber Risk Frequency, Severity and Insurance Viability. Insurance: Mathematics and Economics 106, p. 90-114. |
| Sooie-Hoe Loke | Title:A Poisson-Tweedie Collective Reserving Model for Estimating Provider Payments in Value-based Contracts Abstract: Value-based contracting has become a force in the United States healthcare system over the past decade. A prevalent type of value-based contract is "gain-sharing" where, if the population cost at the end of the contract period is less than an agreed target, the gain is shared with the provider group (the converse occurs when the result is a loss). Using a loss-reserving Poisson-Tweedie framework, we model the insurer's reserve for the VBC portion, as well as for the traditional fee-for-service portion. The model is then applied to actual US healthcare data and various reserve analyses are performed. |