Broker Guy Carpenter has issued a new report in which it says the (re)insurance market is creating the tools which will allow it to commit more capacity to cyber risks breaking the current impasse between supply and demand.
The report investigates the key drivers of cyber catastrophe model differences using advanced predictive analytics. This study links to its earlier report titled Through the Looking Glass: Interrogating the Key Numbers Behind Today’s Cyber Market, which provides a multi-vendor view of global cyber industry loss potential.
The new study, entitled Under the Lens: Investigating Cyber Vendor Model Divergence, found that, while significant progress has been made in advancing cyber catastrophe vendor models over the past decade, a notable degree of variability across model outputs still exists.
The broker said the report is designed to offer clarity about the drivers of this model variability to help cyber carriers establish their unique view of risk and support exposure management and capacity deployment decisions.
Erica Davis Global co-head of Cyber, Guy Carpenter, explained “By marrying cyber catastrophe modelling expertise and predictive analytics, this study helps insurers and reinsurers identify market segments where the model view of risk is most divergent. This will result in more confidence for insurers and reinsurers in making decisions about their deployment of capacity, which ultimately supports the cyber industry’s sustainable growth forward.”
The research, which utilised advanced machine learning techniques, analysed three major cyber catastrophe models – Guidewire Cyence, CyberCube, and Moody’s RMS – to uncover the key factors that affect modeled loss differences.
The report uncovered a number of parameters driving discrepancies, including revenue, industry sector classification and differing treatment of specific coverages.
“The clear driver of loss variability across the three tested vendor models is revenue,” it stated. The study found that annual revenue input results in the highest modelled loss differences, with the greatest model divergence concentrated in the nano (under $1 million) and micro ($1 million to $5 million) revenue bands.
The report noted that cyber risk data for larger organisations is readily accessible but there is a clear reduction in the information available on smaller and micro risks leading to greater variability in modelling.
The study flagged that a deeper understanding of the relative treatments of low-revenue organizations by the vendor models will be essential to the alignment of internal views of risk to vendor views and predicted that increased penetration of cyber in nano and micro markets will lead to better data and reduced variability in modelling going forward.
According to the research, industry sector classification is the second most impactful driver of model variability.
Guy Carpenter added industry sectors use a range of technologies and differ in how they carry out their business. Different sectors may also vary in their security posture, resiliency, and attractiveness to threat actors – all factors that affect the modelling. The report noted that the different classification approach taken by each vendor model leads to additional variability.
Ransomware and malware events are top event drivers for modelled losses across vendors. The report identified differing interpretations and treatments of the Ransomware & Extortion coverage indicator for the models. Highlighting the fact that cyber policies are written with differing coverages using diverse definitions for each, it stated that until this becomes more standardized, there will continue to be challenges in aligning policy wordings with available model functionality.
Additionally, Regulatory Defence & Fines coverage, like Ransomware & Extortion, is indicative of the varying views on coverage options by the vendor models but also illustrates the differing approaches taken in defining cyber scenarios.