Achieved a 20-25 percent reduction in gross loss costs
Enabled better risk selection
Enhanced understanding of which factors were driving cyber losses
A leading global provider of property and casualty (re)insurance wanted to enhance its cyber risk strategy in order to improve risk selection, ensure risk-appropriate pricing, reduce exposure levels, boost market share, and strengthen its cyber position.
The company had developed an in-house cyber risk model to better understand factors driving cyber losses. However, many firmographics critical to pricing cyber risk – such as industry, company size, and jurisdiction – were not explicitly captured in the exposure data, making it difficult to generate reliable results. Additionally, aggregated and incomplete reinsurance books made it challenging to run portfolios through a risk model to identify affirmative cyber accumulations.
Moody’s RMS conducted a comprehensive training program with the company’s cyber team to ground them in the use of Moody’s RMS Cyber Solutions applications. The applications are designed to help users more effectively quantify, manage, and operationalize cyber risk and to establish a single view of risk across primary insurance and reinsurance books.
To address the incomplete exposure data, the native data enrichment tool in Moody’s RMS Cyber Solutions was used to identify missing and incorrect values and supplement the dataset with accurate, company-specific information. Using the application’s disaggregation engine on the reinsurance books, the company generated a modelable portfolio representative of both locations and coverages. By integrating the client’s available low-resolution exposure and policy data with the Moody’s RMS cyber industry exposure database, the company was able to conduct much more accurate exposure assessments.
By introducing Moody's RMS Cyber Solutions applications, the company was able to enhance its overall cyber exposure data – enabling better risk selection, ensuring risk-appropriate pricing, and improving overall portfolio composition.
Embedding high-resolution, accurate information into the underwriting framework enabled the company to improve underwriting profit and achieve a 20–25 percent reduction in gross loss costs across all return periods. In addition, transparent access to the back-end model parameters and assumptions allowed the company to validate its results and understand what factors were driving cyber losses.