Guidewire Cyence Risk Analytics

Measuring the financial impact of cyber risk for the insurance industry through data science and economic modeling

Guidewire Cyence™ Risk Analytics is a cloud-native economic cyber risk modeling solution built to help the insurance industry quantify cyber risk exposures. Leaders across the insurance industry use Cyence Risk Analytics to prospect, underwrite, and price risks. It enables insurers to manage portfolio exposure accumulations and develop new products with confidence.

Cyber risk is evolving. This presents unique challenges for the insurance industry. Without the benefit of substantial loss history to build traditional actuarial pricing models, insurers have to rely on subjective information from the insured—like high-level questionnaires and brief discussions about what security technologies and protocols are in place—to manage cyber underwriting and accumulation.

Unlike traditional catastrophe risks, cyber has no authoritative data source on which to rely. Cyber risk accumulations are stealthy, and they change often. Companies that might otherwise be completely unrelated may share common internet infrastructure and risks: a cloud outage or zero-day vulnerability can cause correlated losses to companies on opposite sides of the globe.

Unlike most P&C insurance lines, cyber risk involves active adversaries who deliberately seek high-value and opportunistic targets. Cyber risk can be modeled using game theory and behavioral economic frameworks. Our models measure company and portfolio risk by examining company posture and comparing it to bad actor motivations and capabilities.

In short, cyber risks call for better tools for underwriting, pricing, and management. These tools need to account for the shifting threat landscape and measure cyber catastrophe exposures in terms of dollars and probabilities.

How We Do It
 

Through a process called data listening, we collect vast amounts of technical and behavioral data from a variety of sources at internet scale, including public data, open-source data, proprietary data, and third-party data. We curate the data and apply sophisticated machine-learning techniques to find the signal through all the noise.

The result is a comprehensive and unparalleled economic cyber risk modeling solution that adjusts as the cyber landscape shifts, continuously gathering data and updating economic models based on changing circumstances. Our risk models include a dynamic cost benefit analysis to keep up with bad actors as they choose targets, and with companies as they shift their mitigation strategies.

Our team dedicates deep talent from the cyber risk, data science, and insurance domains to provide a state-of-the-art economic modeling solution and expert-led operational support. This combination gives insurance clients deep insights into their business and enables growth through data-driven product development, underwriting, pricing, and risk management strategies.

Individual and Accumulated Risk Selection
 

Cyence can be used to assess the risk level of a potential insured at an individual level, but the solution is vital for insurers that need a comprehensive view of their aggregate portfolio exposure. This includes the ability to measure the likelihood and financial impact of a comprehensive set of customizable scenarios.

Cyence examines the correlation of cyber risk within a portfolio and the potential losses that disaster scenarios can have on that portfolio. Understanding the shared attributes and correlation of risk enables realistic, fact-based measures of probable maximum loss. This detailed and continually updated understanding of risk accumulation is crucial for an insurer managing the long-term stability and soundness of their portfolio.

Cyence Risk Analytics Benefits
 

  • Improved risk selection
    Augments underwriting information with 40+ additional risk factors based on externally collected data to better enable underwriter validation and targeted inquiry
  • Advanced risk assessment and stress testing
    Enables insurers to monitor portfolio health through EP curves, a scenario library, and scenario customization
  • Improved growth opportunities
    Delivers the insight needed to design new insurance products and go-to-market strategies