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Risk analytics: How to enhance value from property and cyber risk and insurance

By John Merkovsky , Andy Smyth and Megan Schlosser | December 19, 2023

Quantifying your risks more effectively can help you optimize risk and insurance decisions. We look at the analytical techniques capable of delivering better value on your property and cyber risk.
Corporate Risk Tools and Technology|Cyber Risk Management|Risk and Analytics|Risk Management Consulting

How can you use analytics to make better decisions that enhance financial performance? A recent webinar in WTW’s Outsmarting Uncertainty series provided some answers. WTW experts examined how you can apply analytical techniques to clarify the efficiency of your risk and insurance strategy. They then looked at how to improve value from two specific risk lines using analytics: property and cyber.

Property and cyber risk are both important and costly risks for companies. They can also prove complicated to model. However, if you want to transfer or retain any risk efficiently, you need to model it first and analytics lets you do this at speed and scale. This insight summarises the expert perspectives from the Outsmarting Uncertainty webinar on how.

How to model property risk to optimize risk and insurance

We would argue traditional models of property risk have often been driven by the concerns of insurers, focusing on single perils and single regions to better fit with their approach to risk quantification (for example, California earthquake or Florida wind). We’d also suggest this approach is limited in its ability to facilitate strategic decision making around buying insurance.

To enable a more strategic and efficient approaches to your property insurance, you’ll need to understand the risk to all property locations from all perils. Only when you get a full picture by modelling your property exposures across the whole portfolio and then optimize insurance decisions.

Using analytics, you can input a list of your locations, their addresses, their construction and occupancy types to generate a rapid understanding of your total risk to earthquake, wind, flood, other natural catastrophe and non-cat perils.

You can then look at loss metrics, including frequency of loss, annual average loss, and various loss percentiles to see the full range of loss outcomes on your property exposure. You can then use this insight to evaluate different insurance options, proactively testing how different variations deliver value or otherwise.

For example, you could use analytics to compare your current property risk and insurance program to one with a higher deductible and one with a lower natural catastrophe sublimit. You might then experiment with comprehensive cost of risk calculations, considering the components that make up your total cost of risk – premium, retained losses, and cost of volatility – to assess the value from insurance before choosing the optimal insurance program that best fits your portfolio.

Ultimately, analytically driven property risk quantification is about giving you the transparency you need to reveal the optimal strategy for your organization. It’s also about having your own property risk quantitative insights that enable you to challenge insurer assumptions on your risk and the appropriate way to price it.

The value of scenario-based modeling for cyber risk

Cyber risk is a rapidly changing exposure. Traditional approaches, such as using past loss experiences to predict future losses are not reliable in the cyber risk space; by the time there is enough data and awareness on a type of threat, businesses have responded and criminals have moved on to the next area of weakness they can exploit.

Also, as your organization may never have experienced a cyber loss, the challenge is how to respond when, perhaps, a finance colleague or the chief information security officer (CISO) asks you to demonstrate the value of your preferred approach to cyber insurance.

This is where scenario-based approaches can bridge the gap. Scenario approaches to cyber risk allow you to adapt quickly to the changing cyber environment by combining the latest industry data, cyber expert intelligence and actuarial insight to build a robust library of scenarios from which your organization can select and apply those most relevant to its sector and operations.

Once you’ve selected the scenarios for your company, you can quantify the financial impact down to a granular level, generating transparency on the cyber risk your organization faces and the role of insurance in its risk management strategy. Cyber threats are fast-moving, potentially catastrophic and give rise to insurance premiums that may seems to cost more and more each year, while the value it provides remains unknown. When you’re able to secure a robust and detailed understanding of your cyber risk, you can both unlock the ability to test and optimize your insurance purchasing and unpack the ‘black box’ of cyber risk.

Analytics can help you move beyond the limitations of backward-looking views and a lack of loss history when defending the rationale behind your cyber insurance approach, and we’re seeing first-hand how scenario-led are resonating, even with skeptical stakeholders such some CISOs. When you talk to them about specific risk scenarios and how these could interact with your company’s specific vulnerabilities, you speak their language, connecting your risk management approach to organizational resilience and enduring success.

For smarter ways to quantify your portfolio of risks, get in touch with our risk and analytics experts.


Head of Risk & Analytics and Global Large Account Strategy, WTW

Head of Strategic Risk Consulting

Associate Director, Risk, WTW
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