Cyber risk is pervasive. While bolstering a business’s cybersecurity can help reduce the risk, the possibility of an attack or incident is never zero, especially in today’s digital era. Everything is connected, and cyber threat actors are able to deploy more sophisticated cyber attacks than ever. The answer to this ongoing dilemma is cyber insurance.
Cyber insurance allows organizations to protect themselves from the fallout of a cyber attack. As businesses look to purchase a cyber insurance policy, they will often reach out to cyber brokers.
Insurance carriers write cyber insurance policies based on an organization's exposure and level of risk. As enterprises look to protect themselves from cyber risk, technology that helps effectively quantify their risk is vital to understanding its financial impact.
Cyber risk quantification (CRQ) is the process of measuring cyber risk exposure and its potential financial impact. Cyber risk is a growing risk for all industries, and enterprises must be prepared to face any potential threats. The cyber insurance industry needs to understand the cyber risk exposure of businesses, which is where cyber risk quantification is critical.
CRQ has become an invaluable tool for organizations, markets and society as a whole. For those looking to drive sustainable growth, having the appropriate risk quantification solution is imperative and can involve specific types of analytics and modeling.
Because cyber risk affects so many industries, cyber risk quantification plays a pivotal role in shaping the cyber insurance landscape. Without these analytics tools, understanding financial exposure would be difficult.
Because of the various facets of cyber risk, it’s especially important that insurance executives understand cyber insurance risk analytics and how they can be leveraged to maximize profitability. However, it can be difficult to properly implement CRQ tools across an organization.
Many will argue that the main challenge of cyber risk quantification is that cyber risk is a nascent line. While that’s true when compared to the property and casualty industry as a whole, cyber modeling has come a long way in recent years — and with the appropriate tools, (re)insurers can better understand risk.
However, it’s still important to recognize that cyber risks evolve quickly in comparison to traditional risks like natural disasters, and cyber threat actors are able to leverage new technologies like Artificial Intelligence (AI) to make cyber risk attacks even more complex.
The complexity and interconnectedness of cyber risks can cause cascading effects on many organizations, making it a challenge to fully assess the impact of a cyber attack when it occurs. Risk modeling can help with this, providing more insights about the potential impact of attacks, as well as what businesses can do to mitigate their risk.
Another challenging aspect of CRQ is the lack of standardized definitions and classifications across organizations and industries. For example, one enterprise’s reporting framework may vary from another’s, making it difficult to quantify their specific cyber risk.
Cyber risk quantification should be customizable to a specific view of risk, enabling users to obtain the insights necessary for their particular needs. Experts from varying backgrounds including cyber security, insurance, actuarial science, technology and AI, and modeling will create a holistic cyber risk quantification solution.
Cyber risk is so pervasive and inevitable that effective cyber risk quantification must be able to serve different purposes. For example, it is possible to quantify risk in each segment of the insurance value chain depending on the segment’s and user’s needs. Depending on the different datasets and methodologies a cyber risk solution leverages, underwriters can make more informed risk choices, brokers can obtain insights for advising their clients, and portfolio managers can assess their portfolio's risk level.
Cyber risk quantification involves utilizing relevant cyber data and methodologies. Quantification tools require a range of specific datasets to more accurately and reliably quantify cyber risk.
With cyber as a line only recently maturing, there is less historical data to base risk exposure on, so it is crucial to leverage data in the most effective way. It’s important, then, that quantification tools leverage many types of data, including:
These datasets provide context and inform risk quantification tools so users can glean as much insight as possible.
The methodology used to quantify cyber risk is also essential for attaining relevant and useful results. Cyber risk quantification solutions must be as transparent as possible with their methodology, so users can fully understand how the data is utilized to generate the appropriate insights.
Methodologies involved in cyber risk quantification can vary. They include probabilistic modeling, scenario analysis and statistical analysis. Various cyber risk quantification tools will deploy each kind of modeling depending on the outcome the user is looking for.
Probabilistic modeling uses historical data and mathematical algorithms to estimate the likelihood and severity of cyber events. However, because models cannot rely solely on past data and current external security data, they must also incorporate forward-looking expert-driven parameters to view latent and developing risks and trends. Methodologies like scenario analysis can simulate detailed, pressure tested cyber attack narratives and evaluate their potential impact on insured systems. Machine learning and AI techniques can also enhance cyber risk quantification when implemented correctly, identifying patterns that can anticipate future cyber threats.
A multidisciplinary team with expertise in areas such as cyber insurance, actuarial risk, and cybersecurity is vital for the continuous monitoring and updating of risk models. A regular review of datasets and methodologies can ensure that the insights offered by risk quantification tools are up-to-date and relevant for purposes across the insurance value chain.
Cyber risk insurance involves almost every segment across the insurance value chain, and the relevant cyber risk analytics solutions can help quantify that risk.
Cyber brokers play a pivotal role in navigating the ever-evolving landscape of the cyber insurance market — they must be effective cyber risk advisors in order to support clients and drive profitability. To achieve this, brokers must leverage insurance sales tools that incorporate the.
However, cyber brokers may overlook utilizing analytics, or may only have access to limited tools compared to other P&C brokers. The insurance industry often sees cyber as a more elusive line, and many tools available to brokers don’t have an overview of the industry, as it has been considered less measurable or predictable than other P&C lines. This has created a cyber knowledge gap in the market. Not only are many organizations not covered in the case of a cyber attack, but cyber brokers are missing out on opportunities to help their clients. The right cyber risk quantification tools can help solve these problems.
Cyber risk modeling for brokers should allow cyber brokers to identify and manage risk, which can mean providing better advice to clients on their buying decisions. It should also answer pressing questions, such as — how much risk do they have, what coverages and limits do clients need, and how much risk are their peers transferring? This ensures that they can use knowledge to build trust with clients, accelerating engagements and driving growth.
Cyber brokers must understand the cyber threat landscape in order to advise clients. Reviewing a client’s cyber insurance readiness and understanding how they can improve their cybersecurity will help them mitigate their risk.
Communicating cyber risk holistically is also essential for cyber brokers who want to go above and beyond for clients. Moreover, gaining insights is only one aspect of client advisory — cyber brokers also need to think about how they deliver insights for their clients to act on. Clear reporting tools embedded within a cyber broking quantification tool can streamline this communication with clients.
Cyber risk underwriting has come a long way in the last few years, and as the market continues to change, so do the underwriting challenges in insurance, especially cyber. Underwriters must adapt and tackle these challenges head on.
One of the biggest challenges when underwriting cyber risk is understanding what data to use. Cyber underwriting requires cyber risk data that is not only centered around the cyber posture of an organization, but that is also fit for insurance purposes. This requires turning raw cyber risk data into usable signals — enabling insurers to gain a comprehensive understanding of the potential cyber risk an organization is exposed to so they can price policies accordingly.
Cyber risk quantification solutions made for underwriters are crucial for teams to help make informed decisions that drive growth in the long term. While helping to streamline workflows, these solutions also provide the financial metrics underwriters need to make profitable, data-driven risk selection and pricing decisions.
Single risk underwriting is only one aspect of the insurance value chain — systemic risk also poses a threat. It's important that underwriters consider how their risk choices will fit into an overarching portfolio and how accumulation risk may impact their business. This is where cyber risk modeling comes in.
Cyber risk modeling is essential to the (re)insurance industry in order to identify and assess the risk contribution of various cyber perils. Portfolio managers require cyber risk quantification in the form of a cyber modeling framework that provides a comprehensive view of cyber risk. Understanding the cyber threat landscape is one component of this, but a cyber modeling framework should also provide transparency into the modeling process and enable the ability to tailor current views of risk.
In a shifting cyber risk landscape, it’s important to quantify the tail risk, which could result from potential cyber catastrophe events. Effective CRQ can provide (re)insurers with the insights they need to develop their own view of risk, understand their tail risk, and inform their cyber risk transfer decisions. The most advanced tools that understand cyber risk will use a combination of methodologies, as previously mentioned — ensuring they are fully probabilistic, scenario-based, and data-driven.
Catastrophe modeling is a particularly important aspect of cyber risk management, especially because there has not been a large-scale cyber catastrophe event yet. This kind of attack could potentially impact many organizations and markets via shared technologies or vendors common in separate digital supply chains.
Using a relevant CRQ tool can help (re)insurers quantify the cyber risk of a potential catastrophe. The various components of a cyber scenario should be understood and incorporated into cyber risk quantification methods, looking at how it could potentially impact many markets.
The interconnectedness of the world and the increased reliance on technology means that there is a rise in cyber risk accumulation events. Cyber (re)insurers need to be aware of these events, especially if there is a Single Point of Failure (SPoF) within their insureds’ digital supply chains. A SPoF is a technology or vendor in use by multiple other organizations, so if it gets targeted by a cyber threat actor, all these connected entities can be impacted too.
The past few years have shown the impact of SPoF attacks, such as SolarWinds, Colonial Pipeline and the Change Healthcare cyber attack, demonstrating how essential CRQ is for these kinds of risk. SPoF outages like the CrowdStrike outage can also occur with no malicious intent, Insurers can get ahead of this cyber aggregation risk with a solution that focuses on modeling a SPoF, identifying exposures and helping insurers to make improved portfolio management decisions.
The capital markets are playing a larger role in the insurance industry, offering vital capacity as cyber risk continues to become a greater problem across industries. These kinds of risks require capital markets to provide alternative insurance-linked instruments in the form of Insurance Linked Securities (ILS) — a type of financial instrument used to transfer cyber risk from insurers to the capital markets. They include cyber catastrophe bonds and collateralized reinsurance that cover losses from cyber events. By spreading risk across a wider investor base, they help insurers manage exposure to large-scale cyber incidents, alleviating the catastrophic financial impact.
ILS has only recently made waves within cyber, as investors begin to understand the diversification potential between cyber catastrophe bonds. Diversification within cyber as an asset class is still possible despite systemic cyber events not accumulating across easily visualized fault lines.
Cyber risk quantification is crucial for these kinds of bonds to provide investors with confidence in the transaction’s performance and transparency in the quantification methodology — the appropriate analytics will cover a broad spectrum of cyber risk, providing an expert, holistic view.
Holistic cyber risk quantification is essential for the cyber insurance industry to thrive — and while (re)insurers are embracing opportunities within the CRQ space, it’s not enough to use basic platforms with limited data and analytics. They also need to consider whether they are using the most appropriate tools that can keep up with advancements in cyber attacks leveraging AI and other tactics.
By embracing advanced risk quantification models, (re)insurers can access more comprehensive insights into cyber risk and gain a competitive advantage within a shifting cyber risk landscape. Many cyber risk quantification models are now much more sophisticated
A cyber risk analytics solution should focus on innovation, efficiency, transparency into data and methodology, as well as turning data into actionable insights that are fit for purpose. The right cyber risk quantification software is key to maximizing profitability, so (re)insurers must choose the right partner.