Step inside the new frontier of cyber underwriting—where elite teams are unlocking high-signal external data, filtering out the noise, and gaining an edge that few have mastered.
The underwriting crossroads: Challenges in the cyber landscape
The complexity of modern cyber risk
This article is for insurers—executives, underwriters, and decision-makers at carriers, MGAs, and MGUs—who are focused on strengthening their cyber books.
Here are their goals they share with us most often:
- Profitably grow cyber books
- Sustain long-term profitability in a changing market
- Refine underwriting and risk selection to meet new performance and capital expectations
And here are the common challenges standing in the way:
- Underwriting is too time-consuming or resource-intensive
- Cyber threats are increasing in frequency and sophistication
- Legacy methods fall short in forecasting exposure and loss potential
Today’s underwriters don’t need more data for data’s sake — they need sharper, context-rich signals with proven links to outcomes. The landscape has changed. Our latest research shows a strong correlation between external risk signals and claim frequency. The right mix of internal and external data isn’t just useful — it’s essential.
Underwriting’s challenge: The need for speed and precision
Insurers are all too familiar with the consequences of changing markets and growing corporate demands for faster turnaround times and more profitable book development. Every company is looking for their secret sauce to stay ahead, and having a top-tier underwriting approach can secure a competitive advantage for a carrier or MGA.
But in the race for profitability, underwriting success hinges on more than just speed—it requires precision.
Profitable underwriting – key assumptions:
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To achieve both speed and precision, underwriters need data they can trust—data that not only accelerates decisions but is correlated to claims outcomes. That’s exactly what recent research from Marsh McLennan set out to evaluate.
From signals to outcomes: Lessons from the Marsh McLennan Study
In 2024, The Marsh McLennan Cyber Risk Intelligence Center completed a univariate study evaluating CyberCube’s risk signals across approximately 3,700 cyber insurance policies from 3,200 companies. Of these, 5.37% experienced a claim between 2022 and 2023. The study found that several of CyberCube’s risk factors—developed to improve cyber risk differentiation and financial risk quantification—showed strong correlations with the likelihood of experiencing a data breach or ransomware incident.
You can read the full report here — Improve Loss Ratios with CyberCube's Predictive Analytics
Standout signals and score example
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Phishing Domains: One example of the most powerful insights from the Marsh McLennan study was the strong correlation between claims frequency and specific CyberCube risk signals, particularly Phishing Domains. This single risk signal showed a positive relationship with risk frequency, meaning the more phishing domains detected in a company’s ecosystem, the higher the likelihood of a claim. This makes it a leading indicator of potential cyber incidents, especially ransomware and data breaches. |
Phishing Domains
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Combined Score: Beyond individual signals, CyberCube’s Combined Score emerged as a key differentiator. Unlike traditional single-factor or static scoring models, the Combined Score integrates both security and exposure dimensions into a single, dynamic metric. This dual-lens approach enables a more accurate view of overall cyber risk, allowing underwriters to differentiate between superficially similar submissions and make better-informed decisions. It empowers underwriting teams to not just assess risk, but to rank and prioritize it. |
Combined Score
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Turning signals into strategy
The signals highlighted in the Marsh study are more than academic findings, they’re highly actionable tools for underwriting teams. By using curated, high-impact signals like Phishing Domains and the Combined Score, insurers can:
- Improve risk segmentation
- Accelerate submission triage
- Sharpen pricing decisions at the point of underwriting.
Most importantly, these insights allow underwriters to identify and manage high-risk exposures before they enter the portfolio. This proactive approach reduces claim frequency, supports portfolio diversification, and helps maintain profitability, which is especially critical as market dynamics and loss patterns continue to evolve.
With curated intelligence in hand, underwriters can move faster and more confidently, aligning each decision with both near-term performance goals and long-term sustainability.
Building a sustainable and profitable cyber insurance company
The benefits of predictive intelligence
Just in case the benefits of predictive intelligence have not hit home yet, here are a few more things to consider.
Signal-driven analytics:
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So, how can insurers embrace these forward-looking signals and reap the benefits?
The path forward: embracing data as a strategic asset
Here’s how leading carriers, MGAs, and MGUs can translate analytics into improved underwriting performance and competitive advantage:
Upskilling the underwriting function
Align tools and thresholds with enterprise risk appetite
Adopt CyberCube’s Account Manager, the industry’s most advanced cyber underwriting solution, that allows you to embed your underwriting strategy directly into the workflow. Set data-driven thresholds based on tail metrics, diversification goals, and capital efficiency. The right tooling ensures consistent decisions, faster triage, and better portfolio outcomes, all while keeping underwriters within the guardrails of your risk tolerance.
Train underwriters to interpret and apply signals and data
Equip underwriting teams with the tools, knowledge and confidence to understand what signals like Phishing Domains or the Combined Scores actually represent—and how to use them.
Driving market differentiation
Use analytics to stand out in a crowded market
Predictive signals allow you to underwrite smarter and faster. But they also enable a more tailored approach that includes refining pricing, terms, and risk appetite in ways that competitors can’t easily replicate. This positions your organization to be the partner of choice for brokers seeking speed and consistency.
Position your brand as a data-forward, trusted insurance provider
A strong data strategy isn’t just operational, it’s reputational. Carriers and MGAs that visibly invest in analytics and predictive modeling signal long-term stability to clients, brokers, and reinsurers. It’s not just about writing better risks—it’s about becoming a more resilient, future-ready market.
From strong signals to strategic advantage
Signal-driven data is no longer a luxury, it’s the foundation for profitable and sustainable cyber underwriting. As cyber threats grow more complex and the demand for faster, smarter underwriting intensifies, success depends on more than just speed. It requires clarity around risk fit and the precision to make confident, profitable decisions.
Underwriters need the ability to quickly determine which risks align with their appetite, cutting through the noise of raw data to focus only on what matters. With the right set of curated predictive signals, underwriting teams can improve triage, pricing, and selection—ultimately driving stronger portfolio performance and long-term advantage.
CyberCube’s Account Manager was built with this future in mind. It brings together high-impact analytics, underwriting workflows, and portfolio-aligned decision-making in one platform, empowering underwriters to act decisively and lead in a competitive market.
If you're ready to unlock smarter underwriting and stronger results, take a closer look at how Account Manager can support your goals.
