Property reinsurance rates are at their highest in 17 years. Factors such as shifting weather patterns, record losses and economic uncertainty have damaged reinsurer confidence, leading to steep mid-year rate increases. To unlock future growth and earn reinsurer trust, insurers must: leverage innovative technology to manage evolving risks and adopt a philosophy of radical transparency. Those that don’t will be left behind.
Innovation: An Urgent Opportunity
Reinsurers want to have full confidence in the risks they underwrite. However, with increased losses and rising replacement costs, they have no choice but to be more selective. To bolster confidence, primary carriers must prove their ability to adapt amid a challenging risk climate. The first step? Master new technologies quickly.
The rapid advancement of technology, such as recent developments in generative artificial intelligence (AI), is transforming every industry, including insurance. A 2022 report from Accenture found that 65% of claims executives plan to spend more than $10 million on AI, and 80% believe these technologies offer value. Munich Re’s Patrick Greene says in a recent Reinsurance News article that reinsurers hold this view. Greene emphasized the importance for insurers to integrate AI immediately for efficient claims and underwriting processes. He also stressed that these technologies will soon not be optional for carriers – at least, not if they want to obtain reinsurance.
Still, insurance tends to trail other industries in embracing new technology. While the cost of many AI solutions has decreased and the number of insurers reporting financial benefits from AI has increased from 10% to 20%, the same Accenture report found that fewer than half of those polled said their organization was advanced in automation.
Amid this technological shift, insurers can distinguish themselves from their more conservative competitors. By enthusiastically adopting AI, machine learning (ML) and computer vision (CV), carriers can prove to reinsurers they are forward-thinking and adaptable. Many insurers are already doing exactly this to:
- Accelerate claims: For instance, Allstate uses a conversational AI bot, Amelia, to speed up claims. As of last year, Amelia was handling 250,000 conversations each month and was used by 75% of Allstate call-center employees.
- Optimize inspections: Virtual inspection tools from companies such as JMI Reports and Plnar enable insurers to massively reduce total inspection budgets and the time it takes to perform an inspection.
- Automate underwriting: AI-powered platforms can automate underwriting, rapidly identify property condition and provide straight-through processing for low-risk policies. Munich Re’s Lee Sarkin said in an interview that these systems enhance underwriter efficiency without replacing them.
These examples highlight how AI can boost efficiency and reduce expenses, which is crucial for carriers partnering with reinsurers. But perhaps even more compelling is the predictive power of AI – its ability to anticipate and even avoid future claims.
The Predictive Power of AI
Catastrophic and severe weather events, coupled with a 50% increase in catastrophe rates at July renewals, underline the urgency for reinsurers to address increased losses. The problem isn’t just natural disasters, either. Secondary perils like convective storms have also become a significant loss driver, accounting for 68% of all catastrophic loss dollars in the first half of 2023, Swiss Re reported. In response, some major carriers have stopped writing business in high-risk states such as California and Florida. This is not a long-term solution, though, and certainly does not endear these companies to their policyholders.
The better path forward is to harness the predictive power of AI. AI models can identify properties vulnerable to damage and even estimate potential damage, proving paramount in rebuilding reinsurer trust. Most impressive of all, AI-powered risk insights show what steps can be taken to reduce or avoid losses entirely. At a time when government weather models are viewed as increasingly outdated, insurers need to prioritize investing in predictive AI.
The ideal AI models should analyze both regional hazard data and property-level vulnerability:
- Hazard describes the likelihood that a specific region will experience a catastrophic or severe weather event. This information is largely based on historical losses but can also be determined by increasingly sophisticated catastrophic (CAT) models powered by the latest advances in supercomputing.
- Vulnerability describes the likelihood that a property will be damaged during an event. It can also quantify the amount of damage the property will sustain. Based on risk factors such as roof condition and defensible space, the most accurate vulnerability data is based on CV detections applied to high-quality imagery.
Carriers can and must use predictive AI to mitigate losses, proving to reinsurers that they are a safe investment. Imagine a carrier providing coverage in a coastal region prone to floods and hurricanes. Because roof staining, roof material and tree overhang are strongly correlated with hurricane losses, carriers can flag properties exhibiting those factors while using straight-through processing on ones less vulnerable to damage. Carriers can then contact the policyholders in advance of an event to notify them of their risk level.
If a policyholder simply repairs their roof or trims some vegetation, they could significantly reduce their vulnerability, potentially avoiding future losses. In another example, an insurer could use CV-powered CAT models to monitor and predict the path of a wildfire, notifying policyholders in real time whether they are at risk. When AI is used as a predictive tool, everyone wins. Insurers reduce the possibility of a major loss, policyholders attain a competitive premium and – most relevant for this article – reinsurers trust the risks they are underwriting.
See also: Insurtech: Not Dead but Different
The Imperative of Transparency
Yet, while embracing digital transformation, particularly the predictive capabilities of AI, is the best way for insurers to regain confidence from reinsurers, that is not enough on its own. If they really want to succeed, carriers must marry innovation with a philosophy of radical transparency.
Reinsurers value transparency. Carriers must not only claim AI use but also transparently show its application, avoiding "black box" technologies. In other words, reinsurers need AI to be explainable. Can insurers show confidence scores and accuracy levels for the AI models they use? Can they pinpoint the exact property attributes that contributed to an overall risk score? Reinsurers want to know if their primaries have a sophisticated and explainable system in place for managing risk. The more they can look at the nuts and bolts of this system, the greater their trust.
Embracing innovative technology and prioritizing transparency is key for insurers to foster stronger ties with reinsurers. Moreover, this dual focus doesn't only benefit business-to-business relations; it ripples out to instill greater trust in policyholders. In essence, confidence in an insurer's process naturally boosts faith across the entire insurance ecosystem.