The insurance industry is facing unprecedented challenges as climate change continues to reshape the risk landscape. In a recent episode of the Unstructured Unlocked podcast, Sam Worthington, Managing Director of PNC Technology at Aon, joined hosts Michelle Gouveia and Tom Wilde to explore how insurers are using data, analytics, and advanced technologies to navigate these new realities. Worthington shared valuable insights on how insurers can refine their approach to climate risk to protect policyholders and ensure long-term profitability.
Listen to the full podcast here: Predictive analytics and climate risk: Aon’s Sam Worthington on data-driven insights
Evolving risk: Climate change and its impact on insurance
Extreme weather events are becoming more frequent and severe, posing a significant challenge to insurers tasked with predicting and pricing risk. Worthington highlighted how traditional models that rely on historical data are no longer sufficient to address the evolving nature of these risks. “The nature of risk is changing, and historical experience is a guide but is not predictive of what could happen,” he explained.
Hurricanes, floods, wildfires, and severe hailstorms are just some of the perils that have led to large insured losses in recent years. However, Worthington emphasized that climate change is only one part of the equation. Increased urbanization and the growth of vulnerable infrastructure, like solar farms susceptible to hail damage, have also contributed to the rising number of claims. This combination of factors makes it difficult to isolate the impact of climate change on insured losses.
How technology is reshaping risk modeling
Technology plays a crucial role in helping insurers adapt to the changing risk environment. Worthington outlined how recent improvements in data analytics and machine learning are enhancing insurers’ ability to model and predict risks. “Advances in geospatial mapping and lidar [allow us] to identify buildings and their characteristics—that helps us a lot in terms of modeling,” he noted.
While technology has made significant strides in some areas, such as exposure mapping and financial modeling, challenges remain in accurately predicting the frequency and severity of catastrophic events. Worthington pointed to the uncertainty surrounding rare, volatile events like wildfires and floods, which are influenced by climate change and other factors.
Worthington also highlighted the emergence of startups leveraging machine learning to forecast losses with greater accuracy. “What we have seen in recent years is a growth in startups forecasting absolute losses for the industry and with a limited data sample—actually with some better accuracy than we’ve seen from the traditional… modeling, predicting [things like] ‘How many major hurricanes will we have in the us?’” he shared. These innovations are helping insurers refine their models and improve their understanding of risk.
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From reactive to proactive: the evolving role of insurers
Traditionally, insurers have focused on responding to claims after a loss has occurred. However, Worthington noted that this model is evolving, with insurers now adopting proactive risk management strategies. “We have seen insurers who are proactively taking steps to help their insureds with loss. So, for example, there’s a notification there’s going to be some flooding and the insurer sends a text message to their customers to say, ‘Move your cars,'” Worthington said on the podcast. This kind of immediate warning system exemplifies how insurers can leverage real-time data to prevent potential losses.
He also spoke about how technology is playing a vital role in improving risk management. “One of our clients uses sensors for modeling the temperature of sensitive cargo, and they take that and they’re able to identify [whether it has] moved beyond tolerance and whether there should be a loss or not,” he explained. These advancements are helping insurers streamline the claims process even further and reduce unnecessary payouts.
Rather than waiting for claims to be filed, insurers are now more involved in loss prevention and risk mitigation. By using predictive analytics and automated alerts, insurers can offer more timely interventions, which not only helps to minimize losses but also enhances customer trust and satisfaction.
The role of government and regulation
Government agencies and regulatory bodies play a vital role in supporting the insurance industry’s efforts to manage climate risks. Worthington described the partnership between insurers, data providers, and government agencies as a two-way street. “Governments provide scientific knowledge and data on weather events, and insurers [use] that… to improve hazard modeling,” he explained. In turn, insurers provide feedback on the types of data they need to refine their models and better predict risks.
Regulatory changes, such as updated building codes and flood defenses, also impact insurers’ risk assessments. Insurers must account for these changes in their models to accurately price risk and provide coverage that reflects the evolving risk landscape. Worthington highlighted how insurers play a role in rebuilding communities after disasters by advocating for more resilient construction practices.
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How AI is shaping the future of insurance
No discussion of technology in insurance would be complete without addressing the impact of artificial intelligence (AI). Worthington sees AI as a transformative force that is reshaping many aspects of the insurance industry. “I see AI helping with data ingestion, with claims processing, with the ability then for insurers to quote more quickly…” he said.
AI can reduce the burden on policyholders by automating data collection and analysis, allowing insurers to provide more accurate quotes with less input from customers. AI is also improving claims processing by identifying suspicious characteristics and speeding up the approval process for legitimate claims. Worthington noted that AI can enhance the industry’s overall stability by improving the prudential management of insurance companies.
Looking to the future of AI in underwriting, Worthington highlighted the growing importance of real-time data in reshaping insurance practices. “For something like climate, one theory I’ve got is that we will move away from these one-year policies to something that is… increasingly more short term, or more tailored to customers,” he said. This potential shift could make insurance more adaptive and reflective of current risk levels.
Empowering insurers to navigate climate challenges
As climate risks continue to evolve, insurers must adapt their strategies to remain effective and competitive. Worthington’s insights underscore the importance of leveraging technology and data to improve risk modeling, enhance customer service, and proactively manage losses. Partnerships with technology providers like Indico Data are essential in helping insurers achieve these goals.
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Frequently asked questions
- How do insurers quantify the financial impact of adopting technologies like geospatial mapping and AI on their overall profitability? Insurers quantify the financial impact of adopting technologies like geospatial mapping and AI by evaluating improvements in operational efficiency, risk accuracy, and customer satisfaction. For example, geospatial mapping helps insurers precisely identify property characteristics and vulnerabilities, reducing the margin of error in underwriting and claims assessment. These tools also save time by automating previously manual tasks, allowing underwriters and claims processors to focus on higher-value decisions.
- What specific steps are insurers taking to address the challenges of increased urbanization and vulnerable infrastructure in risk modeling? Insurers are adapting to the challenges of increased urbanization and vulnerable infrastructure by integrating more sophisticated risk assessment models and updating their coverage terms. For instance, they may adjust premiums to account for risks associated with urban development in flood-prone areas or hail-prone regions with solar farms. Additionally, insurers are encouraging policyholders to adopt mitigation measures, such as implementing better building materials or defenses, by offering discounts or incentives. They are also incorporating local environmental data and partnering with urban planners and governments to advocate for resilient infrastructure designs that reduce the exposure to such risks over time.
- What are the main obstacles preventing insurers from fully adopting real-time data and AI-driven decision-making across all lines of business? The obstacles preventing insurers from fully adopting real-time data and AI-driven decision-making include legacy systems, data silos, and the high upfront costs of implementation. Many insurers still rely on outdated systems that are not compatible with modern data-driven tools, making integration challenging. Additionally, unstructured data sources, such as handwritten claims or fragmented customer records, are difficult to incorporate into real-time analytics. Another major challenge is regulatory compliance; insurers must ensure that AI-driven decisions are transparent and fair to avoid potential legal risks.
