Embracing geospatial intelligence to navigate evolving insurance risks

Navigating the complexities of data has become a pivotal challenge for insurers in today’s landscape. As losses increase and risks shift, so does the demand for effective risk management solutions. From fluctuating climate conditions to evolving consumer preferences, insurers face a multitude of factors that shape their risk assessment strategies and operational decisions. Every risk carries a spatial component, and every policy exists within a geographic context, making location intelligence essential in today’s industry.  

Climate change has intensified the frequency and severity of extreme weather events, such as floods and wildfires. Measuring and managing these risks is crucial for insurers. However, it’s no easy task. To build a true picture of risk, insurers need reliable, detailed, scientifically robust data – and lots of it. However, teams often lack the context to size risk accurately. This data is often difficult to access and time-consuming to analyze, requiring in-house GIS specialists and a huge amount of computing resources. As the number of data inputs, use, and complexity grows, risk assessment tools become more complex to manage. Not to mention, regular updates to hundreds of spatial datasets often require manual intervention, which can lead to performance lags. This growing complexity can lead to inefficiencies and increased operational costs, restricting insurers’ ability to respond swiftly to emerging risks.

Likewise, insurers must be as informed as possible during disaster events to allocate resources effectively. Tracking storms or wildfires and their potential impact on policyholders is crucial for timely and efficient claims processing. However, this requires real-time data and advanced analytics to ensure the right number of claims adjusters and resources are deployed to affected areas.

Insurers must also contend with evolving consumer preferences for coverage options. Today’s consumers need more personalized insurance options that cater to their specific needs and circumstances. This shift requires insurers to diversify their policy options while protecting policyholders.

Despite these challenges, there is a clear trend towards leveraging advanced analytics. According to a report from McKinsey & Company, insurers across regions are investing significantly in advanced analytics but have unlocked only a fraction of the potential value. With the average insured losses resulting from natural catastrophes reaching more than $110 billion, analyzing, understanding, and accurately predicting catastrophe risk and exposure of insured assets is fundamental for longer-term profitability. As hazards are set to intensify and become more frequent in the coming decades, expanding the spatial capabilities of your cloud data warehouse through a Location Intelligence tool can provide the necessary context and access to relevant hazard-related datasets and a more modern, scalable approach to catastrophe analytics.

According to a recent survey, spatial data science is now used to solve complex business problems. The insurance industry is using spatial data regularly to conduct catastrophe modeling, fraud analytics, portfolio risk analysis, and pricing model development with near real-time precision and accuracy.

There are many benefits to embracing geospatial data. Location intelligence, for example, can help insurers reduce their exposure to risk while preparing and offering policyholders better service and more varied plans that meet their needs and conduct more accurate and nuanced risk assessments. For instance, overlaying crime data with insurance policies helps insurers understand localized risks of theft and vandalism, thereby adjusting underwriting guidelines and pricing models accordingly.

By leveraging location intelligence and geospatial data, insurers can adjust underwriting guidelines, pricing strategies, and risk management plans accordingly. For example, properties in high-risk wildfire zones during peak wildfire seasons can be offered incentives for proactive wildfire mitigation measures, such as defensible space maintenance and fire-resistant constructions. This not only helps reduce potential losses but also promotes safer communities.

Spatial analytics can also play a crucial role in detecting and preventing fraud. For example, if multiple claims are filed from the same location within a short time frame, this could signal fraud. Advanced geospatial tools can flag these patterns for further investigation, helping insurers safeguard their resources and maintain the integrity of their operations.

Beyond disaster response, geospatial data can help insurers optimize their overall resource allocation. Insurers can strategically deploy resources such as risk engineers and loss prevention specialists to mitigate potential losses by identifying areas with higher risk concentrations. This reduces the likelihood of large-scale claims and supports long-term profitability.

As the insurance industry evolves, staying ahead of technological advancements is crucial. Location intelligence and geospatial data are critical components of this evolution, offering the tools and insights needed to navigate emerging risks and opportunities. By continuously integrating the latest geospatial data and analytics into their operations, insurers can future-proof their business, ensuring they remain competitive and capable of meeting the changing needs of their policyholders.