Embracing Geospatial Intelligence Evolving Insurance Risks

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

Climate change has increased the frequency and severity of extreme weather events such as floods and wildfires. Measuring and managing these risks is crucial for insurers. However, it is not an easy task. To build a true picture of risk, insurers need reliable, detailed, scientifically sound data – and lots of it. However, teams often lack the context to accurately size risk. 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 amount of data input, usage and complexity increases, risk assessment tools become more complex to manage. Not to mention that regular updates to hundreds of spatial datasets often require manual intervention, which can lead to performance delays. This increasing complexity can lead to inefficiencies and increased operational costs, limiting insurers’ ability to respond quickly to emerging risks.

Insurers also need to be as informed as possible during disaster events in order to allocate resources effectively. Tracking storms or fires and their potential impact on policyholders is crucial to timely and efficient claims processing. However, this requires real-time data and advanced analytics to ensure the right number of damage 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 meet their specific needs and circumstances. This change 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 & Companyinsurers in the regions are investing significantly in advanced analytics, but have only unlocked a fraction of the potential value. With average insured losses resulting from natural catastrophes reaching over $110 billion, analyzing, understanding and accurately estimating catastrophe risk and insured asset exposure is critical to long-term profitability. As hazards are set to intensify and become more common over the coming decades, extending the spatial capabilities of your cloud data warehouse with a Location Intelligence tool can provide the necessary context and access to relevant hazard data sets and a more modern and scalable approach to catastrophe analysis.

According to one recent surveyspatial data science is now being used to solve complex business problems. The insurance industry regularly uses spatial data to perform catastrophe modeling, fraud analysis, portfolio risk analysis, and pricing model development with near-real-time precision and accuracy.

There are many benefits to adopting geospatial data. Location information, for example, can help insurers reduce their risk exposure, while preparing and offering policyholders better service and more varied plans to meet their needs and making more accurate and nuanced risk assessments . For example, 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 information and geospatial data, insurers can adjust underwriting guidelines, pricing strategies and risk management plans accordingly. For example, properties in high fire risk areas during the peak wildfire season may be given incentives for proactive wildfire mitigation measures, such as maintaining safe space and fire-resistant construction. This not only helps reduce potential losses, but also promotes safer communities.

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

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

As the insurance industry evolves, it is essential to stay ahead of technological advancements. Location information and geospatial data are critical components of this evolution, providing 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 able to meet the changing needs of their policyholders.