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While the COVID outbreak is of immediate urgency, insurers also must make sure to be prepared to evaluate climate-related risks with robust, comprehensive risk management practices. It’s quite early for predictions, but according to reports, an above-average hurricane season just three short months from now is likely. Last year, the U.S. alone spent $45 billion on damages resulting from weather and climate-related disasters. While managing the COVID outbreak is of immediate urgency, insurers also must make sure to be prepared for climate-related events lurking on the horizon. This situation makes it even more critical for insurance organizations to implement robust, comprehensive risk management practices.

Agile Approaches to Risk Assessment

Insurers understand climate-transition risks and physical risks need to be managed. It is here that smart modeling techniques, such as agile catastrophe modeling, come into play to help insurers identify risk vectors and prepare a comprehensive risk management framework.

Sometimes, the use of a single, unadjusted catastrophe model may be too limiting to address an array of potential scenarios, and therefore a substantial operational risk remains. This is because every model has inherent vulnerability components. To overcome this risk, businesses can turn to catastrophe model blending, which combines the best parts of several appropriate component models.

Ideally, when estimating the impact of an individual risk, businesses can adopt multiple catastrophe models in order to draw numerous permutations of potential outcomes. Resulting overlays can compensate for the vulnerability components present in each model. The overlays can also offer a compounded view of the risk and provide an appropriate explanation of the differences between the expected outcomes of each model.

Feeding the Risk Transfer Value Chain

Predictive risk models are a crucial part of a comprehensive risk management framework. Such frameworks help identify the risk level and its equitable distribution among the concerned parties. Further, by using an effective risk transfer mechanism, the risk is allocated equitably to designated parties, based on an ability to control and insure against the individual risk. Insurers can choose to adopt either a traditional risk transfer mechanism, via a reinsurance policy, or opt for alternative risk transfer (ART) mechanisms.

ART is still in its infancy and is hindered by a lack of historical data, rigorous standards, and overall industry-standard refinements. This can lead to a possible scenario where companies under-protect or over-protect a given risk. While ART products offer bespoke coverage, customization leads to pitfalls similar to those associated with the traditional approach — a lack of trust in the quoted prices and the fairness of the contract.

Emerging risk transfer platforms, on the other hand, can bypass those setbacks, offering businesses a new way to transfer risk. The primary function of such platforms can be to serve as smart marketplaces that offer transparent rules and syndicated risk placement so that all parties’ prices are analyzed and compared, and terms are simultaneously met. The continued development and innovation of risk transfer platforms has the potential to act as the fuel to help propel the industry forward.

Enhanced Risk Assessment with Next Generation Technology

Data is the foundation of all risk models. Unsurprisingly, big data analytics is one of the key technologies that is foundational to the advancement of risk modeling techniques. Big data analytics can help synthesize mixed, multi-dimensional variables to provide new insights in real-time. The Internet of Things (IoT) is already providing an astonishing number of new data sources. Modelers are tapping into such new sources of data to expand their modeling outcomes.

Additionally, remote sensing and geographic information systems (GIS) have improved data‐capture capabilities on observable physical properties. GIS and remote sensing can determine the structural characteristics of a building at a higher resolution than existing information. And, improvements in ground-based radar and satellite data networks can enhance the ability of experts to observe and predict global weather. Since satellites provide information on vast geographic expanses, warning systems can become more effective through advances in forecasting.

By incorporating advanced technologies in risk management, the re/insurance industry can gain ready access to better quality data, as well as enhanced interpolation and extrapolation techniques. Catastrophe models can be developed to deepen the focus on the geospatial accuracy of exposures, which, in turn, can provide a more precise picture of risk. For the industry to manage and mitigate catastrophe losses, embracing next generation technology is an important enabler and a big contributor to refining the way ahead for risk evaluation.