Floods, hurricanes and technical failures – all of these cause major destruction. The insurance industry relies on catastrophe modeling, using risk models that are either commercially available or home-grown, to estimate the potential losses resulting from catastrophic natural disasters. Probably due to climate change, catastrophic events are occurring more frequently. When cat models take hours, and even days, to deliver results, insurers find it more challenging to calculate risk during fast moving events, assess coverage liability, and underwrite policies in real-time. As a result they may lose business and become more vulnerable to financial losses.  In this post we’ll explore the intertwined relationship between cat models and optimizing data computations and elucidate how this synergy augments risk assessment and enables proactive mitigation strategies. 

Benefits of cat models include precision in risk assessment. By considering variables such as weather patterns (which are often affected by changing climate conditions), geographical location, building structures, and vulnerability of assets, insurers can offer granularity to specific regions or even individual policies. Catastrophe risk modeling also empowers insurers to optimize resources while allocating capital more efficiently, gauge risks with much greater accuracy by pinpointing areas that demand greater coverage. 

Cat modeling is data-driven 

Needless to say, cat models can only be as accurate as the data on which they rely and the processing power which enables highly complex risk calculations. By nature, catastrophic events are inherently unpredictable; simulation and modeling must account for the uncertainty of local conditions and random factors. The accuracy of the simulation and modeling directly hinges upon the quality, accuracy, and diversity of data that is incorporated into the computations. To provide the most accurate risk assessments upon which insurers, reinsurers and government bodies rely, cat models must have access to the highest quality, accurate and diverse data sets possible. 

Cat modeling requires massive compute power to perform resource-intensive computations. It fuses advanced data analytics, scientific insights and computational algorithms to evaluate the impact of calamitous events on insurance portfolios. The data sets should have minimal noise and inaccuracies to ensure the seamless aggregation, cleaning, and organization of these diverse data sources.  

For example, to provide a reliable representation of the risk from an event such as a tropical cyclone–induced flood, requires high spatial and temporal resolution simulations for potentially hundreds of thousands of seasons under specific climate conditions. Data is often collected in visual formats, from satellites and other sources. The diversity and heterogeneity of data sources pose technical challenges in unifying datasets for meaningful analysis. In parallel, cat modelers must ensure data governance, privacy, and security amid growing volumes of data. 

Enabling Real-Time Decisions 

By incorporating real-time data streams, catastrophe models can now enable insurers to monitor ongoing events and offer rapid responses. During events like wildfires or earthquakes, this rapid decision-making can save lives and minimize losses. Apart from these life and property-threatening events, insurers need to offer accurate risk assessment and evaluations on an ongoing basis. Slow computing capabilities and lack of real time data affect the business’ ability to provide accurate quotes. 

However, incorporating real time data to cat models raises certain technological challenges, due to the diversity and heterogeneity of data sources, which may be difficult to integrate and harmonize into datasets for meaningful analysis. Proper data management ensures the seamless aggregation, cleaning, and organization of diverse datasets, which comprise sources that include historical catastrophe records and real-time environmental data. Complex models that contain large data sets require significant computational resources, making it challenging to run simulations within reasonable time frames.  

Powering cat models with GigaSpaces technology 

To enable real time integration and fast computation, organizations can use GigaSpaces technology to decouple between the data and the applications, and then replicate the data into a low-latency, high-performance layer that provides ultra-fast read performance. Co-locating data and compute means that risk calculations can run in minutes rather than hours, greatly reducing compute costs. GigaSpaces enables organizations to optimize model efficiency with advanced processing techniques, and to incorporate advanced algorithms and cloud computing resources. This solution also provides distinct business benefits, by increasing the number of permutations and granularity of cat models. 

With the ability to run cat modeling algorithms very efficiently co-located where the hazard, vulnerability and financial module calculations, reinsurers can predict at much higher accuracy the ground up loss, gross and net loss expected after applying a policy structure to such catastrophic event or series of events simulation.

Expanding the usage of catastrophe modeling 

Cat modeling has been adopted beyond its traditional home in the insurance industry, where its primary use cases are risk assessment for catastrophic events. The financial sector has adopted this technology to help predict the impact of disasters on the financial markets, and to help manage investment risks and to hedge against catastrophic events. Government agencies, NGOs and private sources now use cat models to assist with economic planning for disaster response and recovery, for policy-making and regulation and to enhance infrastructure development and resilience. 

Last Words

As catastrophic events are taking place more frequently, perhaps the result of climate change, it is essential for (re)insurers to increase the coverage of their cat modeling simulations to the max. GigaSpaces technology enables risk assessment across wide regions and populations, enabling insurers and governments to handle growing demand, offering a broader scope and improved granularity. 

With GigaSpaces technologies, organizations can now execute cat modeling, feeding it with more financial, environmental and historic data, and run the simulation 10 times faster and obtain far more accurate results. Organizations can run algorithms very efficiently in-memory; co-located with the hazard, vulnerability and financial module calculations. Faster processing enables reinsurers to minimize their exposure in underwriting policies, and provide policy proposals, in a very short time frame. Instead of “I’ll get back to you next week,” an accurate policy can be ready within hours or even minutes, offering a distinct competitive advantage.