Hurricane season in the North Atlantic arrived early this year, giving business owners near the coast a sobering reminder to take natural catastrophes seriously and plan ahead. Despite predictions of a quiet season, even the best prepared businesses can be vulnerable. It only takes one hurricane making landfall to cause significant property damage and leave behind a swath of destruction.

Catastrophe (CAT) models, which use algorithms to estimate potential losses stemming from a catastrophic event, have become key components of property insurance underwriting and are critical tools in quantifying potential losses from hurricane damages. Yet each hurricane season puts CAT models to the test, with potentially billions of dollars riding on the accuracy of these models. The key to developing effective CAT modeled loss estimates is accurate, high-quality data.

High-quality data leads to improved loss estimates and more effective insurance coverage. Incomplete or inaccurate data in CAT models can lead to greater uncertainty, potential higher loss estimates, increased insurance premiums and lower risk capacity.

As hurricane season is underway, now is the time to evaluate your CAT modeling data to ensure it is accurate and of the highest-quality.

Conduct a Data Quality Review
Consider these potential benefits for businesses who conduct a comprehensive review of CAT data quality:

• Reduce uncertainty caused by inaccuracies in the original data.
• Improved loss estimates.
• Better inform insurance underwriters with a better quantification and qualification of risk.
• Potential reduced premiums, better guidance as to deductibles and policy limits, and help in securing more effective insurance coverage.

Focus on Data Quality
Analytics and CAT models are only as valuable as the data organizations enter into them. CAT models are sensitive to the uncertainty poor or missing data can create, so bad information can result in sub-optimal results.

Conversely, better data can result in significant premium savings. Improving the quality of the data in your CAT model will enable you to produce better quantification and qualification of the risk being considered by underwriters. To put it simply, your business will be better prepared to anticipate and weather the storm.

Evolving CAT Modeling Techniques
It wasn’t too long ago that organizations did not have these analytical capabilities in their risk management toolkit. CAT models have evolved significantly in the last two decades, both in methodological rigor and rate of adoption. In 1992, Hurricane Andrew exposed serious shortcomings in traditional actuarial methods and revealed that the commonly used risk models were outdated and ineffective. Following 2005’s devastating Hurricane Katrina, technology improvements and the greater availability of data have been instrumental in building CAT models that are much more complex, robust, and accurate.

Today, models are updated after each significant storm to account for new data and a growing body of knowledge. Organizations can fine-tune their specific CAT loss estimates with each model update, aligning risk data with risk tolerance and insurance choices. Rigorous, data-centric models are now widely in use across the industry.

Using sophisticated analytics, organizations can now tap into vast stores of aggregated claim and loss data to gain a clearer understanding of the potential costs and overall financial impact of catastrophe damages. This includes a data-driven, improved forecast of estimated losses each hurricane season.

Companies in hurricane-prone areas have routinely used their own individual loss histories to estimate potential losses from hurricane damages, but now companies can access an industrywide look at loss data, spanning other organizations within their same geographic area and expanding to regions with similar climates across the globe. Organizations today can use data-driven analytics to provide a statistically credible view into expected losses each hurricane season.

Despite the official forecast of a relatively quiet hurricane season, insurers, risk managers and organizations must resist the temptation of complacency in their preparations. The time is now to review CAT models and ensure the use of accurate, high-quality data. Improved data quality in CAT models can lead to a better understanding of risk and help mitigate losses this hurricane season.

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