The average cost of damage caused by a hurricane in the U.S. is USD 21.6 Billion. Hurricane Katrina alone caused damages to the tune of USD 160 Billion. Coastal insurance has now become essential given that 134+ million people are expected to be living along the U.S. coastline by 2020.
Coastal insurance is an additional coverage that people staying on the coasts need to take over and above their standard home and flood insurance. Coastal home insurance could include wind deductibles, named storm deductibles and hurricane deductibles.
For insurance companies, the biggest challenge in coastal insurance is in underwriting. The problem is in assessing the risk of natural calamities accurately to underwrite profitable policies. Even a relatively weak hurricane like Sandy costs insurers billions of dollars. With coastal exposures expected to increase at the rate of approximately 7 percent every year, there’s an urgent need to re-assess existing risk models. The new risk models must factor in historical data, current risks, climate data and location-specific risks among others.
Analytics and big data can play a significant role in coastal risk assessment. There’s no dearth of data as a result of connected devices, satellites, flood maps and other sources. However, tapping into all the available data and ensuring their veracity is a challenge for insurers. Big data and analytical tools can help glean the right insights to help underwriters re-distribute risks effectively. Additionally, image analytics can help underwriters assess the insurability of a property before a calamity and speed up the claims process.
With natural calamities on the rise globally, insurers are in a predicament. They have to increase the premiums or exit the market. Analytics can help them walk the fine line and balance their act. Coastal insurance is crucial for customers in times of distress and could be a viable option for insurance companies in the long term.