A Personal Account of Hurricane Matthew
Managing Catastrophic Events with Data Analytics and Modeling
During the night of Hurricane Matthew, I happen to read the Three Little Pigs to my three-year old daughter. We were at home winding down after a long and anxious day in south Florida. Emotions were high as the region was getting ready for its major hurricane in over a decade.
While my neighbors were frantically putting up shutters and some literally tying down roofs (see actual picture), I was spending family quality time with a peace of mind and confidence that we were safe from this hurricane. Throughout the day, I had been running models and scenarios to help my family make critical and real time decisions with respect to the time and expense associated with disaster preparedness, loss control and prevention. With an approaching Hurricane, each hour counted.
Hurricanes are serious and not to be taken lightly. We see the awesome power and destruction that major Hurricanes cause in coastal communities in both wind and flood damage to people, assets and company earnings. Today’s information overload, media sensationalism and general human anxiety creates a lot of noise and may cloud people’s judgement that may lead to emotional decisions and not decisions based on facts and science.
A hurricane can cause economic loss to commercial businesses and municipalities. Time is money for businesses and government. Every hour of business interruption represents millions of dollars, be it loss of sales for a retail company or loss of revenue for an airport or seaport. In addition, the hourly expense to prevent and mitigate losses such as storage or transportation also contributes to the overall economic loss.
With advances in risk analytics software, the insurance industry is seeing an intersection between science and technology that enables users to make analytics based decisions in real time. Crowdsourcing data and models from authoritative sources and independent scientists empowers users to make critical decisions that safeguards companies from catastrophic economic losses.
Companies that use analytics and models to identify, quantify and monitor forecasted losses not only can provide their management, customers and general public the confidence in protecting the company but can also gain a competitive advantage. The opportunity cost for those companies that overreact – extended closures and cessation of operations – creates an opportunity for those with better data to capture additional revenue during this period of uncertainty.
Given the unpredictable nature of catastrophic events, modeling what-if scenarios based on forecast and/or historical events, also provides decision maker with an additional critical data that further supports critical recommendations that a risk manager will need to make.
Further, if and when an event or near miss occur, using analytics in post event assessment will help companies expedite decisions in identifying potential impacted losses and damage, values at risk and estimated losses.
We were lucky in south Florida that Hurricane Matthew was not the big bad wolf of storms that the media had originally reported. Before and after catastrophic events, risk managers and brokers experience a lot of huffing and puffing from many areas of an organization. It is a stressful experience. Business continuity plans are reviewed and deployed, coverages are reviewed, claim and loss control resources are put on notice, reports need to be prepared and presented to executives, etc.
The use of risk analytics and modeling provides risk managers and brokers powerful information that helps preserve a company or municipality reputation and creates the confidence in managing catastrophic events by the hair of a chinny chin chin.
Eduardo Hernandez is a co-founder and Business Development leader for EigenRisk, a risk analytics company serving the (re)insurance, intermediary and risk management community. He has 20 years of risk and insurance management experience in underwriting, placement, product development and sales.
