Tag Archives: hurricane katrina

In a Crisis, Will You Be Ready?

S___ happens!

Fifty years ago, Rock, David and I were at Pelican Aviation’s hangar listening to several seasoned pilots talk about their most terrifying experiences in the air. One said, “The engine made a loud noise, the plane shook violently and suddenly I couldn’t see a thing.” One of us innocently asked, “What happened – did the windshield shatter?” His answer was simple, “No, tears.”

Having spent much of my adult life in insurance, I’ve seen many disasters. The question is: Are we ready?

Hurricane Katrina was a terrible event for Mississippi. In New Orleans, there was minimal wind damage, but there were levee failures and accompanying social/civil chaos.

There was also a little-noticed success story: LSU’s medical school relocated from New Orleans (blocks from the chaos) to Baton Rouge in about a week. This required some luck, community (BR and NO) support and, I believe, some divine intervention, but it was an example of leadership at its best.

What if you had to relocate your office, all your team and everyone’s families following a catastrophe? Have you even considered the possibility?

See also: 4 Lessons From Harvey and Irma  

Here’s reality – many if not most of us will face great challenges. Some may parallel experiences we’ve seen before, just with greater or lesser intensity. There will be more fires, hurricanes and floods. Terrorists will attack us again. Planes will crash. We can’t stop all the bad in the future – the best we can do is try to avoid or at least mitigate the damage.

Most of us watched the successful rescue of 12 young soccer players and their coach from a flooded cave in Thailand. Relative to 9/11 or Hurricane Katrina, it is a minor event, but I believe it will prove to be one of the best case studies anywhere of what to do when the stakes are high, time is limited and you don’t know what to do.

Remember, these folks were lost for about 10 days before anyone even knew where they were. The last few days of their stay were examples of calm, leadership, courage, planning, possibilities and then very deliberate action.

Every Seal, volunteer, civilian, etc. should be celebrated for their effort, courage and patience – living and learning as they progressed. They didn’t rush in, reacting to a terrible situation. They walked, crawled, and swam in, well-prepared and observing appropriate caution. In construction, we’d say: Measure twice, cut once.

Never forget that one of the rescue team died early in the process. Was this loss the impetus to do things differently? I don’t know. I do believe our greatest learning occurs in adversity – it is the wisdom of scar tissue! The death was tragic but may have slowed the process and improved results.

I encourage each of us to consider the disasters that could be on our horizon. Begin a crisis management process with your families and your organizations. You won’t have all the answers, and you don’t yet know all the questions – nonetheless, be as prepared as you can and program for the unexpected. Plan your actions and act your plan.

See also: Innovation — or Just Innovative Thinking?  

A speaker once said at an agents meeting, “the merchant of misery is either at your door, just left or will soon arrive.” The best thing you can to is to be as prepared as possible and hope and pray you are blessed with the courage, skills, patience, process and RESULTS that these Thai crisis managers enjoyed.

Be prepared. Practice your preparations. Preparedness is a process not a one-time event. If in the end all of your preparation is not needed, BE THANKFUL. If it is needed, you may thank me for the suggestion. Good luck and Godspeed.

How to Avoid Failed Catastrophe Models

Since commercial catastrophe models were introduced in the 1980s, they have become an integral part of the global (re)insurance industry. Underwriters depend on them to price risk, management uses them to set business strategies and rating agencies and regulators consider them in their analyses. Yet new scientific discoveries and claims insights regularly reshape our view of risk, and a customized model that is fit-for-purpose one day might quickly become obsolete if it is not updated for changing business practices and advances in our understanding of natural and man-made events in a timely manner.

Despite the sophisticated nature of each new generation of models, new events sometimes expose previously hidden attributes of a particular peril or region. In 2005, Hurricane Katrina caused economic and insured losses in New Orleans far greater than expected because models did not consider the possibility of the city’s levees failing. In 2011, the existence of a previously unknown fault beneath Christchurch and the fact the city sits on an alluvial plain of damp soil created unexpected liquefaction in the New Zealand earthquake. And in 2012, Superstorm Sandy exposed the vulnerability of underground garages and electrical infrastructure in New York City to storm surge, a secondary peril in wind models that did not consider the placement of these risks in pre-Sandy event sets.

Such surprises affect the bottom lines of (re)insurers, who price risk largely based on the losses and volatility suggested by the thousands of simulated events analyzed by a model. However, there is a silver lining for (re)insurers. These events advance modeling capabilities by improving our understanding of the peril’s physics and damage potential. Users can then often incorporate such advances themselves, along with new technologies and best practices for model management, to keep their company’s view of risk current – even if the vendor has not yet released its own updated version – and validate enterprise risk management decisions to important stakeholders.

See also: Catastrophe Models Allow Breakthroughs  

When creating a resilient internal modeling strategy, (re)insurers must weigh cost, data security, ease of use and dependability. Complementing a core commercial model with in-house data and analytics and standard formulas from regulators, and reconciling any material differences in hazard assumptions or modeled losses, can help companies of all sizes manage resources. Additionally, the work protects sensitive information, allows access to the latest technology and support networks and mitigates the impact of a crisis to vital assets – all while developing a unique risk profile.

To the extent resources allow, (re)insurers should analyze several macro- and micro-level considerations when evaluating the merits of a given platform. On the macro level, unless a company’s underwriting and claims data dominated the vendor’s development methodology, customization is almost always desirable, especially at the bottom of the loss curve where there is more claim data; if a large insurer with robust exposure and claims data is heavily involved in the vendor’s product development, the model’s vulnerability assumptions and loss payout and developments patterns will likely mirror that of the company itself, so less customization is necessary. Either way, users should validate modeled losses against historical claims from both their own company and industry perspectives, taking care to adjust for inflation, exposure changes or non-modeled perils, to confirm the reasonability of return periods in portfolio and industry occurrence and aggregate exceedance-probability curves. Without this important step, insurers may find their modeled loss curves differ materially from observed historical results, as illustrated below.

A micro-level review of model assumptions and shortcomings can further narrow the odds of a “shock” loss. As such, it is critical to precisely identify risks’ physical locations and characteristics, as loss estimates may vary widely within a short distance – especially for flood, where elevation is an important factor. When a model’s geocoding engine or a national address database cannot assign location, there are several disaggregation methodologies available, but each produces different loss estimates. European companies will need to be particularly careful regarding data quality and integrity as the new General Data Protection Regulation, which may mean less specific location data is collected, takes effect.

Equally as important as location is a risk’s physical characteristics, as a model will estimate a range of possibilities without this information. If the assumption regarding year of construction, for example, differs materially from the insurer’s actual distribution, modeled losses for risks with unknown construction years may be under- or overestimated. The exhibit below illustrates the difference between an insurer’s actual data and a model’s assumed year of construction distribution based on regional census data in Portugal. In this case, the model assumes an older distribution than the actual data shows, so losses on risks with unknown construction years may be overstated.

There is also no database of agreed property, contents or business interruption valuations, so if a model’s assumed valuations are under- or overstated, the damage function may be inflated or diminished to balance to historical industry losses.

See also: How to Vastly Improve Catastrophe Modeling  

Finally, companies must also adjust “off-the-shelf” models for missing components. Examples include overlooked exposures like a detached garage; new underwriting guidelines, policy wordings or regulations; or the treatment of sub-perils, such as a tsunami resulting from an earthquake. Loss adjustment difficulties are also not always adequately addressed in models. Loss leakage – such as when adjusters cannot separate covered wind loss from excluded storm surge loss – can inflate results, and complex events can drive higher labor and material costs or unusual delays. Users must also consider the cascading impact of failed risk mitigation measures, such as the malfunction of cooling generators in the Fukushima nuclear power plant after the Tohoku earthquake.

If an insurer performs regular, macro-level analyses of its model, validating estimated losses against historical experience and new views of risk, while also supplementing missing or inadequate micro-level components appropriately, it can construct a more resilient modeling strategy that minimizes the possibility of model failure and maximizes opportunities for profitable growth.

The views expressed herein are solely those of the author and do not reflect the views of Guy Carpenter & Company, LLC, its officers, managers, or employees.

You can find the article originally published on Brink.

Hurricane Harvey: A Moment of Truth

The first major hurricane to make landfall in the U.S. since hurricanes Dennis, Katrina, Rita and Wilma in 2005, Hurricane Harvey will cause billions of dollars in economic damage and disrupt countless lives. In the wake of massive economic losses and untold human suffering, including loss of life, millions of individuals and businesses will turn to their insurers for help. This will be a make-or-break experience, a real moment of truth.

Insurers will be presented with a golden opportunity to justify the public’s trust and earn the respect of policyholders, regulators, legislators and others in government. But insurers also run the risk of failing to live up to expectations and incurring the wrath of voters and their elected representatives.

See also: Flood Risk: Question Is Where, Not When  

The first test may well be distinguishing damage caused by wind from damage caused by flooding, as virtually all insurance policies exclude losses due to flooding (the exception being those policies issued by the National Flood Insurance Program). Insurers will need to be careful, thorough and fair when settling claims.

Equally important, insurers will need to be perceived as having been so, and communication will be key. Insurers would be well advised to do what they can to make policyholders feel they have been treated with respect, dignity and compassion even when their claims must be denied or settled for some amount less than the claimant sought.

Moreover, insurers would be well advised to settle claims as quickly as possible without unduly sacrificing sound loss adjustment and efforts to weed out fraud and abuse.

Finally, with the media sure to draw attention to heartbreaking stories about human tragedy in Harvey’s aftermath, insurers might benefit from doing what they can to shine a light on their efforts to help individuals and businesses recover. Surely it is worth noting that, as others evacuate, insurers gear up to send large numbers of claim adjusters to work in extremely difficult conditions in hard-hit areas.

Hurricane Harvey will also lead to many other moments of truth. For example, the devastation caused by Harvey may well prove to be the first real test at extreme scale of new insurtech created to improve loss adjustment. Will use of drones, aerial imagery, artificial intelligence, digitalization, big data, predictive analytics and the like prove as beneficial as hoped? Will insurtech entrepreneurs and insurers who have invested in these technologies be vindicated? And, on a more positive note, will experience coping with Harvey reveal new opportunities to use emerging technologies to increase speed, efficiency and fairness?

Insured losses from Hurricane Harvey may also test reinsurance mechanisms, including catastrophe bonds, other insurance-linked securities and sidecars. And what about so-called hedge fund reinsurers, which sought to profit by investing insurance float using strategies like those typically employed by hedge funds? Will they continue to participate as claims mount, or will they instead seek to exit the business? Some past catastrophes triggered significant inflows of fresh capital, as investors sensed opportunities to profit from a turn in reinsurance markets. Such was the case following Katrina, Rita and Wilma in 2005. Will the “fast money” come rushing in again, and, if it does, will it prove to also be “smart money”?

All of the above raises the question, “Will Hurricane Harvey lead to a reset of catastrophe models, pricing for hurricane risk and underwriting?” Some past storms, such as Hurricane Andrew in 1992, convinced insurers that they had previously underestimated hurricane risk and thus led to dramatic resets in coastal property insurance markets, with attendant price increases and availability problems. Whether Harvey brings about such a reset seemingly depends on whether current catastrophe models did an adequate job alerting insurers to the risk of an event like Hurricane Harvey. If so, changes in coastal property insurance markets may be muted. If not, expect price increases and availability problems.

See also: Is Flood Map Due for a Big Data Make-Over?  

Last, and let’s hope least, Hurricane Harvey may test insurers’ enterprise risk management. Prior to Harvey, the property/casualty industry had ample surplus, and most insurers were well capitalized. But surplus was not evenly distributed across insurers, and only the surplus of those insurers that wrote policies covering properties struck by Harvey is available to cover claims from Harvey.

If an insurer only wrote risks in Oregon, its surplus won’t be called upon to cover claims from Harvey. Bottom line, insurers that covered properties affected by Harvey, that were aware of potential losses and that have ample financial resources to cover claims and continue operations can give themselves good grades for enterprise risk management.

On the other hand, Insurers that covered properties affected by Harvey, that were surprised by their losses and that lack the resources to cover claims must give themselves failing grades for enterprise risk management.

And then there is a gray area: insurers that intelligently judged the risk of insolvency to be acceptably small, took a calculated risk and then lost that bet. Though such insurers will fail, it cannot be said that their enterprise risk management failed. Eliminating even the most remote chance of insolvency is not practical. Neither is it economically viable. Sound enterprise risk management consists of: 1) understanding risks; 2) making conscious, intelligent decisions about which risks to take, which risks to avoid, which risks to mitigate and which risks to transfer; and, 3) enforcing controls that keep operations within the bounds established by an enterprise’s appetite for risk.

6 Reasons We Aren’t Prepared for Disasters

When dawn broke on the morning of Sept. 8, 1900, the people of Galveston, Texas, had no inkling of the disaster that was about to befall them. The thickening clouds and rising surf hinted that a storm was on the way, but few were worried. The local Weather Bureau office, for its part, gave no reason to worry; no urgent warnings were issued, and no calls were made to evacuate. But by late afternoon it became clear that this was no ordinary storm. Hurricane-force winds of more than 100 mph were soon raking the city, driving a massive storm surge that devoured almost everything in its path. Many tried to flee, but it was too late. By the next day, more than 8,000 people were dead, the greatest loss of life from a natural disaster in U.S. history.

Fast-forward to September 2008 when Hurricane Ike threatened the same part of the Texas coast — but this time it was greeted by a well-informed populace. Ike had been under constant surveillance by satellites, aircraft reconnaissance and land-based radar for more than a week, with the media blasting a nonstop cacophony of reports and warnings, urging those in coastal areas to leave. The city of Galveston was also well-prepared: A 17-foot-high seawall that had been constructed after the 1900 storm stood ready to protect the city, and government-flood insurance policies were available to residents who were at risk of property loss. Unlike in 1900, Texas residents really should have had little reason to fear. On their side was a century of advances in meteorology, engineering and economics designed to ensure that Ike would, indeed, pass as a forgettable summer storm.

See also: 5 Techniques for Managing a Disaster  

It didn’t quite work out that way. Warnings were issued, but many in low-lying coastal communities ignored them — even when told that failing to heed the warnings meant they faced death. Galveston’s aging seawall turned out to be vulnerable; it was breached in multiple places, damaging roughly 80% of the homes and businesses in the city. The resort communities to the north on the Bolivar Peninsula, which never saw the need for a seawall, fared even worse, witnessing almost complete destruction. And among the thousands of homeowners who suffered flood losses, only 39% had seen fit to purchase flood insurance. In the end, Ike caused more than $14 billion in property damage and 100 deaths — almost all of it needless.

Why are we underprepared for disasters?

The gap between protective technology and protective action illustrated by the losses in Hurricane Ike is, of course, hardly limited to Galveston or to hurricanes. While our ability to foresee and protect against natural catastrophes has increased dramatically over the course of the past century, it has done little to reduce material losses from such events.

Rather than seeing decreases in damage and fatalities because of the aid of science, we’ve instead seen the worldwide economic cost and impact on people’s lives as hazards increased exponentially through the early 21st century, with five of the 10 costliest natural disasters in history with respect to property damage occurring since 2005. While scientific and technological advances have allowed deaths to decrease on average, horrific calamities still occur, as in the case of the 230,000 people estimated to have lost their lives in the 2004 Indian Ocean earthquake and tsunami; the 87,000 who died in the 2008 Sichuan earthquake in China; the 160,000 who lost their lives in Haiti from an earthquake in 2010; and the 8,000 fatalities that occurred in the 2015 Nepalese earthquake. Even in the U.S., Hurricane Katrina in 2005 caused more than 1,800 fatalities, making it the third-most deadly such storm in U.S. history.

In our book “The Ostrich Paradox,” we explore six reasons that individuals, communities and institutions often under-invest in protection against low-probability, high-consequence events. They are:

  1. Myopia: a tendency to focus on overly short future time horizons when appraising immediate costs and the potential benefits of protective investments;
  2. Amnesia: a tendency to forget too quickly the lessons of past disasters;
  3. Optimism: a tendency to underestimate the likelihood that losses will occur from future hazards;
  4. Inertia: a tendency to maintain the status quo or adopt a default option when there is uncertainty about the potential benefits of investing in alternative protective measures;
  5. Simplification: a tendency to selectively attend to only a subset of the relevant factors to consider when making choices involving risk; and
  6. Herding: a tendency to base choices on the observed actions of others.

See also: Are You Ready for the Next Disaster?

We need to recognize that, when making decisions, our biases are part of our cognitive DNA. While we may not be able to alter our cognitive wiring, we may be able to improve preparedness by recognizing these specific biases and designing strategies that anticipate them.

Adapted from The Ostrich Paradox: Why We Underprepare for Disasters, by Robert Meyer and Howard Kunreuther, copyright 2017. Reprinted by permission of Wharton Digital Press.

Are You on Your Game, or Is Your Game Over?

Weeks ago, Jim and I met for coffee to solve all the world’s problems. We didn’t, but he did hand me an article about Sudoku and said, “There may be a story in here.” He was right. I just didn’t realize how quickly it would appear on my computer screen.

Later that day, when I was driving down Main Street in New Iberia, La., I saw mobs of “geeks” (the politically correct term is “millennials”) playing Pokémon Go. My wife works Sudoku puzzles. I had read about Pokémon Go, but I have never even seen it played before.

See also: Pokémon Go Highlights Disruptive Technology  

I was impressed with the marketplace’s embrace of Pokémon Go. One hundred million devotees in less than a year is a game-changer.

If, like Sudoku, your business is manual, local- and pencil- and paper-dependent, your universe is limited to yesterday. If you are global and virtual like Pokémon Go, there are no boundaries — only opportunities. Your future depends on the choices you make, local or global, manual or virtual.

Now let’s quit playing around and get serious about the insurance industry and your place, if any, in tomorrow’s world.

Whether you prefer the metaphor of revolution or evolution, our world is changing. The change is going to be structural, revolutionary and transformational. The reason is that when one thing is different it’s change; when everything is different it’s chaos.

In terms of natural disasters, think 9/11, Hurricane Katrina and New Orleans, the Japanese tsunami, etc. For economic crises, consider the 2008 economic collapse, the stock market crash, the GM bailout, the demise of AIG, Lehman Brothers, etc. — and then remember the past and current reshuffling of the retail and distribution systems in our world (Amazon, Uber, Airbnb, Expedia, WebMD, Netflix, etc.) I could go on, but I won’t. I can’t remember all the changes, nor can I outrun the pace of change.

These changes from yesterday were triggered by systems, big data, technology, global competition and corruption, the internet and a marketplace that has evolved over time — from the corner store, to Main Street, to strip shopping centers, to malls, to box stores and even to a virtual presence in cyberspace. The big change now and tomorrow is not place but rather people and pace.

See also: Look Up, Look Out, Think New!

Our industry was built for a “father knows best” world. The youngest of the Greatest Generation are now 70 years old. Their progeny are the Boomers, who are 52 and older. Those in Gen X are age 32 and above, and the Gen Y and millennials are somewhere between 12 and 34.

In tomorrow’s market, age doesn’t matter — wiring does. Every preceding generation was born to analog; these Gen Yers/millennials are digital natives. What we “old” folks see as aberrations, they see as the norm — and they and the market ain’t going back ever again. By 2025 (which is nine years away) millennials will be 75% of the working people. The next nine years may bring more technological advances than we’ve seen in our collective lifetimes.

Our options are simple: We can go enjoy a smoke and a Sudoku on the bank of the nearest tar pit and wait for a meteor to end our pain and frustration, or we can shift into high gear and catch up with the roaming hordes of Pokémon Go folks and play in — and with — the world as it’s going to be!

THE MARKET IS CHANGING BECAUSE BUYERS CHANGE!

Change or die! Carpe mañana!