Perceptions of Risk
Decisions we make today will shape our future. It is our experience that guides us. It is our ability to recognize the influences on each decision we make that allows us to make better decisions as we mature. Or so we hope.
The difficulty with decision-making is that our environment and influences are ever changing. Expectations are always changing. As such, leadership is difficult. Leaders must be confident enough to make decisions as they humbly seek wisdom and historical perspective from those with alternative views, experiences and understanding. As we might say, those who remember the past are less condemned to repeat it.
As a risk manager, I rely on experiential data. As I conduct strategic risk assessments, I purposely look for known threats or symptoms that may defeat a proposed decision or path. I look for flawed strategies that have caused failure in similar situations in the past. That approach is part of my craft. But is it reliable?
The insurance industry has plenty of historical failures to study. Unfortunately, we can only discuss failures that have already occurred. We cannot foresee the failures yet to play out.
The mortgage industry, specifically the subprime mortgage industry in 2006 and 2007, is an excellent example of how mimicking the success of many subprime lenders of years past looked like a fruitful and profitable venture. Many banks adopted the strategy. And why shouldn’t they have? Everyone was doing it. Risk? What risk?
But clearly the measure of risk in that situation was distorted. Huge risk existed, but at the time it was perceived that the risk of not entering the market was far greater than the risk of any financial disaster. Clearly, this turned out to be a disastrous gamble.
In the years following the meltdown, the entire banking system froze. This reaction was almost equally catastrophic. No one lent anything to anyone, at any rate, for any time, for any reason. This response was a disaster in of itself.
Except for a courageous and wise few, the opportunity to profit was missed. Decisions were made in a dangerously risk-averse environment fueled by some leaders’ loss of faith in the banking system. Those risk-averse leaders failed to seize a tremendous opportunity to make money.
During this time, I observed two environments: An environment of excessive confidence, and one of deep naiveté coupled with excessive fear and paralysis. It was a dramatic spread.
Leaders who kept their perspective and passed up the easy money maintained the ability to capitalize on tremendous opportunities after the crash. Those who stayed their course, were true to their values and recognized the distortions in the markets were able to lead their organizations through the disaster and come out stronger than their peers.
These leaders, we must cherish and embrace. The leaders who were paralyzed by fear are still immobilized or likely now unemployed.
But we shouldn’t be too hard on those leaders who only now may understand their failures.
Any point in time never seems historic when you are living through it. As we enter or exit the next realm of decision-making, I hope we are able to recognize the distortions and the opportunities, and sidestep the decisions that may cause us peril.
Coping with Cancellations
Airlines typically can offset revenue losses for cancellations due to bad weather either by saving on fuel and salary costs or rerouting passengers on other flights, but this year’s revenue losses from the worst winter storm season in years might be too much for traditional measures.
At least one broker said the time may be right for airlines to consider crafting custom insurance programs to account for such devastating seasons.
For a good part of the country, including many parts of the Southeast, snow and ice storms have wreaked havoc on flight cancellations, with a mid-February storm being the worst of all. On Feb. 13, a snowstorm from Virginia to Maine caused airlines to scrub 7,561 U.S. flights, more than the 7,400 cancelled flights due to Hurricane Sandy, according to MasFlight, industry data tracker based in Bethesda, Md.
Roughly 100,000 flights have been canceled since Dec. 1, MasFlight said.
Just United, alone, the world’s second-largest airline, reported that it had cancelled 22,500 flights in January and February, 2014, according to Bloomberg. The airline’s completed regional flights was 87.1 percent, which was “an extraordinarily low level,” and almost 9 percentage points below its mainline operations, it reported.
And another potentially heavy snowfall was forecast for last weekend, from California to New England.
The sheer amount of cancellations this winter are likely straining airlines’ bottom lines, said Katie Connell, a spokeswoman for Airlines for America, a trade group for major U.S. airline companies.
“The airline industry’s fixed costs are high, therefore the majority of operating costs will still be incurred by airlines, even for canceled flights,” Connell wrote in an email. “If a flight is canceled due to weather, the only significant cost that the airline avoids is fuel; otherwise, it must still pay ownership costs for aircraft and ground equipment, maintenance costs and overhead and most crew costs. Extended storms and other sources of irregular operations are clear reminders of the industry’s operational and financial vulnerability to factors outside its control.”
Bob Mann, an independent airline analyst and consultant who is principal of R.W. Mann & Co. Inc. in Port Washington, N.Y., said that two-thirds of costs — fuel and labor — are short-term variable costs, but that fixed charges are “unfortunately incurred.” Airlines just typically absorb those costs.
“I am not aware of any airline that has considered taking out business interruption insurance for weather-related disruptions; it is simply a part of the business,” Mann said.
Chuck Cederroth, managing director at Aon Risk Solutions’ aviation practice, said carriers would probably not want to insure airlines against cancellations because airlines have control over whether a flight will be canceled, particularly if they don’t want to risk being fined up to $27,500 for each passenger by the Federal Aviation Administration when passengers are stuck on a tarmac for hours.
“How could an insurance product work when the insured is the one who controls the trigger?” Cederroth asked. “I think it would be a product that insurance companies would probably have a hard time providing.”
But Brad Meinhardt, U.S. aviation practice leader, for Arthur J. Gallagher & Co., said now may be the best time for airlines — and insurance carriers — to think about crafting a specialized insurance program to cover fluke years like this one.
“I would be stunned if this subject hasn’t made its way up into the C-suites of major and mid-sized airlines,” Meinhardt said. “When these events happen, people tend to look over their shoulder and ask if there is a solution for such events.”
Airlines often hedge losses from unknown variables such as varying fuel costs or interest rate fluctuations using derivatives, but those tools may not be enough for severe winters such as this year’s, he said. While products like business interruption insurance may not be used for airlines, they could look at weather-related insurance products that have very specific triggers.
For example, airlines could designate a period of time for such a “tough winter policy,” say from the period of November to March, in which they can manage cancellations due to 10 days of heavy snowfall, Meinhardt said. That amount could be designated their retention in such a policy, and anything in excess of the designated snowfall days could be a defined benefit that a carrier could pay if the policy is triggered. Possibly, the trigger would be inches of snowfall. “Custom solutions are the idea,” he said.
“Airlines are not likely buying any of these types of products now, but I think there’s probably some thinking along those lines right now as many might have to take losses as write-downs on their quarterly earnings and hope this doesn’t happen again,” he said. “There probably needs to be one airline making a trailblazing action on an insurance or derivative product — something that gets people talking about how to hedge against those losses in the future.”
6 Truths about Predictive Analytics
Predictive data analytics is coming out of the shadows to change the course of claims management.
But along with the real benefits of this new technology comes a lot of hype and misinformation.
A new approach, ACE 4D, provides the tools and expertise to capture, analyze and leverage both structured and unstructured claims data. The former is what the industry is used to – the traditional line-item views of claims as they progress. The latter, comprises the vital information that does not fit neatly into the rows and columns of a traditional spreadsheet or database, such as claim adjuster notes.
ACE’s recently published whitepaper, “ACE 4D: Power of Predictive Analytics” provides an in-depth perspective on how to leverage predictive analytics to improve claims outcomes.
Below are 6 key insights that are highlighted in the paper:
1) Why is predictive analytics important to claims management?
Because it finds relationships in data that achieve a more complete picture of a claim, guiding better decisions around its management.
The typical workers’ compensation claim involves an enormous volume of disparate data that accumulates as the claim progresses. Making sense of it all for decision-making purposes can be extremely challenging, given the sheer complexity of the data that includes incident descriptions, doctor visits, medications, personal information, medical records, etc.
Predictive analytics alters this paradigm, offering the means to distill and assess all the aforementioned claims information. Such analytical tools can, for instance, identify previously unrecognized potential claims severity and the relevant contributing factors. Having this information in hand early in the claims process, a claims professional can take deliberate actions to more effectively manage the claim and potentially reduce or mitigate the claim exposures.
2) Unstructured data is vital
The industry has long relied on structured data to make business decisions. But, unstructured data like claim adjuster notes can be an equally important source of claims intelligence. The difficulty in the past has been the preparation and analysis of this fast-growing source of information.
Often buried within a claim adjuster’s notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs. Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports, legal notifications, and conversations with the employer and claimant. This unstructured data, for example, may indicate that a claimant continually comments about a high level of pain.
With ACE 4D, the model determines the relationship between the number of times the word appears and the likely severity of the claim. Similarly, the notes may disclose a claimant’s diabetic condition (or other health-related issue), unknown at the time of the claim filing but voluntarily disclosed by the claimant in conversation with the adjuster. These insights are vital to evolving management strategies and improving a claim’s outcome.
3) Insights come from careful analysis
Predictive analytics will help identify claim characteristics that drive exposure. These characteristics coupled with claims handling experience create the opportunity to change the course of a claim.
To test the efficacy of the actions implemented, a before-after impact assessment serves as a measurement tool. Otherwise, how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects?
Say certain claim management interventions are proposed to reduce the duration of a particular claim. One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience. In other words, how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place?
An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug, but instead of the two approaches running at the same time, the placebo group is based on historical experience.
4) Making data actionable
Information is everything in business. But, unless it is given to applicable decision-makers on a timely basis for purposeful actions, information becomes stale and of little utility. Even worse, it may direct bad decisions.
For claims data to have value as actionable information, it must be accessible to prompt dialogue among those involved in the claims process. Although a model may capture reams of structured and unstructured data, these intricate data sets must be distilled into a comprehensible collection of usable information.
To simplify client understanding, ACE 4D produces a model score illustrating the relative severity of a claim, a percentage chance of a claim breaching a certain financial threshold or retention level depending on the model and program. The tool then documents the top factors feeding into these scores.
5) Balancing action with metrics
The capacity to mine, process, and analyze both structured and unstructured data together enhances the predictability of a model. But, there is risk in not carefully weighing the value and import of each type of data. Overdependence on text, for instance, or undervaluing such structured information as the type of injury or the claimant’s age, can result in inferior deductions.
A major modeling pitfall is measurement as an afterthought. Frequently this is caused by a rush to implement the model, which results in a failure to record relevant data concerning the actions that were taken over time to affect outcomes.
For modeling to be effective, actions must be translated into metrics and then monitored to ensure their consistent application. Prior to implementing the model, insurers need to establish clear processes and metrics as part of planning. Otherwise, they are flying blind, hoping their deliberate actions achieve the desired outcomes.
6) The bottom line
While the science of data analytics continues to improve, predictive modeling is not a replacement for experience. Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models, and base their actions on this guidance and their seasoned knowledge.
The reason is – like people – predictive models cannot know everything. There will always be nuances, subtle shifts in direction, or data that has not been captured in the model requiring careful consideration and judgment. People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions.
Please download the whitepaper, “ACE 4D: Power of Predictive Analytics” to learn more about how predictive analytics can help you reduce costs and increase efficiencies.
This article was produced by the R&I Brand Studio, a unit of the advertising department of Risk & Insurance, in collaboration with ACE Group. The editorial staff of Risk & Insurance had no role in its preparation.