A Watchful Eye on Tianjin Losses
As I write this, little is yet known about the total loss impact, both locally and globally, of the recent explosions in the port of Tianjin, China. Current estimates as reported in news media are that losses could exceed $1.5 billion.
Around the globe, risk managers are keeping a watchful eye on the escalating impact of this event. Potential losses continue to unfold within companies, and insurers are already being put on notice accordingly.
Given the nature of this event, it is likely that analyses of coverage and the various loss impacts will be vastly complex.
Some of the key areas to monitor include:
Supply chain / contingent business interruption impact
Damages to products and materials flowing through and stored at the port of Tianjin will likely impact companies throughout multiple tiers of the supply chain.
Similar to supply chain impacts following the earthquake/tsunami in Japan and flooding in Thailand, it could take some time for the full impact of losses from Tianjin to be realized by many companies.
This creates a critical need for risk professionals to communicate early and often with their organizations’ supply chain management and sales departments to identify potential business interruption, contingent business interruption and extra expense losses.
Marine cargo, stock throughput, and other property coverage
The myriad of different coverages maintained by most companies can create a complex mix, requiring careful analysis to determine how each applies to the situation in Tianjin.
Because the event encompassed direct destruction of product and materials, contamination due to toxic chemicals and quarantine of vessels at the port, unraveling the related coverage issues may be a daunting and lengthy task, particularly given the limited flow of information concerning the explosion and its impact.
It is essential that risk professionals work closely with their advisors to understand application of their coverage.
Civil authority and ingress-egress claims
The closure of the port by Chinese authorities has prevented companies from accessing or moving their assets, thereby creating causes of loss that may trigger business interruption coverage.
Insurers typically require explicit documentation, including government documents, to support these types of claims. The capture of necessary and crucial documentation should occur as soon as possible and continue throughout the claims process.
Local versus master insurance policies and related coverage issues
Companies need to be aware that differences in underwriting practices and policies by Chinese insurers may create gaps in coverage.
Additionally, any operating companies in China that are part of joint ventures with foreign companies may face issues regarding varying interests by the parties to the joint venture.
It is crucial to fully understand these interests and their potential impact on insurance recovery early in the claims process.
At this time, we anticipate losses resulting from Tianjin will continue to unfold over the months ahead. Risk professionals are well encouraged to closely monitor potential losses as discussed above and to consult with their advisors, as required, throughout the process.
Aquifers Approaching Point of No Return
A new pair of studies show many of the largest aquifers are being depleted at alarming rates. Out of 37 of the world’s largest aquifers, more than 21 are past sustainability tipping points, which means that the rate of withdrawal exceeds the rate of replenishment.
Of those at highest risk, 13 are on the verge of exceeding the point at which they may not come back.
In a June 17 PBS NewsHour broadcast, Professor James S. Famiglietti of the University of California, Irvine, the lead author of one of those reports, discusses the potential impact of the dwindling supply of freshwater resources.
Famiglietti’s comments support those of experts interviewed for Aquifer: Nothing in the Bank, part of R&I’s April 2015 Emerging Risks special coverage. In the April article, experts discussed the deep impact of the depletion of California’s Central Valley aquifer on agriculture, as well as the ripple effects for real estate, construction, energy production and more.
VIDEO: Reports confirm that California’s Central Valley has been losing about 5.5. trillion gallons of groundwater per year for the last four years.
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.