Tag Archives: loss prevention

Loss Prevention or a Trojan Horse?

A buzz phrase gaining momentum in the insurance market at the moment is “ecospheres of prevention.” It’s about using smart devices to monitor for signs of impending loss events, and to then signal that to policyholders. For policyholders, this means events relating to your health, your house, your car and other such life contexts. It’s exciting stuff, a harbinger of a new insurance that focuses on preventing losses rather than simply paying out for them. Yet it is replete with issues of ethics and power, which I’ll explore in this post.

I should start by making clear that I’m a big fan of loss prevention. I spent several years immersed in the commercial side of it early on in my insurance career, with qualifications to match. Later on, I often got involved in related negotiations for personal lines policyholders with particular needs. So the idea of an ecosphere of prevention rings a bell with me.

The more I looked into this phrase, though, and weighed up the sector drivers being messaged around it, the more that bell began to sound like one of warning. Might I perhaps be over interpreting a trend still in its early stages? I think not, for it is already influencing the shape of insurance products and services. And, as academics often remind me, if what you see now seems still to be taking shape, address it now, for you’re almost certainly seeing only the tip of the iceberg.

Not Something New

These ecospheres of prevention are being used to position the sector as taking the lead in reducing losses. Yet is that really the case? It’s a mixed record.

On the one hand, personal policyholders have been calling for a long time for some form of recognition for taking the initiative on loss prevention. The sector’s response has instead been to position loss prevention measures as policy endorsements for above average risk properties, such as with locks for theft cover. Anything that didn’t fit into such endorsements was ignored, the argument being that there just weren’t the premium levels in, say, household insurance to warrant meaningful reductions.

On the other hand, the sector would argue that it was accepting that above average risk property at market rates because of that locks endorsement. In effect, this gave an inbuilt reduction in premium from what would otherwise be an exorbitant (go away) price. There’s some mileage in that, but I think in overall terms the personal lines market has seen loss prevention measures in a pot half empty way, despite policyholders urging a pot half full approach.

What I believe this adds up to is that it is not the financial dimension that has moved the retail insurance sector down this ecosphere of prevention road. The underlying, substantive reasons can be summed up as power and data.

The EOW Problem

I’m going to focus now on one particular use to which this ecosphere of prevention is being put, and that is around the “escape of water” peril, henceforth referred to as EOW. The last few years have seen a noticeable increase in debate about what is often referred to as the escalating problem of EOW claims.

It’s been explained to me that this is down to the age of what is being insured. Our housing stock is getting older, and the pipe work in it is just as bad. To this can be added the financial pressures that many families have faced since the financial crisis of 2008. The result is houses having pipe work that could be past its “best by” date. It’s an interesting narrative, with some mileage in it, but I’m not totally convinced that this seeming eruption of dire pipe work is so new a problem as it’s made out to be.

See also: COVID-19’s Once-in-a-Lifetime Opportunity  

It doesn’t help that, so often, the chatter in the trade press about the EOW problem takes the form of sponsored articles co-written by an insurer and its leak detection partner. Not hard to see a rather massive conflict of interest there. So I’m prepared to give little more than a nod to this narrative, until some serious, independent data and analysis is available.

Enforcing an Old Policy Warranty

The narrative we actually hear little to nothing of is that of policy warranties. Every property policy has a maintenance warranty, requiring the policyholder to keep the property in good repair. If the policy holder doesn’t, then the claim is likely to be turned down. After all, why should insurance pay for that lack of proper maintenance?

The problem, however, with the maintenance warranty is that it has not always been easy to enforce, and only then in an “after the event” situation. The sector felt it was invariably going to be on the defensive, only able to apply the warranty in the most extreme cases.

And this is where leak detection devices came in. Installed in the home, these devices signal to the policyholder and the insurer that a leak has happened, and, if the device is sophisticated enough, turn the water off automatically before much damage is done. Great, you may think, but remember that that device now puts the insurer in a much stronger position in terms of how it uses that maintenance warranty.

Able to detect even a dripping tap, the data streaming to the insurer now equips it with an evidence base for assessing the extent to which a loss was down to lack of maintenance or an insured event. It allows the insurer to apply the maintenance warranty earlier and more confidently. And it is the policyholder who then has to argument for the warranty not to be applied and the claim covered. Power has swung most definitely in the insurer’s favor.

A More Telling Narrative

Yet what’s wrong with that? many insurance people will ask. If the loss was down to maintenance, then great, the sector no longer has to pay out for the policyholder’s inadequate upkeep of the pipe work. And I recognize that argument, but then interpret it against the context of a wider and more telling narrative.

Consider what one leading European device manufacturer said recently about a partnership with an insurer: “We have created a blueprint for the introduction of water safety systems on a large scale to limit the risks for insurers.” And this from the same source: “a complete solution to eliminate the biggest cost driver in home contents insurance: water damage.”  

That is the language of business performance. It is not the language of loss prevention. This is about deploying devices with the aim of “eliminating” EOW claims. And in the process, pushing the cost of those leaks onto the policyholder.

Again, I hear insurance people say, what’s wrong with that? We don’t pay for maintenance; it’s the policyholder who should. That’s right, but that’s a position predicated on a number of attributes about that device, about the data streaming from it, about the analytics analyzing it and about the human or system assessing it. Accuracy is one of them, the best interests of the customer another, and several others lie in between. The opaqueness with which these ecospheres of prevention are assembled makes it hard to just go along with the apparent efficacy of the decision chain set up by these devices. This brings us to the data that such devices produce.

Data Harvesting

Leakage device manufacturers often talk about how well their product can detect even a dripping tap, and about how well it can interpret the incoming signals. Some talk about tracking patterns of behavior within the house, simply through water use. This is more than a lot of flushing in the morning and evenings. It’s about harvesting lots of data and using it to anticipate behaviors. This is hydraulic modeling and inferential analytics, using machine learning for anomaly detection. What’s more, it may not even be personal data. Inferential analytics bypasses the need for it.

What all this points to then are property utilities supporting lifestyle monitoring, through devices trained to detect down to the level of a drip of water. This is not an ecosphere of prevention. It is an “infosphere of assessment.” In other words, the house as an informational environment, used by insurers to identify business opportunities.

This could put insurers onto a tightrope of compliance. The senior managers and certification regime requires U.K. insurers (and European insurers by similar directives) to always act in the best interests of each customer. The accountability risk that’s present here is in the form of a gross/net gap, between what insurers say and what they do. In other words, the actual outcomes that result.

That risk is going to become much more tangible for senior management functions as supervisory technologies are put to judging the outcomes emerging as a result of gross/net gaps like this. The reason for turning those supervisory technologies onto something as apparently mundane as escape of water detection is that it is just one of a series of devices being promulgated by insurers across health, life, motor, commercial equipment and household portfolios.

This device-led “infosphere of assessment” should be a central part of a regulatory review of data ethics in the insurance sector. While the current pandemic has delayed the FCA’s data ethics review, it has also allowed the regulator to weigh up the implications of device-driven insurance even more carefully. Insurers should do the same.

A Divided Market

Let’s move on and look at another aspect of this situation, again using EOW losses as an example. Leak detection devices fall into two categories. At the cheaper end, they detect but don’t turn off. At the more costly end, they do both.

At the more costly end, Grohe’s Sense Guard retails at around £400 and is being marketed by insurers to more affluent customers, for example those with second homes. Fine, but that could still be a tough sell in terms of value, even before you add in the cost of all the sensors. Yet that value needs to be seen relative to devices at the lower end of that cost spectrum (the ones that detect but don’t turn off).

What emerges is a near future that could look something like this: Affluent customers will be required to install a “detect and turn off” device in their second homes. They will also be encouraged financially to install a “detect and turn off” device in their main home, and in return get continuing EOW cover, probably a lower excess and several frills as part of the package.

Less affluent customers could face this choice: either a) install a detect only device and get some cover, but with a high excess, or b) with no device, face higher excesses, less cover and a firmer handling of that maintenance warranty.

See also: Step 1 to Your After-COVID Future  

This is in effect moving the risk (and cost) of most EOW claims over to the policyholder. Of course, that’s not how this whole EOW movement wants to portray it, preferring instead to use words like water safety and water guard. In particular, the emphasis is on the reduction in losses as an all-’round good thing (which it is), rather than the reduction in cover, which is not a good thing.

The point, however, is this: If reasonable levels of EOW cover are only available in a device-oriented medium- to high-net-worth market, then the broad insuring public will feel let down. Times two. Stratification of the household market will step up, with actual cover (as opposed to loss prevention offers) being, for most people, harder and harder to acquire.

Summing up

The EOW water debate is moving in directions that risk undermining trust in the sector. In considering how to position themselves in relation to that debate, insurers might want to start with a small survey. At the next board meeting, ask each of the directors to confirm (anonymously, of course) whether, in the last five years, he or she has had their home’s pipe work tested and repaired by a competent trades person, for no other reason than it seemed about time to do so? Then ask the same question of technical underwriters, and this time ask them to evidence it.

I suspect few in either group will have done so. And that should tell you something. Is it asking too much of the sector, as the saying goes, to align the talk with the walk?

EOW has been a standard peril for so many, for so long, that something more imaginative, more innovative than so-called ecospheres of prevention is needed. Yes, engage in any necessary reform of EOW cover and the maintenance warranty. No, don’t use leak detection devices as a Trojan horse for household data.

Bring your customers with you: It will be worth it in the end.

You can find the article originally published here.

Predictive Analytics for Self-Insureds

Predictive analytics is widely used in the insurance industry. Is it time for self-insureds to reap the benefits of predictive analytics and realize significant bottom-line improvements as well?

Self-insureds can use predictive analytics for employee cost benchmarking, early identification of late-developing claims, and budget and allocation decision-making tools. Currently, the key area of focus for self-insured risk managers is claim prevention. But even the best claim prevention methods are not enough to avoid all claims. Claims occur despite a company’s and risk manager’s best efforts. If traditional claim prevention is the only defense against losses, these companies will lag behind their contemporaries who, to keep claim costs down, are already using predictive analytics.
Primer on Predictive Analytics
Whether we know it or not, we have all encountered predictive analytics in our personal lives by simply browsing the Internet. Companies like Amazon leverage their immense amount of data to predict customers’ shopping preferences to drive additional sales. Likewise, the insurance industry, with its substantial volume of data, views predictive analytics as an essential capability. Forward-thinking self-insureds see the benefits of these tools and realize that they should be used to better understand their exposure and better control their losses.

Predictive analytics can improve both customer satisfaction and company profits. Last week, I bought a webcam from Amazon. The product page displays the specific details on the webcam and shows an assortment of additional items frequently bought by other customers who purchased this webcam. Amazon uses its customer purchase data to predict that webcam purchasers also routinely buy extension cables, microphones, and speakers along with their webcam. Thus, Amazon has effectively applied predictive analytics to identify cross-sell opportunities that benefit its customers (I was glad to be reminded that I would, in fact, need an extension cable) and increase its revenue (I spent an additional $5.99).

Amazon’s product pages are an easily visible example of predictive analytics. Similar to Amazon, the insurance industry has adapted predictive analytics to not only increase premiums but also to improve risk selection. Risk selection—being able to identify the good/profitable risks and the bad/unprofitable risks—is so important for insurance companies that the popularity of predictive analytics comes as no surprise. The range of ways insurance companies are using these tools includes evaluating the profitability of their accounts, assisting in effective underwriting and proper pricing, marketing to the appropriate client base, retaining their customers, and estimating the lifetime value of each customer.

It is important to note that predictive analytics is not just a data summary. Predictive analytics sorts through vast amounts of data to find relationships among variables to predict future outcomes. This data can often be seemingly disparate, or even appear to be unrelated; it also often combines the use of both internal company and data from outside the organization. For example, a commonly cited and successful example of such a relationship involved insurers finding a direct correlation between two variables: credit scores and auto claims. Also, the more data fed into an analysis, the more robust it will be. Self-insureds have quite a bit of employee data that greatly aids analyses’ predictive abilities. In addition to loss-specific data, payroll, human resources information, and other available third-party data should be utilized to its fullest extent for optimal results.

Applications for Self-Insureds
Self-insureds have numerous opportunities to benefit from predictive analytics. Three large workers’ compensation areas on which predictive analytics sheds light are: 1) identifying which employees cost more than industry and company averages, 2) predicting early on which claims are the most likely to have late-developing costs, and 3) constructing qualitative cost/benefit scenarios to help risk managers allocate their budgets effectively.

Self-insureds applying predictive analytics to their workers’ compensation claims, for example, have a number of employee variables to work with such as: employee age, length of employment, state of residence, employment type (full time/part time), salary type (hourly/salary, low wage/high wage), and claim type (indemnity/medical only). Predictive analytics can find relationships that will affect future claim activity on current and future employees.

The real goal of predictive analytics for self-insureds is to help guide and support risk managers’ decisions. Predictive analytics can be applied to both ‘pre-claim’ and ‘post-claim’ loss prevention methods. To aid in claim prevention, pre-claim-focused analyses are used to highlight high-risk (high-cost) employees. The loss costs of various groups are compared to each other, a company average, and to average industry loss costs provided by the National Council on Compensation Insurance (NCCI). Loss categories higher than company or NCCI averages get closer examinations for loss drivers and mitigation strategies. A higher-than-average cost for newly hired employees may signal a need for more training. A higher-than-industry cost for claims in certain states may be noted. A particular type of injury may emerge as the most costly. The potential savings can be estimated as the difference between the current loss costs and the benchmark loss costs, times the percentage of employees or expected claims involved.

For example, a self-insured entity may know that its newly hired employees experience a larger proportion of losses than employees with longer tenure. If it conducts an analysis and discovers that low-wage employees working in Illinois with less than six months of experience have substantially higher costs than the average employee, claim prevention resources could be specifically aimed at that employee demographic to control costs.

Savings Opportunities
A notable benefit of predictive analytics is that it provides quantitative cost-saving information to risk managers. Continuing with the prior example, assume 2,500 employees are newly hired, low-wage employees in Illinois and their average costs have been shown to be three times higher than the company average of USD $1.50/$100 of payroll. We can estimate that a reduction from $4.50 to $1.50 could create $2.25 million in savings. Asking senior management for $100,000 for more new hire training in Illinois facilities will be much easier with the quantitative support provided by predictive analytics.

(2,500 employees with an average payroll of $30,000 save $3 = 2,500 x 30,000/100 x 3 = $2.25M)

Not only can predictive analytics assist with reducing cost ‘pre-claim’ by focusing on exposure, it can also reduce costs once a claim has occurred. Knowing the easy-to-identify large claims will be second nature to risk managers, however, ‘post-claim’ predictive analytics can look into claim development details to find characteristics that late-developing, problematic claims (and often not the obvious large ones) have in common. After a loss has occurred, one of the most effective ways to manage costs is to involve a very experienced claims handler as soon as possible. The results of effective ‘post-claim’ predictive analyses will assist in implementing cost-saving claims triage. Because the best resource post-claim is good claim management, predictive analytics can get late-developing, problematic claims the timely attention they need to contain the ultimate costs or even settle the claim.

Loss savings based on predictive analyses extend beyond claim cost reduction. Being able to quantitatively show potential savings and concrete mitigation plans will make a positive impression on senior company management and excess insurance carriers. Demonstrating shrewd knowledge of the loss drivers and material plans to reduce the losses can aid in premium negotiations with excess carriers for all future policy years. And if the insurer or state is holding any collateral, the predictive analytics’ results can be used by the self-insured in negotiating.

The key to unlocking further potential cost savings in your self-insured plan is readily available in your own data. Predictive analytics is the tool that will help risk managers make better claim reduction decisions and produce actionable items with real cost savings now and in the future. Risk managers and self-insured companies can look forward to possible benefits such as loss cost reductions along with reductions in excess premium and collateral, and quantitative information to help them with budgeting and allocation. As more self-insureds begin applying predictive analytics to control costs, companies that are not using these tools will be at a competitive disadvantage.

For more information on predictive analytics, watch this video of a Google hangout with Michael Paczolt and Terry Wade of Milliman, Inc.:

Internal Vs. External Benchmarking Of Insurance Claim Data

Data-driven analysis is a critical decision-making tool for Construction Financial Managers and other industry leaders.

Decision-making is arguably the most important responsibility of company leadership.

Companies that make better decisions make fewer mistakes, and achieve a distinct competitive advantage in the marketplace.

The underlying purpose of benchmarking is to continually improve the quality of organizational decision-making.

As construction risk management consultants, we help contractors prevent accidents, mitigate claims, and reduce the total cost of risk through a continuous improvement process.

We believe companies must instill management accountability for continuous improvement by linking performance measurement to both prevention activities (leading indicators) and operational results (lagging indicators). As the adage goes:

“What gets measured is what gets done.”

In our consulting roles, we frequently help companies establish realistic performance measures by conducting various types of claim and loss analysis.

This type of data analysis is usually the starting point in a performance improvement process — and a common practice among insurance agencies, brokerages, carriers, and risk management consulting firms.

In addition, we are often asked to conduct a benchmarking analysis that compares one company's claim and loss data against peer companies or to the construction industry as a whole.

The term “benchmarking” refers to the comparison of a company's performance results against those of similar peer companies. Benchmarking evolved out of the quality improvement movement in the late 1980s and early 1990s.

Its initial intent was to identify leading companies regardless of industry sector, and apply their best practices to improve one's own company. Over time, benchmarking has become synonymous with process improvement.

The traditional view of benchmarking required two separate disciplines focused on performance improvement: measures and methods. Identifying and capturing performance indicators (the measures) is only the first step; developing and implementing performance improvement (the methods) is the second and most important step for the benchmarking process to be truly effective.

The Health Club Analogy
There is limited value in benchmarking without applying new methods to address continuous performance improvement. Performance improvement requires more than the measurement of performance indicators; it requires the implementation of changes in management disciplines to attain improved operational results.

Using only performance indicators without implementing new methods to improve operations is akin to joining a health club and expecting the benefits without actually using the equipment or committing to an exercise program.

Merely jumping on the scale and gauging your weight relative to others doesn't help you achieve your own weight loss goals anymore than comparing your pulse and respiration rate to others helps you attain your aerobic or cardiovascular fitness goals. What matters most is that a person embarking on a weight loss or fitness program stays committed to the process and monitors his or her own progress.

Similarly, we believe the ongoing monitoring of claim and loss data specific to an individual company is even more important than the initial measurement of insurance claim and loss data relative to other companies.

Baselining As Benchmarking
The term “baselining” refers to the internal benchmarking process that occurs when a company compares its performance against its own results year after year. Ongoing, internal monitoring allows a contractor to determine if the company's claim and loss trends are improving or deteriorating, and to make the critical performance improvement decisions necessary to facilitate a change in results.

Referring back to the health club analogy, baselining does not compare an individual's weight and aerobic fitness to that of the other health club members. Instead, individual fitness goals and measures are established, monitored, and tracked to verify continuous personal improvement.

Similarly, a construction company can develop a baseline analysis of its loss cost performance by reviewing loss and claim data for a minimum of 3-5 years. Company results are compared from year to year, and ideally are broken down by operating entity, division, project, manager, or even crew levels.

Exhibit 1 provides a sample of a baseline analysis that compares one company's relative claim and loss performance within all of its operating divisions.

2001-2006 Total Claim Cost per Man-Hours Worked by Division


This analysis reviews the historical loss cost data for the entire company and breaks it down into meaningful data relative to each operating division. The total workers' comp, Comprehensive General Liability, and auto liability incurred claim costs (sum of paid and reserves) for each company division over a five-year period were compared to the total man-hours for each division, producing a cost per man-hour figure.

The results illustrate dramatic differences in total claim costs per man-hour for each division. This baseline analysis was the first step in raising awareness of the predominant loss leaders within the company. This increased awareness led to a detailed analysis that established plans of action and realistic cost targets by company division for the upcoming year.

External Benchmarking
We acknowledge that there are numerous benefits to measuring the frequency, type, and cost of insurance claims compared to peer groups and/or the entire construction industry. Such analyses provide the ability to:

  • Identify leading types and sources of claims
  • Establish strategic objectives to prevent the occurrence of common industry claims
  • Increase knowledge of industry best practices
  • Determine operational performance improvement priorities
  • Create awareness among managers and employees about the costs of claims and the impact on profitability
  • Post positive results on company websites and for use in other marketing materials

The Bureau of Labor Statistics provides safety-related data so that companies can externally benchmark injury and illness data against specific industry groups. (Check out the Web Resources section at the end of this article for more information.)

In addition, Bureau of Labor Statistics data is used to calculate and compare OSHA Recordable Incident Rates and Lost Workday Incident Rates, both of which are common construction industry benchmarks. This data is useful when making high-level comparisons within construction industry segments relative to injury and illness rates.

We also use external benchmarking analyses to establish risk reduction, loss prevention, or cost containment goals. In “Risk Performance Metrics” by Calvin E. Beyer in the September/October 2007 issue of Building Profits, a sample benchmarking comparison shows a representative contractor's duration of lost workdays workers' comp cases in median number of days compared against the median duration for the industry. Results such as these can highlight the importance of an increased focus on injury management and return-to-work programs.

The benchmarking analysis in Exhibits 2A and 2B compares a contractor's workers' comp claim and loss performance to an established group of peer contractors in the same specialty trade. (These companies engaged in similar work, and performed in states with similar insurance laws and legal climates.)

WC Claims Per $1 Million WC Payroll by Company

The analysis was based on total incurred workers' comp costs and total number of workers' comp claims as compared to payroll for each entity. Overall, Company D had worse results than the other three companies.

This prompted an in-depth review of Company D's workers' comp losses by division and occupation. As shown in Exhibit 3, the company experienced significant claim frequency and severity issues within the first six months of employment.

WC Claim Count & Cost by Length of Service

These findings triggered the development and implementation of specific activities designed for Company D's new employees.

Below are some of the activities that were incorporated into the formal improvement plan:

  • hiring processes
  • new hire skills assessments
  • orientations
  • daily planning meetings
  • formal training

Other Sources Of Benchmarking Data
Professional associations and industry trade/peer groups also provide comparative data for benchmarking purposes.

The Construction Financial Management Association's Construction Industry Annual Financial Survey is an excellent source for understanding the key drivers of contractor profitability. We use the survey data to determine comparative profit margins for different types and classes of contractors when we calculate a revenue replacement analysis to show the additional sales volume needed to offset the cost of insurance claims. (This technique was highlighted in the “Risk Performance Metrics” article previously mentioned.)

Similarly, the Risk and Insurance Management Society (RIMS) conducts an annual benchmarking survey that reviews insurance rates, program coverages, and measures of total cost of risk.

An example of a peer group data source for benchmarking is the Construction Industry Institute (CII). The Construction Industry Institute is a voluntary “consortium of more than 100 leading owner, engineering-contractor, and supplier firms from both the public and private arenas” (www.construction-institute.org). It develops industry best practices and maintains a benchmarking and metrics database for its participating members.

Another peer group example involves members of captive insurance companies sharing and comparing claim and loss data for the group as a whole. There is a major advantage when a true peer group shares benchmarking data: Such data sharing often leads to peer pressure in the form of increased ownership and accountability for improvement by the companies shown to be the poorest performing members.

We continue to search for more new sources of industry best practices and comparator data. A possible emerging source for the construction industry is the National Business Group on Health. This organization has developed standardized metrics known as Employer Measures of Productivity, Absence and Quality™ (EMPAQ®).

EMPAQ® helps member companies gauge the effectiveness of their injury and absence management and return-to-work programs. The founder and principal of HDM Solutions, Maria Henderson, served as a project sponsor for EMPAQ® from 2003-2007, and co-presented with Calvin E. Beyer on “Return to Work as a Workforce Development Strategy” at CFMA's 2008 Annual Conference & Exhibition in Orlando, Florida.

Limitations Of External Benchmarking
We fear that the increasing popularity of external benchmarking analyses may indicate that it has become a “quick fix” solution or a management fad. When asked to conduct an external benchmarking analysis, we always ask the following questions:

  • What is your purpose in seeking these comparisons with other companies?
  • Who are you trying to convince and what are you trying to convince them to do?
  • What specific peer companies should be used for comparative purposes?
  • Are these companies (and their operations and exposures) truly similar enough for a fair comparison?

Beware Of Pitfalls
There are many hurdles to surmount in locating suitable companies for external benchmarking comparisons. Generally, when benchmarking comparisons can be made, more often than not the greatest value lies in the workers' comp line of insurance coverage.

Here are some key factors to consider when choosing contractors for external benchmarking comparisons:

  • Percent of self-performed work vs. subcontracted work
  • Payroll class codes and hazard groupings of selfperformed work
  • Differential geographic labor wage rates
  • Payroll rate variances between union and merit shop operations
  • Size of insurance deductibles
  • Claim reporting practices

For example, claim reporting practices must be similar in order to minimize distorting the frequency or average cost of a claim. If one or more comparison companies self-administers minor claims or does not report all claims to their carrier, using carrier loss reports for the comparison is an invalid method.

We also find that comparing the frequency of claims and total loss dollars divided by thousands or millions of dollars of payroll (exposure basis) is a helpful workers' comp benchmark between companies of similar operations in similar states.

Likewise, a suitable benchmark for auto liability performance compares the frequency of claims and total loss dollars per one hundred vehicles.

When benchmarking fleet-related claims, ensure that the number and size of fleet vehicles — as well as the type of driving (urban vs. rural) and the total number of miles driven annually — are similar among the contractors whose claims are being compared.

Benchmarking comparisons of Comprehensive General Liability insurance results are especially challenging due to delays in reporting third-party bodily injury and property damage claims, in addition to the expected long tail of loss development for these claims.

All of these factors are compounded by vastly different litigation trends and liability settlements in various states and regions of the country.

Common Limitations Of Data Sources
Whether or not you intend to develop a baseline of your company's claim data or to benchmark your company's performance against a peer company, there are several issues that must be successfully resolved regarding the data's quality and integrity.

Based on our experience, we classify the key challenges associated with exposure and claim/loss data into the categories shown in Exhibit 4: availability, accuracy, accessibility, standardization, reliability, comparability, and date-related problems.

Seven Data Challenges

Value Of Multiple Measures
Evaluating data from various sources and different angles is also valuable. Why? Because it's possible to gain a better understanding of the whole by dissecting the parts. This practice illustrates the principle of multiple measures.

This approach is substantiated by 2006 research, which concluded that the “simultaneous consideration” of frequency and severity provides a more comprehensive result than performing analysis based solely on one factor.1

This is similar to our approach when we conduct a “Claim to Exposure Analysis” and review historical frequency and severity vs. the relative bases of exposure for each line of casualty insurance coverage.

Returning to the health club analogy, when starting a formal exercise program, you often begin with such general baseline measurements as height and weight; this is usually followed by additional measurements, such as BMI, body fat content, and the girth of arms, legs, and chest (the baseline).

As we all know, weight alone is not always the best indicator of success in fitness efforts. In fact, since muscle weighs more than fat, an increase in total body weight may actually occur after beginning and maintaining a fitness program.

Although you might not experience a dramatic weight drop, you could see a reduction in waist size and BMI — positive changes that would not be evident unless multiple measures were being used and reviewed.

Benchmarking insurance claim and loss data performance is like comparing one person's height and weight against the ideal height and weight charts based on the entire population.

Wouldn't it be more effective to establish your baseline weight and other multiple measures initially so you can see the progress you are making?

This is similar to the baseline measurements that a company should take (as well as the multiple measures) that are necessary to meet your company's performance improvement goals for financial success, operational excellence, or risk reduction.

Web Resources:

  1. U.S. Department of Labor BLS Incidence Rate Calculator and Comparison Tool
  2. National Institute for Occupational Safety and Health Work-Related Injury Statistics Query System
  3. Risk and Insurance Management Society, Inc. Benchmark Survey
  4. Construction Industry Institute Benchmarking & Metrics
  5. National Council on Compensation Insurance, Inc. (NCCI Holdings, Inc.) Benchmarking Tools
  6. Employer Measures of Productivity, Absence and Quality EMPAQ
  7. CFMA's Construction Industry Annual Financial Survey with Benchmarking Builder CD

Cal Beyer collaborated with Greg Stefan in writing this article. Greg is Assistant Vice President, Construction Risk Control Solutions, at Arch Insurance Group. As a member of the Southeast Regional team in Atlanta, GA, Greg supports underwriting and claims in risk selection, claim mitigation, and risk improvement activities. He is also responsible for high-risk liability risk reduction initiatives including contractual risk transfer, construction defect prevention, and work zone liability management.

1 Baradan, Selim, and Usmen, Mumtaz A., “Comparative Injury and Fatality Risk Analysis of Building Trades,” Journal of Construction Engineering and Management, May 2006, pp. 533-539.