I love listening to life stories.  As a hospice chaplain, I loved sitting with our patients and their loved ones engaging in what many hospice teams call “life review.”  When did you meet your spouse?  When was Reggie born?  What is your favorite holiday?  When did you learn you were ill?  A few simple questions and the stories come pouring forth.

Of late, I’ve been listening to the life stories of Gen X individuals whose Baby Boomer mom or dad, stepmom or stepdad, died in the fall of 2010.  Each story is unique and beautiful, full of grace-filled surprises found in the midst of daily survival.  As they review the life of the parent who has died through the lens of caregiving and grieving, we catch a glimpse of how the first wave of the Baby Boomers is aging and dying. The tidal wave of elderly boomers has not hit us yet, but it is coming.

According to AgingStats.gov: “The baby boomers (those born between 1946 and 1964) will start turning 65 in 2011 … The older population in 2030 is projected to be twice as large as their counterparts in 2000, growing from 35 million to 72 million and representing nearly 20 percent of the total U.S. population.”

Baby boomers will live longer and in greater numbers than ever seen before with few youngsters to support them financially and physically.  What and who will ensure that the Baby Boomers have space and time to age gracefully?

The idea that our current healthcare system is less than adequate to support the needs and expectations of the “silver tsunami” of the Baby Boomers is far from new.  Debates continue on how Medicare and the long-term care system need massive overhaul, so I won’t enter that minefield.  My mind goes to the home.  I think of how as the boomers begin to age, they will need “informal” or “family” caregivers by the thousands.  “Informal caregiving” can be defined as “unpaid care given voluntarily to ill or disabled persons by their family and friends.” They assist a parent, friend or neighbor with completing normal activities of daily living ranging from driving, grocery shopping, taking medication, managing money, to even more personally vulnerable activities like bathing, dressing, using the toilet, or eating.

In past generations, a less debilitated spouse would be the primary informal caregiver, but there are a shockingly high number of single boomers.  According to a recent survey of the McKinsey Global Institute, by 2015, 46% of all boomers aged 65 and above will be unmarried, creating 21 million unmarried households.  For the same age group in 1985, there were only 10 million unmarried households.  In an age marked by high rates of divorce, either the role of an ex-spouse will change or an adult child will be forced to step up as the primary caregiver and decision maker for an aging parent.  The increased caregiving burden on the Gen X and Millenials of the future will demand creative work, family, financial, and practical solutions that just don’t exist yet.

According to the AARP, most informal caregivers provide an average of 21 hours of care per week, so basically a part-time job.  They paint a picture of informal caregiving where caregivers assume responsibility for their loved one’s day to day care, triage any health care crises, absorb financial burdens big and small, and tend to underestimate how much time and how stressful being a caregiver will truly be.    As a mother of three, these observations sounded a lot like caring for a toddler.  It shouldn’t have surprised me then when their data showed that a typical caregiver in the US is a 46-year-old woman who works outside the home.

That sounds a lot like me and my friends in a few years — we have jobs, kids, friends, hobbies and parents — and my anxiety rises as I think about 2030.  How will my life story be changing?

Rosalynn Carter once said, “There are four kinds of people in this world: those who have been caregivers, those who currently are caregivers, those who will be caregivers, and those who will need caregivers.”

The next 30 years will be defined by the quality of care we provide for our elders.  How will the Baby Boomers age and die?  How are we as their kids going to care for them well and honor their memory and legacy?  What kind of lives will we review?

http://www.kevinmd.com/blog/2012/01/baby-boomers-age-die.html

“Scientia potentia est” is Latin for “knowledge is power.”

This phrase could be linked to many different businesses and operations, but is especially relevant to the healthcare professional liability (HPL) industry.  As HPL risk transfer becomes more and more prevalent, every bit of knowledge or “risk data” has to be exploited to its full potential in order to stay ahead of the curve. In this article we will look at the art of “risk analytics,” and how it can be one of the most important weapons in a HPL underwriter’s arsenal.

One HPL exposure that has evolved is now identified with a new name, Senior Care Long-Term Services and Supports (LTSS). It is currently one of the largest growth industries in the U.S., and Insurance carriers that learn how to underwrite and manage these risks will see exponential growth opportunity. 

LTSS Data

Actual LTSS exposure and care data had been difficult and expensive to obtain in the past, requiring the use of consulting firms to harvest and organize the data into usable chunks. The utilization of now publicly available data will be a key differentiator in successfully underwriting LTSS insurance and programs in the foreseeable future.

An example of this new published data that would be valuable for a LTSS healthcare liability program risk analysis is the upcoming National Survey of Residential Care Facilities (NRSCF), to be released by the National Centre for Health Statistics (NCHS) by the end of 2011. Under this umbrella, the NSRCF would consist of three products: a methods report (describing how the NSRCF was conducted) a facility data brief (containing highlights of major findings on U.S. residential care facilities), and a facility public-use data file with documentation (containing data collected about the healthcare facilities). 

By April 2012, the NCHS also plans to make available to the public a resident public-use file and a data brief, reporting selected characteristics of residents of U.S. residential care facilities. The NSRCF, the first nationally representative sample survey of residential care communities, was conducted between March and November 2010.  Interviewers collected information on more than 2,300 facilities and over 8,000 residents.  Reports like this can be invaluable for healthcare program underwriter’s managers, giving them a real-time, all-encompassing perspective that will allow them to shape their insurance program to be right at the cutting edge of the market.

Using timely and informative LTSS data in a responsive and influential manner, when coupled with risk analytics modeling, helps to provide the insurance manager with answers to very important questions about the true exposures and gives them a much needed advantage.  The reach of risk analytics is spreading through expert third-party service providers; and the advantages of sophisticated modeling tools are available to most, regardless of in-house technological expertise or available capital.

Underwriting insurance for the growing LTSS healthcare industry is becoming increasingly difficult as every day healthcare facilities tweak and evolve their operations to remain profitable under changing Medicaid / Medicare reimbursement policies and with evolving regulatory expectations.  Over time, rising acuity, additional services, and diminished staffing ratios will lead to adverse incidents if not kept in check. It used to be that these fluctuations in underlying risk would go undetected, but with the utilization of modeling tools and effective risk analytics, even subtle changes in staffing, acuity, and services can be revealed. Healthcare modeling provides the insurance program underwriters with the knowledge of any change to risk drivers, thereby allowing the program leadership to make pre-emptive changes and to manage risk more effectively.  For that reason, risk analytics is revolutionizing the processes and tools employed by insurers to more quickly and accurately market, price, and underwrite their products.

Once a change to a risk driver is detected, the underwriter can project how these changes will affect the overall portfolio of risk exposure.  From that knowledge, the program manager gains deep insight into actual loss costs and can confidently adjust premiums, offer feedback regarding risk management, and continually monitor-preferably before any loss occurs.  Without predictive modeling and risk analysis after an account is written, the policy is generally held in status quo with minimal consideration to any variation in underlying risk, until it’s too late and a major loss develops.

With improved risk data management, insurers can lower overall costs, charge adequate premiums, reduce claims, gain competitive advantage, and increase their market share.  It all starts with underwriting the data.  Every exposure must be analyzed to establish the appropriate premium in order for the program to remain viable for the long term.  For that reason, experienced industry-specific underwriters who understand the specific risk are critical.  Historically the theory has been vetted. Throughout many risk industries, predictive modelling strategies, when measured against traditional under-writing approaches, were found to be more accurate.  Essentially, predictive modeling can help eliminate the human and emotional response that naturally occurs in the underwriting, loss control, and claim handling process.

With risk analytics, potential claim incidents can be rapidly and cost-effectively analyzed.  At this time, ‘real’ risk is identified sooner, triaged appropriately, and dealt with proactively.  Effective risk analytics can accelerate the acquisition of knowledge, place claims into proper context, lower claims administration costs, and help improve overall outcomes.  Moreover, predictive modeling can chart the course for improved negotiations with plaintiffs, and ideally, lower overall settlements.  There are many variables that go into each case that ultimately determine how it is settled. Once a case proceeds to court, the deciding factor is people in the jury box.  How they will decide is extraordinarily unpredictable.  With the passage of time, the cost to settle any case may increase exponentially.  Risk analytics and predictive modeling provide the insurer and the defense team with rapid access to the information needed to manage incidents proactively, triage claims effectively and settle claims before that critical window of opportunity closes.

A program underwriter has to play to the strength of risk analytics in order to benefit from it, which includes being savvy and quick enough to respond.  This also includes being flexible enough with the tailoring and implementation of a predictive model to match the flexibility of risk analytics as predictive modeling tools are available for any step along the continuum, including marketing analytics, underwriting, risk management, and loss mitigation.

Another advantage of predictive modeling is the ability to establish more accurate actuarial reserves.  With improved accuracy in identifying overall risk, carriers can establish and responsibly change reserves as needed. Such financial efficiencies allow an organization to direct their financial resources to the most effective point.  This helps make great savings as the captive program is aimed specifically at the exact areas that require focus-enabled by risk analytics. 

Historically, a large portion of an insurance program’s expenses are consumed by the initial application and risk underwriting processes.  Predictive “sales” modeling can assist in finding suitable accounts more efficiently than the traditional approach that requires underwriting to review and analyze 10-20 accounts before finding one that fits for the risk program’s appetite.  This can be viewed as a sales divining rod-finding the suitable risk with minimum marketing or sales expense outlay.

Conclusion

Risk analytics is not a “magic solution” for the insurance program and the actual underwriting of a profitable book of business still requires a great deal of work.  While risk analytics and predictive modeling have tremendous advantages to offer insurers and risk management organizations, the ultimate value is derived when the experts interpret the information correctly and make the right decisions.

Reading the landscape through accurate data, analyzing trends and acting on them accordingly and efficiently helps insurance captive managers take out some of the risk of managing risk.

“Scientia potentia est”… or more simply, “in the land of the blind-the man with one eye is king.”

Paul R Marshall – marshallpr@gmail.com

Quote: Predictive modeling provides insurance professionals with the knowledge of any change to risk drivers, thereby allowing the risk program manager to make pre-emptive changes and to manage risk more effectively.

Some risk managers have an unfair advantage. It’s called predictive modeling, and it is revolutionizing the way risk is both predicted and managed. These advanced predictive modeling tools provide captives with the information necessary to quickly and accurately market, price, underwrite and defend claims more effectively without a major investment of capital. Fortunately, through expert third-party service providers, the advantages of such sophisticated modeling tools are available to any captive regardless of in-house technological expertise or available capital. Ultimately, these advantages enable captives to operate more efficiently, thereby lowering overall costs and creating a competitive advantage. For any captive that seeks to improve overall performance, it’s time to understand how predictive modeling can make success a reality.

Know the risks

Every day, healthcare facilities tweak their operations to remain profitable under changing reimbursement policies and evolving regulatory expectations. These tweaks, while important for profitability, often have major implications for underlying risk. To illustrate, consider a healthcare facility that is advised by a consultant to reduce staff and increase its Medicare payer-mix in order to improve or maintain profitability. In the short term, this advice may assist the facility in reaching such goals, but eventually, this decision will have a dramatic effect on risk. Overtime, rising acuity and diminished staffing ratios will lead to adverse incidents. Traditionally, this variation in underlying risk would go undetected. But with predictive modeling tools and risk analytics, even subtle changes in staffing and acuity can be revealed. Predictive modeling provides insurance risk managers with the knowledge of any change to risk drivers, thereby allowing the captive to make pre-emptive changes and to manage risk more effectively.

Once a change to a risk driver is detected, the captive can predict how these changes will affect risk and impact its overall portfolio. From that knowledge, the captive gains deep insight into actual risk and can confidently adjust premiums, offer feedback regarding risk management, and continually monitor before a loss occurs. Without predictive modeling and risk analysis, after an account is written, the policy is generally held in status quo with minimal consideration to any variation in underlying risk, until it’s too late and a major loss develops. Conversely, not all changes to risk are negative. Some operational decisions result in positive effects on risk or provide compensating controls. Consequently, it takes sophisticated tools to parse out the individual effects (both positive and negative), add them up correctly in terms of future risk and obtain the most accurate score for that account.

Operational and financial efficiencies

“Predictive modeling infuses more accuracy and more integrity into the process of predicting losses and identifying the risk drivers that allow insurers to operate more efficiently,” explains Chris Kramer, senior vice president of Atlas Insurance Management. These efficiencies, while evident in every step along the continuum from marketing analytics to claims defense, are most notable in the areas of underwriting and claims management.

                It all starts with underwriting. Every account must be analyzed to establish the appropriate premium in order for the captive to remain viable for the long term. For that reason, experienced underwriters are critical. However, predictive modeling can provide rapid, critical information to assist underwriters in making more accurate, consistent and timely decisions about the potential for risk. To analyze just one account, an underwriter can spend hours. In comparison, a predictive modeling system can present an analysis in minutes with equal or greater accuracy. In fact, predictive modeling systems have been tested against traditional underwriting approaches and were found to be five to ten times more accurate.

                “There are only so many things an underwriter can look at to assess risk. If he had an infinite amount of time to assess each risk, he’d be almost 100 percent accurate. But, in reality, no underwriter has infinite time, and we all make judgment calls and mistakes. Predictive modeling allows all the people involved, from underwriters and actuaries to those who set reserves, to use the insight gleaned from hundreds of data points. After the data is entered into predictive modeling software systems, highly reliable results provide underwriters with an enhanced ability to assess risk and set proper premiums and parameters for coverage,” Kramer adds.

Defend Yourself

                In terms of claims management, predictive modeling accelerates the acquisition of knowledge and helps insurers put claims into the proper context. “The quicker we can investigate, understand and evaluate the claim, the quicker we can reach our decision points,” explains Paul Hamlin, founder and president of Hamlin and Burton Liability Management, Inc. and an expert in the field of claims management. “Does the claim have value? What is the potential value of this claim? Are we comfortable defending it at trial? Risk analytics helps us tailor the investigations, because if we can identify problem areas, we can dig deeper and perform more pointed and specific investigations. Any time we have access to critical information about the facility, staffing levels, potential problem areas, etc. early in the claims-handling process, we improve our chances of being able to evaluate the claim accurately and resolve it in an optimal fashion.”

                Moreover, predictive modeling can chart the course for improved negotiations with plaintiffs and, ideally, lower overall settlements. “Predictive modeling and risk analytics have allowed us to focus on critical claims by gaining rapid clinical and analytical insight to help us put claims into a broader context and obtain more favorable outcomes,” notes Jonathan Swann, underwriter, CareSurance Nursing Home Programme at Lloyd’s. At a minimum, the information guides claims professionals and quickly gives them the wisdom to determine which claims to defend and which to settle. Too many captives are satisfied with loss ratios of 50 percent because, for the industry as a whole, this is acceptable. But, predictive modeling is going to shake up the perception of ‘acceptable’ loss ratios and has already demonstrated its ability to deliver significantly lower loss ratios.

                The end result is improved accuracy and lower claims administration costs. Claims managers who research files in the traditional approach will spend 25 to 50 percent more time completing the legwork necessary to obtain the data necessary to evaluate a claim. “Most data in the comprehensive risk analysis we could find out on our own, but it would take a tremendous amount of legwork and would be done slowly, expensively and inconsistently,” says Hamlin. If predictive modeling and risk analysis can save an insurer just five percent of total claims management expense, that could easily be converted into a significant competitive advantage.

It’s flexible—pick and choose applications

                Predictive modeling tools are available for any step along the continuum, including marketing analytics, underwriting, risk management and loss mitigation. To illustrate, consider a captive created to meet the insurance needs of nursing homes belonging to a particular faith-based association. Let’s assume that the president of the association has several nursing homes in the pool and believes his facilities deserve a reduced rate. In this instance, the captive can use an outside, objective third-party expert and access models that quickly evaluate each facility and provide a one-page summary of the comparative risk.

                Such analysis can also provide the quantitative information necessary to allocate premiums more fairly and more accurately across the captive. Essentially, predictive modeling can help eliminate the human and emotional response that naturally occurs in the underwriting process. As explained by John Henry, principal owner of the Boston Red Sox: “People operate with beliefs and biases. To the extent that you can eliminate both and replace them with data, you gain a clear advantage. Actual data means more than individual perception/belief.”

Opportunities

                Predictive modeling and public data can also be used to target preferred risk and steer the acquisition of new business. By taking the offensive position in finding new business that meets a desired set of risk parameters, a tremendous amount of savings can be realized. On average, 50 percent of the first year’s premium is paid out in commission, underwriting and the sales application process—not to mention that finding just one suitable account in the traditional approach may take underwriting the effort of reviewing and analyzing more than 10 accounts before finding one that is right for the risk program.  

                Another advantage of predictive modeling is the ability to establish more accurate reserves. With improved accuracy in identifying overall risk, carriers can establish reserves that are commensurate with the underlying risk. Such financial efficiencies allow an organization to direct limited resources to the right place. If too much premium is allocated to reserve, then not enough is available for operations, and vice versa. Again, predictive modeling helps an organization be both fiscally and operationally efficient.

Consider the evidence

To remain competitive in the industry, insurers need to capitalize on advances in technology and the growing availability of data by implementing data-driven predictive modeling tools. Evidence clearly indicates that insurers that embrace the power of sophisticated predictive modeling tools experience significantly lower loss ratios than industry averages. The reason is simple: by gaining greater insight into what is happening today in their risk pool, action can be taken to address risk, appropriately price the premium and operate more effectively.

About the author:

Paul Marshall has 19 years of experience in the healthcare liability insurance and claims management industry.  At his current position as VP Sales Healthcare with American safety Insurance he is responsible for business development of ASI Healthcare’s insurance programs and agent broker relations.  Prior to joining ASI, Paul held senior management positions for several leading healthcare insurance, claims and risk management organizations including PointRight Inc., Hamlin & Burton Liability Management and Neace Lukens Insurance and Risk Management. Paul has diverse and cutting-edge experience implementing risk management processes and communicating risk management principles.

Paul Marshall can be contacted at:

paul.marshall@amsafety.com

marshallpr@gmail.com 

http://www.linkedin.com/in/paulrmarshall
Paul R Marshall│Vice President Sales

American Safety Insurance │ASI Healthcare

27865 Clemens Rd.  Westlake, OH 44145

D: 440.925.2030 │ M: 937.475.1071

O: 440.925.1930 │ F: 866.827.6057

 paul.marshall@amsafety.com

www.amsafety.com

Paul Marshall  summarizes how turning to Data Analytics and Predictive Modeling helped Healthcare Liability programs return favorable underwriting results.

Published US Captive Magazine 2009

Abandoned by traditional insurance carriers during the liability crisis of the late 1990s, many nursing home operators and long term care providers implemented risk retention strategies seeking safe harbor from an increasingly erratic and unpredictable insurance marketplace. Though some instituted large deductible programs and various forms of hastened self insurance plans, others sought long term solutions through captives, group captives and risk retention groups. In return for their strategic investment of capital and human resources, these long term care organizations gained more than just stability and consistency of coverage – they gained an education in the world of loss prevention and risk management to help provide continuity and buttress the volatility of the insurance cycle.

Undoubtedly, those who developed their sense of capital preservation using proprietary data and risk management techniques discovered the power of data analytics and predictive modeling, or risk analytics. By tapping into the full potential of available data, much of which is collected in the routine business of providing care, these long term care liability organizations revolutionized their approach to risk management and are enjoying tremendous success.

At the other end of the spectrum, however, some insuring organizations have resisted change, clung to traditional risk management practices and have comparatively fallen behind. For these organizations, opportunity knocks. It’s time to recognize the power and predictive ability of information and apply the lessons learned by their pioneering counterparts that have already forged ahead.

Risk Analytics – Defined

While every organization strives to manage risks more effectively, many rely heavily on infrequent (annual/bi-annual) on-site assessments to evaluate risk. Not only is this a costly mechanism, it’s far less effective. Consider a captive that insures two hundred nursing home locations and evaluates risk through site visits carried out by a team of nurses. Regardless of skill, each nurse invokes some element of subjectivity rendering the outcomes inconsistent. Furthermore, the mere presence of an on-site visit modifies facility behavior making any findings dubious. Moreover, on-site visits are subject to time constraints, which drastically limit the quantity of information reviewed.

Data analytics, on the other hand, is a quantitative methodology of examining data for the purpose of drawing conclusions about the information. When coupled with predictive modeling, it has the potential to answer very important questions about risk. The analytic process essentially breaks down the very complex system of providing care into individual predictors.  These predictors help drill-down and recognize that not all quality problems are created equal: some apparent weaknesses are mitigated by compensating factors while other weaknesses, in combination with other facts, exacerbate risk. Larger and more specific pools of data offer higher predictive precision. Fortunately, in the long term care industry, there is no shortage of very specific data.   

Virtually all long term care facilities, for the past 20 years, have faithfully submitted billions of data elements and various quality measures to the Centers of Medicare and Medicaid Services (CMS). When combined with additional sources of data for thousands of facilities such as care processes, deficiencies, fire safety, staffing to acuity ratios and survey results, the potential for risk prediction is obvious. After the data is cleaned, transformed, indexed, benchmarked and passed through highly sophisticated predictive models, the potential is fully realized in its ability to answer important questions such as “which facility is most likely to experience a big claim?”  

Unlimited Opportunities for Improved Risk Management

Incorporating risk analytics into any risk management program allows for more confident, timely and accurate analysis of risks – and fewer surprises! In a broad context, these tools equip an organization with the ability to connect the dots between underwriting, risk management and loss evaluation. Every phase of the continuum relies on the same validated data source. As a result, a deeper understanding of the relationship between quality and risk emerges. In more specific terms, opportunities include the following:

• Immediate adjustment for risk – Long term care is a highly dynamic risk environment, as is most healthcare risk. Each facility’s level of risk can progress rapidly with changes to resident acuity, staffing ratios, agency dependency and others. For example, in today’s long term care environment, providers are admitting more short term, higher acuity residents. Without a corresponding adjustment to staffing ratios, exposure results. Exposure that must be addressed before it’s too late.
• Focus risk management dollars – The provision of healthcare services goes hand in hand with the issue of allocating limited resources, and long term care is no exception. Through predictive analytics an organization can target risk management resources more effectively, gain the largest impact and achieve better results.
• Put claims in context – When claims occur, it’s important to place each claim in a proper context. Assume two facilities face a claim for a resident fall. The context of the claims may be quite different. One facility’s data may demonstrate that the fall was an anomaly that occurred despite having excellent protocols and risk management practices. While the other facility’s data may reveal a true risk management weakness. Traditional insurance would treat these facilities equally; however, risk analytics provides the requisite facts to guide decisions about future risks and the wisdom to determine which claims to defend and which to settle.
• Cut through the politics – A data driven emphasis on risk eliminates bias and reduces conflict. When the “data” (versus a “person”) reveals a risk management weakness it’s less offensive and less emotional.
• Allocate premiums more fairly – A risk management program founded on evidence based data has the ability to more precisely allocate premium dollars according to risk. This opportunity is especially important for captives who assume risk for a broad spectrum of providers.
• Reinsurance negations – Traditional underwriting providers, such as Lloyds, have taken notice that some long term care providers have made great strides in risk analytics which is increasing underwriting comfort in the reinsurance/excess coverage arena. But, negotiating for the best premiums will require data that demonstrates a sound risk management program. 
• Defense strategies – In the event that a claim requires a defense, an organization with essential risk management data is prepared to defend. Too often, claims are being adjudicated long after the personnel involved are gone, charts are incomplete and paperwork is missing. With a risk management program based on data analytics, the information needed is quickly accessible.

When conducted properly, risk analytics equips any risk bearing entity with the tools needed to underwrite effectively, price accordingly, monitor risk timely and target risk management dollars wisely. Compared to standard industry techniques and practices, these tools are proven to deliver.

Tools That Deliver – Success Story   

Primary users of this data driven approach, which integrates risk analysis, risk management and loss control with predictive models, includes operators with large self-insured retentions (SIR) and various insurance groups including Lloyds of London syndicates and OneBeacon; underwriting organizations such as CFC Underwriting (Lloyds programs); and wholesale distributors such as AmWins, Highland Risk and London American.

Collectively, these organizations rely upon predictive modeling and risk analytics for establishing  premiums, reinsurance, acquisition and sales costs, loss forecasts, proactive risk management and other components involved in building terms and conditions issued to benefit an insured. Most often, the insured is a U.S. based long term care (skilled nursing, assisted living, CCRC) provider. Successful pricing of policies is considered intellectual property and when used strategically will generate larger than average underwriting profits for these insurance organizations.

 Overall, program success is driven by more consistent and cost effective underwriting, applying risk management dollars to the area of largest exposure (typically two-thirds of losses are attributed to twenty percent of the facilities in a captive) and proactively managing loss ratios achieving results that are better than half the industry norm. Additionally, these advanced underwriting tools can lead to increased market share at the expense of their competition by avoiding the high risk insureds that have simply been lucky while rewarding the unlucky with a carefully-priced competitive offering.    

The success of these tools in the long term care arena should leave many in the industry wondering what other areas of healthcare professional liability could gain from a similar data-based approach such as physicians and hospitals groups. Comparable benefits could be realized in these market segments by finding similar risk data points and developing a parallel profiling system to determine corresponding risk predictors.

Show Me the Money

Some captives may consider paying out sixty cents per dollar as a success – well, it’s not. The fact is, these high payout ratios equate to billions of dollars: dollars that could be returned to facilities and invested in infrastructure, used in operations or spent to improve risk management programs. True success can not be claimed until loss ratios are managed down to their lowest possible level. And that is what data analytics, predictive modeling and risk analytics can offer organizations willing to harness the power of knowledge and technology – an opportunity to manage loss ratios to their lowest possible level.

For more information please contact :

Paul R Marshall
937.475.1071
marshallpr@gmail.com
www.linkedin.com/in/paulrmarshall

Published Capive Review CR Fall 2009.  www.captivereview.com

Risk analytics is revolutionizing the processes and tools employed by insurers to more quickly and accurately market, price and underwrite their products. Ad­ditionally, these tools have the potential to enhance an insurer’s ability to manage claims more effectively. With improved management, insurers can lower overall costs, reduce premiums, reduce claims, gain competitive advantage and, ultimately, increase their market share. Through advances in technol­ogy and data availability, many insurers are already benefiting from the use of data analytics and predictive modeling capabilities to better understand and identify risk.

What is risk analytics?

Risk analytics is a quantitative method of examining data for the purpose of draw­ing conclusions about the information. When coupled with predictive modeling, risk analytics has the potential to answer very important questions about risk. The critical ingredient is data, which must be gathered, scrubbed, aligned, indexed and benchmarked to be useful in build­ing predictive models. Ultimately, the potential of data analytics is realized when the user gains rapid access to answers for important questions such as, “What is the likelihood that this incident will lead to a million-dollar settlement?”

To illustrate the value of risk analytics, consider a healthcare facility that experi­ences a patient fatality due to a fall. Too often, if that claim is not handled prop­erly, the facility can expect to payout the maximum allowed according to policy limits. However, if the claim is handled properly (for example, timely and pro-actively), the claim can be settled out of court for a fraction of that cost. Unfortunately, healthcare systems can generate upwards of 100 incidents per month, and available claims management resources do not extend beyond the constraints of full blown lawsuits. Consequently, inci­dent reports pile up and obscure the real risks. With risk analytics, incidents can be rapidly and cost-effectively analyzed. Then, “real” risk is identified sooner, triaged appropriately, and dealt with proactively.

Logistically, it is a simple process. The unique circumstances of each incident are captured. The situation is analyzed in the context of like-facilities and resident health. This provides the immediate insight necessary to gain perspective and focus resources accordingly. Within the long-term care industry, public access to Online Survey, Certification and Report­ing (OSCAR) data, Minimum Data Set (MDS) assessments and survey results provides for highly effective predictive modeling opportunities.

Advantages of analytics

While a few risk managers are truly gifted with a sixth sense for risk, most are not. With access to risk analytics, all claims managers can be equally and consistently equipped with the tools and potential to greatly improve their performance. Risk analytics provides the critical information needed to move forward with confidence and target at­tention on the high-risk situations that will benefit most from a proactive stance. In addition to providing a virtual red, yellow or green flashing light on each incident, risk analytics can provide a number of other advantages:

1. Accelerate the acquisition of knowl­edge – “The quicker we can investi­gate, understand and evaluate the claim, the quicker we can reach our decision points. Does the claim have value? What is the potential value of this claim? Are we comfortable defending it at trial? Risk analytics helps us tailor the investigations because if we can identify prob­lem areas, we can dig deeper and perform more pointed and specific investigations. Any time we have access to critical information about the facility, staffing levels, potential problem areas and so forth, early in the claim handling process, we improve our chances of being able to evaluate the claim accurately and resolve it in an optimal fashion,” explains Paul Hamlin, founder and president of Hamlin and Burton Liability Management and expert in the field of claims management.

2. Place claims into proper context – Not all falls are created equal. The actual context of individual claims may be quite different. For example, one facility’s data may demonstrate that the fall was an anomaly that occurred despite having excellent protocols and risk-management practices, while the other facility’s data may reveal a true risk manage­ment weakness. This knowledge advantage offers rapid discernment of the overall quality of care provid­ed at one facility versus another and can chart the course for improved negotiations with plaintiffs and, ideally, lower overall settlements. “Risk analytics has allowed us to focus on critical claims by gaining rapid clinical and analytical insight to help us put claims into a broader context and obtain more favorable outcomes,” notes Jonathan Swann, underwriter, CareSurance Nurs­ing Home Program at Lloyd’s. At a minimum, the information guides claims professionals and gives them the wisdom to determine which claims to defend and which to settle, fast.

3. Lower claims administration costs – Claims managers who research files in the traditional approach will spend 25-50% more time complet­ing the legwork necessary to obtain the data necessary to evaluate a claim. “Most data in the compre­hensive risk analysis we could find out on our own, but it would take a tremendous amount of leg work and would be done slowly, expen­sively and inconsistently,” says Paul Hamlin. If risk analytics can save an insurer just 5% of total claims man­agement expense, that impact could easily be converted into a significant competitive advantage.

4. Detect fraud – Claims fraud is a multi-billion dollar problem and healthcare fraud is the major contributor. While the reasons for this are many, the sad truth is that insurers can only investigate a small percentage of suspected cases due to limited resources. Consequently, it is critical to rapidly evaluate every file to identify the likelihood of fraud and then focus investiga­tive resources accordingly. Failure to identify fraud raises claims cost, which increases premiums for all insured. For insurers, even a minor improvement in the ability to detect fraud can generate a significant return on investment.

5. Triage claims – By identifying underlying problems quicker, risk analytics gives the insurer the clarity to identify real risk and fast-track the potentially large settlements at a much earlier stage in the process. These cases, which are more likely to evolve into large settlements, are instantly identified, designated a priority and handled appropriately.

As the application of risk analytics becomes more widespread in the claims handling arena, new uses of these tools are expected. One application cur­rently being explored is with causation defense. To illustrate, assume a nursing home resident is afflicted with severe skin breakdown that leads to an amputa­tion of a lower extremity. Such a case is likely to result in a lawsuit. To prepare for that possibility, the insurer can utilize predictive modeling to develop an ef­fective defense strategy. By processing the detailed circumstances of the case through a sophisticated data analysis, the insurer can look at the medical condition of the resident and infer what prob­ability existed that the resident would have experienced severe skin breakdown regardless of the facility’s quality of care or resident specific care plan.

Proceed with caution

Any statically driven predictive modeling tool has limitations and the old adage, “garbage in, garbage out” will always apply. While risk analytics and predictive modeling have tremendous advantages to offer insurers and risk management organizations, the ulti­mate value is derived when the experts interpret the information correctly and make the right decisions. After all, there are many variables that go into each and every case that ultimately determine how it is settled. Once a case proceeds to court, the deciding factor is the six or eight people in the jury box. Just how they will decide is extraordinarily unpredictable. “There are precious few variables that exist between the cases we win in trial and the ones we lose in trial,” says Paul Hamlin.

Timing is everything

With the passage of time, the cost to settle any case may increase exponen­tially. Risk analytics and predictive modeling provide the insurer and the defense team with rapid access to the information needed to manage incidents proactively, triage claims effectively and settle claims before that critical window of opportunity closes. It is simply an industry best practice available for any insurer ready to harness the power of knowledge and technology and apply the old adage: “An ounce of prevention is worth a pound of cure”.

“Risk analytics provide the insurer with rapid access to the information needed to manage incidents proactively”

Mary Chmielowiec is the executive vice-president for insurance and Paul Marshall is direc­tor of insurance business develop­ment at PointRight. PointRight, formerly known as LTCQ, is based in Lexington, Massachusetts, and analyses liability risks for the insurance industry using pre­dictive models and underwriting deci­sion support that enable risk bearing organizations to more quickly and accurately analyze and manage the risks that shape their bottom line.

Published CaptiveReview Fall 2009

PointRight analyzes liability risks for the insurance industry using predictive models and underwriting decision support that enable insurance / risk bearing organizations to more quickly and accurately analyze and manage the risks that shape their bottom line. When coverage is bound the same model-based risk analysis helps focus risk management and optimize the use of risk management dollars using our Web-based services and account management. When a claim is made, PointRight provides an evaluation that leverages the model-based risk analysis putting claims into context, assisting in more favorable outcomes.

For additional information please contact:

Paul R Marshall
937.475.1071
marshallpr@gmail.com
www.linkedin.com/in/paulrmarshall

Extracted from Blink, by Malcolm Gladwell, as a discussion and action document for the Medical and Healthcare Leadership Group in The Leadership Hub. www.TheLeadershipHub.com

It’s a Leadership Hub recommended read for improving your decision-making.

Why doctors are sued. And how to predict which ones will be.

In this extract from the book ‘Blink: The Power of Thinking without Thinking’, Malcolm Gladwell explains how mistakes are not the reason that doctors are sued by their patients.

Let’s take the concept of thin-slicing one step further. Imagine you work for an insurance company that sells doctors medical malpractice protection. Your boss asks you to figure out for accounting reasons who, among all the physicians covered by the company, is most likely to be sued. Once again, you are given two choices. The first is to examine the physicians’ training and credentials and then analyze their records to see how many errors they’ve made over the past few years. The other option is to listen in on very brief snippets of conversation between each doctor and his or her patients.

By now you are expecting me to say the second option is the best one. You’re right, and here’s why. Believe it or not, the risk of being sued for malpractice has very little to do with how many mistakes a doctor makes. Analyses of malpractice lawsuits show that there are highly skilled doctors who get sued a lot and doctors who make lots of mistakes and never get sued. At the same time, the over-whelming number of people who suffer an injury due to the negligence of a doctor never file a malpractice suit at all. In other words, patients don’t file lawsuits because they’ve been harmed by shoddy medical care. Patients file lawsuits because they’ve been harmed by shoddy medical care and something else happens to them.

What is that something else? It’s how they were treated, on a personal level, by their doctor. What comes up again and again in malpractice cases is that patients say they were rushed or ignored or treated poorly.

…..1 of 4

….2 of 4

“People just don’t sue doctors they like,” is how Alice Burkin, a leading medical malpractice leader, puts it. “In all the years I’ve been in this business, I’ve never had a potential client walk in and say, ‘I really like this doctor, and I feel terrible about doing it, but I want to sue him.’ We’ve had people come in saying they want to sue some specialist, and we’ll say,’ We don’t think that doctor was negligent. We think it’s your primary care doctor who was at fault.’ And the client will say, ‘I don’t care what she did, I love her, and I’m not suing her.’”

Burkin once had a client who had a breast tumour that wasn’t spotted until it metastasized, and she wanted to sue her internist for the delayed diagnosis. In fact, it was her radiologist who was potentially at fault. But the client was adamant. She wanted to sue the internist. “In our first meeting, she told me she hated this doctor because she never took the time to talk to her and never asked about her other symptoms,” Burkin said. “‘She never looked at me as a whole person’, the patient told us… When a patient has a bad medical result, the doctor has to take the time to explain what happened, and to answer the patient’s questions – to treat him like a human being. The doctors who don’t are the ones who get sued.” It isn’t necessary, then, to know much about how a surgeon operates in order to know his likelihood of being sued. What you need to understand is the relationship between that doctor and his patients.

Recently the medical researcher Wendy Levinson recorded hundreds of conversations between a group of physicians and their patients. Roughly half of the doctors had never been sued. The other half had been sued at least twice, and Levinson found that just on the basis of those conversations, she could find clear differences between the two groups. The surgeons who had never been sued spent more than three minutes longer with each patient than those who had been sued did (18.3 minutes versus 15 minutes).

They were more likely to make “orienting” comments, such as “First I’ll examine you, and then we will talk the problem over” or “I will leave time for your questions” – which help patients get a sense of what the visit is supposed to accomplish and when they ought to ask questions. They were more likely to engage in active listening, saying things such as “Go on, tell me more about that,” and they were far more likely to laugh and be funny during the visit. Interestingly, there was no difference in the amount or quality of information they gave their patients; they didn’t provide more details about medication of the patient’s condition. The difference was entirely in how they talked to their patients.

…..2 of 4

….3 of 4

It’s possible, in fact, to take this analysis even further. The psychologist Nalini Ambady listened to Levinson’s tapes, zeroing in on the conversations that had been recorded between just surgeons and their patients. For each surgeon, she picked two patient conversations. Then, from each conversation, she selected two ten-second clips of the doctor talking, so her slice was a total of forty seconds. Finally, she “content-filtered” the slices, which means she removed the high-frequency sounds from speech that enable us to recognize individual words. What’s left after content-filtering is a kind of garble that preserves intonation, pitch and rhythm but erases content. Using that slice – and that slice alone – Ambady did a Gottman-style analysis. She had judges rate the slices of garble for such qualities as warmth, hostility, dominance, and anxiousness, and she found that by using only those ratings, she could predict which surgeons got sued and which ones didn’t.

Ambady says that she and her colleagues were “totally stunned by the results,” and it’s not hard to understand why. The judges knew nothing about the skill level of the surgeons. They didn’t know how experienced they were, what kind of training they had, or what kind of procedures they tended to do. They didn’t even know what the doctors were saying to their patients. All they were using for their prediction was their analysis of the surgeon’s tone of voice. In fact, it was even more basic than that: if the surgeon’s voice was judged to sound dominant, the surgeon tended to be in the sued group. If the voice sounded less dominant and more concerned, the surgeon tended to be in the non-sued group. Could there be a thinner slice? Malpractice sounds like one of those infinitely complicated and multidimensional problems. But in the end it comes down to a matter of respect, and the simplest way that respect is communicated is through tone of voice, and the most corrosive tone of voice that a doctor can assume is a dominant tone. Did Ambady need to sample the entire history of a patient and doctor to pick up on that tone? No, because a medical consultation is a lot like one of Gottman’s conflict discussions or a student’s dorm room. It’s one of those situations where the signature comes through loud and clear.

Next time you meet a doctor, and you sit down in his office and he starts to talk to you, if you have the sense that he isn’t listening to you, that he’s talking down to you, and that he isn’t treating you with respect, listen to that feeling. You have thin-sliced him and found him wanting.”

(Or HER, surely, Malcolm).

Recommended Action: Buy the book Blink, if you haven’t read it already, to become more aware of how ‘thin slicing’ contributes to your decision-making. If you help to run a medical establishment, what action can you take based on the information in Blink to help take the lead on this issue – reducing the number of legal cases that your medical people generate inadvertently?

You can buy Blink at

www.amazon.co.uk

www.amazon.com

www.amazon.de

www.amazon.fr

www.amazon.jp

www.amazon.ca

and other book sale sites and all good book stores . It’s a Leadership Hub recommended read for improving your decision-making.

http://www.insurancenetworking.com/news/10501-1.html

February 11, 2008

Seattle — In recent years, increasing attention has been paid to the role of lifestyle-based analytics (LBA) in health insurance underwriting, according to Seattle-based actuarial and consulting firm Milliman Inc. In some instances, proponents of LBA made overly optimistic claims about the use of consumer data as a predictor in the underwriting process. Milliman’s Jonathan Shreve, author of the white paper “Lifestyle-based Analytics: A Practical Guide”, examined the appropriate and effective use of LBA as an advance in risk selection and classification.

Medical studies have shown that lifestyle characteristics and habits have a clear impact on disease prevalence. LBA uses information about lifestyle to enhance the risk classification system for relevant conditions. This information comes from data aggregators, which collect information from a variety of sources. Statistics, when properly interpreted, can enable underwriters to identify relationships between lifestyle information and prevalence of various diseases, which may result in a strong correlation with expected claims, Milliman reports. Hence, LBA can help differentiate high-cost and low-cost insurance plan members.

“For some of the correlations we have found, we believe there is a clear cause and effect—people who exercise more have fewer cardiovascular problems, and people who live alone have greater rates of depression,” Shreve says. “Sometimes, the lifestyle data may reflect the condition, rather than the other way around.”

LBA is increasingly viewed as a high-quality advance in the art of risk selection, according to Milliman. It does not pick out specific individuals in a group who definitely have a condition, thus limiting its application in individual and disease management applications. Nonetheless, LBA does identify meaningful differences from one person to another, and from one group to another, in the likelihood of experiencing or developing adverse conditions.

It now appears likely that LBA could be used in conjunction with other risk classification tools to produce underwriting results with a higher degree of accuracy.

Source: PR Newswire

For more information on related topics, visit the following channels:

Risk Management

Underwriting

http://www.insurancenetworking.com/news/insurance_technology_property_casualty_actuaries_predictive_analytics-12137-1.html

By INN Editorial Staff
April 6, 2009

Property/casualty insurance actuaries should not be afraid to try alternative approaches to data mining as part of the predictive modeling process, lawyer and economist told the Casualty Actuarial Society (CAS) Ratemaking Seminar in Las Vegas.

Ayres, author of the book “Super Crunchers,” noted in a keynote address that non-traditional approaches to predictive analysis, such as neural networks, might have a role for actuaries, subject to regulatory constraints.

“You should be thinking about trying alternative approaches, even if your central approach is general linear regression,” he said at the seminar. “Every once in a while I would try a neural network and see if your traditional approach is robust to alternative specifications.”

He went on to explain that as the size of datasets has increased, neural networks may be able to estimate many more parameters than traditionally accommodated by linear regression.

He also cited the example of Epagogix Ltd, a UK-based company specializing in artificial intelligence, and its ability to forecast the box office success of movies by using a neural network model.

According to Ayers, a studio gave Epagogix the scripts for nine movies and asked the company to make their predictions on the box office revenues before a single frame was shot. Independently the studio also made their predictions.

While Epagogix wasn’t perfect, it made accurate predictions on about six of the nine movie scripts—twice the accuracy of the studio.

“If Epagogix can be successful at number-crunching on a very high degree of difficulty question with relatively little data, it shouldn’t be surprising that people in this room can do a much better job on trying to score out some insurance risk when we have much larger data sets,” Ayers said.

Ayres observed that a chic approach in certain number-crunching areas is to draw on the power of the collective wisdom of crowds to make a prediction. However, he suggested that true wisdom lies in mining a company’s historical data.

Ayres went on to discuss a number crunching research project he co-authored on Lojack, a hidden radio transmitter device used for retrieving stolen vehicles.

“This is central to insurance,” he said. “The theory we wanted to test is about hidden precaution. The big difference between Lojack and many traditional car alarms is that Lojack is hidden to a potential thief.”

The idea behind the research, according to Ayers, was that hidden precautions could have a positive influence in reducing theft in a city because the thieves would become scared—they wouldn’t know which vehicles were Lojack-equipped.

“We looked at data from 1981 to 1994 in 60 large cities,” Ayres said. “After Lojack comes in there is a substantial downturn in crime. The bottom line is that we found the social benefit of Lojack was 15 times greater than the costs of putting the device in the vehicle.”

Most of that benefit was external to the owner of the Lojack, however. “Most of the benefit isn’t that it reduces your chance of getting a theft loss but that it reduces the loss on auto theft for other people in that city who don’t have Lojack,” he said.

According to Ayres, the findings suggest that insurer premium discounts to car owners who install Lojack are far less generous than the apparent social benefit. Yet Lojack appears to be one of the most cost-effective crime reduction approaches.

For more information on related topics, visit the following channels:

• Claims
• Data Management
• Enterprise Technologies
• Insurance Network

©2009 Insurance Networking News and SourceMedia, Inc. All rights reserved. SourceMedia is an Investcorp company. Use, duplication, or sale of this service, or data contained herein, is strictly prohibited.

http://www.insurancenetworking.com/issues/2008_60/insurance_technology_reinsurance_data_standards_risk_management-12106-1.html

Ed McKinley
April 1, 2009

Property/casualty reinsurers suddenly saw the need for catastrophe modeling and risk-concentration data in 1992, after Hurricane Andrew pulverized parts of the Bahamas, South Florida and the Louisiana coast.

Desire for data intensified among casualty reinsurers after the 9/11 terrorist attacks. Life and health reinsurers have begun thirsting for better data ever since the AIDS epidemic broke out in the 1980s, and finally became proactive in seeking better data in the last year or so, sources say.

Not surprisingly, with claims soaring, no reinsurer wants to settle for aggregated data from primary carriers, says Karlyn Carnahan, a principal at Novarica, a New York-based consulting company. Reinsurance companies want to know the exact locations of the risk-down to the street addresses, Carnahan says. They want to know the precise nature of the risk, such as whether a building is brick- or wood-framed, for example. And they want to know the concentration of the risk, such as the number of employees working in a high-rise.

A reinsurer with too much risk in too small a geographic area or poorly understood risk could face gigantic losses from a single event, says Karen Pauli, research director in the insurance practice at TowerGroup Inc., a Needham, Mass.-based consulting company.

Karlyn Carnahan

IN-HOUSE DATA TOOLS

These concerns have helped shape operations at Minneapolis-based ING North America Reinsurance Corp.

In 2003, ING Re created a risk concentration database in-house. The company uses the database to determine risk exposure by address or postal code, or at the city or regional level, and then compare the exposure to risk-management standards, says Rick Becchetti, the company’s senior risk management analyst. Cat modeling software from an outside vendor, combined with the database, helps determine potential losses from terror attacks, earthquakes or other disasters, he says.

ING Re uses the information to create risk concentration analysis reports for clients. The reinsurer considers the reports a value-added service that differentiates the company from competitors, Becchetti says. Typically, the reports include color-coded maps that track the risk concentration and cat modeling results of the client’s exposure, he says.

Yet, primary carriers handle data with varying degrees of sophistication, says Melissa Tilford, VP of marketing and underwriting for ING Re. “The larger writers typically are farther along in awareness,” she says, adding that the differences in capabilities among primary carries derive mostly from their willingness to bear the cost and the commitment from top management.

To improve that performance among primary carriers, underwriters or account managers provide data to Becchetti, who says he “plows through it, filling in the gaps wherever necessary.”

He then brings the information into the risk concentration database to check for violations of ING Re’s parameters – too much exposure in a single building in Lower Manhattan, for example.

“If there are any violations, the president of our company gets involved,” Becchetti says. “The president’s going to approve violations with certain restrictions, and then that information goes into the database.”

Originally, ING Reinsurance hoped its primary carriers would use the risk-concentration database themselves. Instead, clients have indicated they prefer the reinsurer to handle the entire risk-concentration process. Today, ING Reinsurance operates the system for internal reporting, and as a service to primary carriers.

However, clients take a “hands-on” approach to another service. Primary carriers with treaty agreements are using ING Re’s Wingman, an in-house online system, to get quotes and bind individual policies 24 hours a day, Tilford says. Wingman became available in March 2007 for workers’ compensation policies associated with corporate aircraft, she says.

When using the system, primary carriers answer questions about the policy, and then click a button and get a price based on the information specific to the policy, the terms of the treaty and other data, Tilford says.

Wingman also handles renewals, cancellations and mid-term changes, she notes, while allowing users to test varying levels of coverage.

ING Re learned to make the system easy for primary carriers to use, without sacrificing the data needed to understand the risk, Tilford notes. “If we had made it too cumbersome people wouldn’t want to use it,” she says.

FORMING PARTNERSHIPS

Developing a system that primary carriers want to use can transform reinsurers from low-bid contractors into valued partners, according to another reinsurer.

Charlotte, N.C.-based Transamerica Reinsurance Co. got more than it bargained for when it commissioned Edison, N.J.-based MajescoMastek to create an online system that streamlines data collection and underwriting on the life side of the reinsurance business.

The resulting technology not only reduces the underwriting workload more than anticipated, but also brought the unexpected benefit of refining all-important mortality calculations, says David Dorans, VP of product consulting and development for Transamerica Re.

“We went in there trying to save nickels and dimes, and instead found we were saving dollars,” Dorans says, noting the system allocates much of the savings to the primary insurers.

Operational costs account for about 8% of each dollar in premiums, while mortality accounts for 60%, says Erik Stockwell, VP and GM of life and annuities for MajescoMastek. Dorans places mortality as high as 70%.

Stockwell quotes the CEO of a large reinsurer as saying that a 1% improvement in mortality results can increase corporate ROI by 3%. “It’s a huge swing just by moving this dial just a little bit,” he says.

With primary carriers achieving average margins of 3% to 4%, a 1% increase in profitability on the percent of premium brings a 25% increase in profitability, says Dorans. “It’s where the money is,” he says. “If we get the mortality right, everything else is going to go swimmingly. If the mortality is wrong, it doesn’t matter what else went right – you’re going to have a real problem on your hands.”

The system’s improvements in mortality results didn’t become apparent to Transamerica Reinsurance and MajescoMastek until three years ago, which was more than three years after the launched the system, says Dorans.

ERRORS AND EXCEPTIONS

The system, dubbed the Mortality Management Solution by Majesco-Mastek, rids underwriters at the primary carriers of the job of sifting though every bit of information collected for policies. The system sorts through the data, and presents underwriters with only the information that falls outside established parameters and warrants personal review.

Relieved of the need to review data the system automatically approves, one primary carrier reports its underwriters have become 40% more productive, Dorans says. That means a lot because of the scarcity of good, experienced underwriters, says MajescoMastek’s Stockwell.

The screening also eliminates errors because underwriters are no longer “buried under a mountain of meaningless data,” says Dorans. And the system’s auditing process also catches potentially costly mistakes.

Anytime an underwriter makes a decision that differs from the system’s usual approach, the system alerts the primary insurer and Transamerica Reinsurance. That double-checking amounts to an audit of 100% of exceptions, Dorans says, noting that the insurance industry usually audits fewer than 1% of an underwriter’s cases.

“There’s a significant amount of human error in the traditional environment that a system like this can catch, and it can lead to substantial savings down the road – saving half a million or a million dollars,” Dorans says.

Primary insurers that work with Transamerica Reinsurance can choose to use part of the system, but Dorans recommends taking on the entire offering. Many insurers are still converting paper records to digital images when they could take the additional step of converting the records to data ready for manipulation.

Some primary insurers hesitate to invest in the changes required to their basic IT systems to accommodate the system, Dorans acknowledges. Only six primary carriers, less 10% of the reinsurers’ clients, have fully committed to the system, he says. Yet, two more primary insurers appear likely to sign up, according to Dorans, and Transamerica Reinsurance and MajescoMastek can now bring newcomers up to speed within four months, Stockwell says.

Ed McKinley is a freelance business writer based in Chicago.

(c) 2009 Insurance Networking News and SourceMedia, Inc. All Rights Reserved.

Technology and Standards

As reinsurers and reinsurance brokers have become more acutely aware of the importance of amassing, interpreting and transferring data, the underlying technology and standards have been evolving, too, says Peter Marotta, enterprise data administrator for ISO Inc., a Jersey City, N.J.-based provider of risk information.

Cat modeling, data analytics and geographic information systems, or GIS, are the overlapping methodologies that provide and feed off of better data, says Marotta. Most companies turn to vendors for cat modeling instead of developing the software in-house, he says, adding that many insurers buy more than one system so they can compare the results. Boston-based AIR WorldWide Corp., a company affiliated with ISO, began offering cat modeling software in 1987, when its founder saw no single company had broad enough experience to single-handedly understand hurricanes, earthquakes, terror attacks and other disasters, says Bill Churney, VP of business development at AIR Worldwide.

Better data and better analysis of the data matter most when companies can exchange that information well. That’s where standards can help. The two-fold Reinsurance Large Commercial Standard helps companies speak the same “language” when exchanging data, says Lloyd Chumbley, VP for standards at Pearl River, N.Y.-based ACORD.

On one hand, the standards specify consistent technical parameters, making sure, for example, that two trading partners use the same set of Web services definitions. On the other, the standards define business terms so that all companies define a particular roofing material the same way.

ACORD updates the standards as technology evolves and the industry’s understanding of risk improves, Chumbley says. “After 9/11, for instance, it became very important to understand not just the location of a risk, but what floor they’re on,” says Chumbley. “The technology side tends to be more stable.”

Companies tend to adopt the standards when they update IT infrastructure, or when they start working with new trading partners that are committed to the standards process, Chumbley says, noting that “no one goes out and updates their systems just to meet standards.”

(c) 2009 Insurance Networking News and SourceMedia, Inc. All Rights Reserved.

For more information on related topics, visit the following channels:

• Data Management
• Enterprise Technologies
• Insurance Network
• Risk Management

©2009 Insurance Networking News and SourceMedia, Inc. All rights reserved. SourceMedia is an Investcorp company. Use, duplication, or sale of this service, or data contained herein, is strictly prohibited.

Posted using ShareThis

Twitter can be a great space for building community around your membership-based organization, whether you work for a professional society, trade association or a cause-related nonprofit.

Here’s a quick eight-step rundown of how to set up a Twitter account for your “.org”. This isn’t the only way, of course. But if you are starting from scratch, this is what we’re finding works the best.

1. Set up a main “umbrella” account for the organization – e.g. @ORGtweets or just @ORG (”ORG” being whatever your acronym is).

Why? So people can find you easily. In the description, put in a nutshell what the organization does. A mission statement in under 140 characters, for example. (Be pithy – people like that. There are lots of other places you can be boring). For the website link field of the profile, create a Twitter landing page on your website which says, “Welcome to the Twitter page for [ORG]! We’re glad you’re here. Here’s what we’re all about. Here are some of the things we tweet about. And here are our team members, should you be interested in following them too.” Then list your staff on Twitter as per #2.

2. Give your staff their own individual accounts – e.g. ORG_Bob, Maggie@ORG, etc. If you have several staffers already on Twitter with their own followers, allow them to use their accounts for tweeting on your behalf, assuming they are willing to do that.

Why? because people want to see individual people representing their organizations. There can be backlash when that doesn’t happen. Presumably each staffer will have their own personality, their own things they like to tweet about personally and professionally, and they will also have their own content that they are responsible for – namely PR, or marketing, or advocacy, or publications, or events. Each person will grow their own followers independently – and can share them under the umbrella account as they go along (see #5.)

3. Use a multiple Twitter account client to manage your accounts.

Why? Because it’s MUCH easier than signing in and out of accounts all day. SplitTweet works great, as do HootSuite and CoTweet (currently in private beta).  All these services allow you to monitor multiple accounts at the same time – so your team can choose to tweet something to their individual accounts and the umbrella account, or just to one at a time.  SplitTweet has a cool “track your brand mentions” feature; CoTweet allows you to tag your replies as being from a particular person, and allows you to assign responses to team members. HootSuite has great analytics and intelligent search for Twitter conversations. All three are always improving and evolving as professional Twitter use grows, and there may be a new multiple account application on the scene by the time this post appears, so just find the one that has the functionality you need.

4. Ask each staffer to follow people who tweet regularly about your industry or cause, as well as actively Tweeting members, donors, or other stakeholders.

Why?  Because Twitter is about conversation – and directed conversation can build community.  Find those other interested Tweeps simply by using Twitter search for your particular industry keywords, your organization name mentions, even competitor or sister organization mentions.  Twitter directories like Twellow , We Follow , and Twibs allow you to find people based on tags or types of business.  Each staffer should find their own relevant people to follow, based on their particular interests or area of expertise.  If you have members, or an email list of any kind, use Twitter’s own import function to import emails and find those members already on Twitter (only do a few at a time).  Look for names you recognize, or clearly active Tweeters (you can tell by the number of updates, friends and followers they have).  You only need to find a few key active stakeholders – others will come with them when they start to interact with you.  Ask those you have a good “real life” relationship with to help you spread the word about your new presence on Twitter.

5. Under the umbrella account, periodically retweet items from your team members as well as from their followers/friends.

Why? To show a coherent stream of content where visitors can immediately see what you’re about and that different people speak for you in different ways. If managed well, you can follow relevant public conversations between team members under the umbrella too – conversations that might draw people in to whatever topic you are discussing. Retweeting good stuff by people who are part of your network gives them an ego boost and shows them that it’s not all about you, that you’re paying attention to what they are talking about, that you’re interested in learning from them too.

6. Got an annual conference or big fundraising event? Use hashtags to enable your registrants and anyone else to find you through your event promotion.

Why?  Because the buzz leading up to and during face-to-face events can bring your organization into focus and can attract new people to your cause. Tweet lots of good stuff about how cool your event will be and use and promote a specific designated hashtag for it. Remember to publicize the hashtag in your other promotional materials too. We’re often asked about whether it’s a good idea to set up a separate Twitter account (as opposed to a hashtag) for a conference – this can work too, but a hashtag is more easily found in search, will trend if you have lots of people Tweeting the event, and allows you to differentiate between annual conferences from year to year – e.g. #Tech09 versus #Tech10.  Also, the staff members who have built a following on Twitter will stay visible and won’t be hidden under a conference account. They will each be enabled to add their own personal takes on the conference, by talking about the particular sessions they are attending and the things they care about from their individual (professional) viewpoints.If you set up a new account for each conference, you are basically starting from zero friends and followers each time – and it takes time to build those networks.

7. Bottom line:  Share great content.

Why?  Because great content sparks word of mouth, and word of mouth (you guessed it!) builds community.  Ask each staffer to take responsibility for sharing links with interesting and useful information relevant to their specific areas of expertise.  Encourage them to engage in conversation with their Twitter networks, respond to things other people are tweeting about, retweet links and tweets from people outside your organization as well as your own; don’t be afraid to actually converse about topics of interest. Find champions within your networks to help you spread the word about specific issues. Use your umbrella account to corral it all in a place where people can find it easily. Community builds around shared interests, but only if you nurture it and feed it, which means listening as well as talking.

8. Bonus: Benchmark and measure!

Why?  So you can see how it’s all going and know when it might be necessary to put in a bit more effort or move up to the next level of awesomeness. Benchmark and measure your progress using whatever metrics make the most sense to you.  Number of followers, organization links retweeted, new registrants to your events, etc.  There are lots of specific Twitter analytics apps out there, but measure engagement in other ways too.  Building community online is all about building community offline.

That should be enough to get you started!  Here are a couple places to find examples of associations and nonprofits on Twitter, as well as three related posts from Twitip that dig a bit deeper into Tweeting for organizational use.  Tweet on!