Building Captive Program With Predictive Modelling Published Cayman Captive 2010
- 1. modelling risk predictive modelling ©iStockphoto.com / MichaelShivers mary chmielowiec and paul marshall discuss the building and maintenance of a profitable captive. caym a n c a p t i v e 2 010
- 2. Some captive managers have an unfair advantage. it’s called predictive evident in every step along the continuum from marketing analytics modelling, and it is revolutionising the way risk is both predicted and to claims defence, are most notable in the areas of underwriting and managed. these advanced predictive modelling tools provide captives claims management. with the information necessary to quickly and accurately market, it all starts with underwriting. every account must be analysed to price, underwrite and defend claims more effectively without a major establish the appropriate premium in order for the captive to remain investment of capital. Fortunately, through expert third-party service viable for the long term. For that reason, experienced underwriters providers, the advantages of such sophisticated modelling tools are are critical. However, predictive modelling can provide rapid, critical available to any captive regardless of in-house technological expertise information to assist underwriters in making more accurate, consistent or available capital. Ultimately, these advantages enable captives to and timely decisions about the potential for risk. to analyse just one operate more efficiently, thereby lowering overall costs and creating a account, an underwriter can spend hours. in comparison, a predictive competitive advantage. For any captive that seeks to improve overall modelling system can present an analysis in minutes with equal or performance, it’s time to understand how predictive modelling can make greater accuracy. in fact, predictive modelling systems have been success a reality. tested against traditional underwriting approaches and were found to be five to 10 times more accurate. Know the risks “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 every day, healthcare facilities tweak their operations to remain be almost 100 percent accurate. But, in reality, no underwriter has profitable under changing reimbursement policies and evolving infinite time, and we all make judgement calls and mistakes. predictive regulatory expectations. these tweaks, while important for modelling allows all the people involved, from underwriters and actuaries profitability, often have major implications for underlying risk. to to those who set reserves, to use the insight gleaned from hundreds of illustrate, consider a healthcare facility that is advised by a consultant data points. after the data is entered into predictive modelling software to reduce staff and increase its medicare payer mix in order to systems, highly reliable results provide underwriters with an enhanced improve or maintain profitability. in the short term, this advice may ability to assess risk and set proper premiums and parameters for assist the facility in reaching such goals, but eventually, this decision coverage,” Kramer adds. 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 Defend yourself predictive modelling tools and risk analytics, even subtle changes in in terms of claims management, predictive modelling accelerates staffing and acuity can be revealed. predictive modelling provides the acquisition of knowledge and helps insurers put claims into insurance professionals with the knowledge of any change to risk the proper context. “the quicker we can investigate, understand drivers, thereby allowing the captive to make pre-emptive changes and evaluate the claim, the quicker we can reach our decision and to manage risk more effectively. points,” explains paul Hamlin, founder and president of Hamlin Once a change to a risk driver is detected, the captive can predict and Burton Liability management, inc. and an expert in the field how these changes will affect risk and impact its overall portfolio. of claims management. “Does the claim have value? What is the From that knowledge, the captive gains deep insight into actual risk potential value of this claim? are we comfortable defending it at and can confidently adjust premiums, offer feedback regarding risk trial? Risk analytics helps us tailor the investigations, because if management, and continually monitor before a loss occurs. Without we can identify problem areas, we can dig deeper and perform predictive modelling and risk analysis, after an account is written, more pointed and specific investigations. any time we have access the policy is generally held in status quo with minimal consideration to critical information about the facility, staffing levels, potential to any variation in underlying risk, until it’s too late and a major loss problem areas, etc. early in the claims-handling process, we improve develops. conversely, not all changes to risk are negative. Some our chances of being able to evaluate the claim accurately and operational decisions result in positive effects on risk or provide resolve it in an optimal fashion.” compensating controls. consequently, it takes sophisticated tools to moreover, predictive modelling can chart the course for improved parse out the individual effects (both positive and negative), add them negotiations with plaintiffs and, ideally, lower overall settlements. up correctly in terms of future risk and obtain the most accurate score “predictive modelling and risk analytics have allowed us to focus on critical for that account. claims by gaining rapid clinical and analytical insight to help us put claims into a broader context and obtain more favourable outcomes,” notes Jonathan Swann, underwriter, careSurance nursing Home programme Operational and at Lloyd’s. at a minimum, the information guides claims professionals financial efficiencies 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 “predictive modelling infuses more accuracy and more integrity into 50 percent because, for the industry as a whole, this is acceptable. But the process of predicting losses and identifying the risk drivers that allow predictive modelling is going to shake up the perception of ‘acceptable’ insurers to operate more efficiently,” explains chris Kramer, senior vice loss ratios and has already demonstrated its ability to deliver significantly president of atlas insurance management. these efficiencies, while lower loss ratios. c ay m a n c a p t i v e 2 010
- 3. the end result is improved accuracy and lower claims administration of the association has several nursing homes in the pool and believes costs. claims managers who research files in the traditional approach his facilities deserve a reduced rate. in this instance, the captive can will spend 25 to 50 percent more time completing the legwork use an outside, objective third-party expert and access models that necessary to obtain the data necessary to evaluate a claim. “most quickly evaluate each facility and provide a one-page summary of the data in the comprehensive risk analysis we could find out on our own, comparative risk. but it would take a tremendous amount of legwork and would be done Such analysis can also provide the quantitative information necessary slowly, expensively and inconsistently,” says Hamlin. if predictive to allocate premiums more fairly and more accurately across the modelling and risk analysis can save an insurer just five percent of total captive. essentially, predictive modelling can help eliminate the claims management expense, that could easily be converted into a human and emotional response that naturally occurs in the underwriting significant competitive advantage. 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 it’s flexible—pick and gain a clear advantage. actual data means more than individual choose applications perception/belief.” predictive modelling tools are available for any step along the continuum, including marketing analytics, underwriting, risk Opportunities management and loss mitigation. to illustrate, consider a captive predictive modelling and public data can also be used to target created to meet the insurance needs of nursing homes belonging to preferred risk and steer the acquisition of new business. By taking a particular faith-based association. Let’s assume that the president the offensive position in finding new business that meets a desired set of risk parameters, a tremendous amount of savings can be realised. 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 analysing more than 10 accounts before finding one that is right for the programme. another advantage of predictive modelling 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 organisation 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 modelling helps an organisation be both fiscally and operationally efficient. “predictive modelling consider the evidence to remain competitive in the industry, insurers need to capitalise provides insurance on advances in technology and the growing availability of data professionals with the by implementing data-driven predictive modelling tools. evidence clearly indicates that insurers that embrace the power of sophisticated knowledge of any change predictive modelling tools experience significantly lower loss ratios than industry averages. the reason is simple: by gaining greater to risk drivers, thereby insight into what is happening today in their risk pool, action can be taken to address risk, appropriately price the premium and operate allowing the captive more effectively. to make pre-emptive changes and to manage Mary Chmielowiec is executive vice president for insurance at PointRight Inc. She can be contacted at: mary.chmielowiec@pointright.com risk more effectively.” Paul Marshall is director of insurance business development at PointRight Inc. He can be contacted at: paul.marshall@pointright.com The company website is: www.pointright.com caym a n c a p t i v e 2 010
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