Thanks to a compelling business case and a proven track recordin banking and other industries, a growing number offorward-looking life insurance companies have embraced predictivemodeling and are seeing tangible benefits from initial investmentsin new tools and processes.

To date, predictive modeling in the life insurance industry hasprimarily been considered for underwriting because of the costsavings and increased speed-to-issue. However, it's now gainingtraction in other areas and functions, such as sales and marketingprograms, where the value proposition is similarly clear. If theexperience of the property and casualty (P&C) insurance sectoris any indication, the growing adoption by life insurers shouldcome as no surprise. Indeed, there is a pervasive sense among lifeinsurers that predictive modeling's time has come.

Lessons from the P&C Sector

During the last decade, predictive modeling tools and techniqueshave caused significant shifts in the competitive landscape of theP&C sector, starting with underwriting, where advancedsegmentation has resulted in greater growth and profitability.Because of this success, P&C carriers rolled out predictivemodeling capabilities in other areas of the business. In productmanagement, for example, it has been used in pricing, featureselection and class-plan design. Within claims operations, P&Ccarriers leverage predictive modeling for resource allocation andanti-fraud efforts.

A number of industry observers and analysts have credited thesophisticated use of predictive modeling as the driving forcebehind the impressive market share gains and rapid growthtrajectory of formerly “middle-of-the-pack” carriers. Thealteration of the competitive landscape has confirmed thatpredictive modeling offers both top-line and bottom-line benefits.It is not surprising, then, that predictive modeling has become anindustry-leading practice in underwriting, product management andclaims.

The life insurance sector has been laggard in the use ofpredictive modeling. Yet, leading insurers have begun implementingthe same techniques and have started to realize tangible value fromrelatively modest investments. Effective adoption starts with asense of urgency about taking action supported by a quantifiablebusiness case and targeted applications based on sound actuarialand business principles.

The Business Case

Within life insurance underwriting processes, there has been aconcentrated push during the last several years to produce moreimmediate underwriting decisions for as many policies as possible,while maintaining a competitive product offering and profitablecustomer base. Traditionally, in order to qualify for the lowestpossible premium, potential customers undergo a fully underwrittenapplication process, which involves medical tests, such as invasiveblood and fluid tests, EKG scans or full medical exams, based oninsurance amount, and risk factors such as age. Statements fromattending physicians may also be required before decisions can bereached. The testing is typically expensive and time-consuming,sometimes taking weeks or months to produce underwriting decisions.Further, the long, traditional process has been a barrier topurchase for many consumers, with higher non-taken rates for thosecustomers who are finally offered policies. Companies not wantingto utilize a fully underwritten application process have so farbeen limited to simplified-issue and guaranteed-issue underwritingguidelines. However, because the premiums associated with theseguidelines are higher than the best premiums offered in themarketplace, most companies are at a competitive disadvantagerelative to their peer group. That is why this option is just notfeasible for many insurers.

To differentiate themselves from their competitors, someinsurers turned to automated underwriting as a means to reducecosts and streamline these processes. Straight-through processingand electronic interfaces for policy applications eliminate theneed for costly and error-prone manual data entry. Furthermore,automated underwriting can utilize electronic applications with“drill-down” capabilities. For many applicants, this means fewerquestions and a faster application process. Workflow tools andbusiness rules further streamline the process by automating theordering of medical tests and by recommending approval of (and incertain instances actually approving) applications withoutunderwriter involvement. Such underwriting systems have the addedadvantage of being able to automatically assign tasks and cases tothe right underwriter at the right time, based on current workloadsand resource availability. These capabilities allow underwriters tofocus on the risk assessment of borderline cases.

Predictive modeling should be viewed as the next step beyondautomated underwriting. Specifically, it streamlines and optimizesunderwriting decision-making by applying business rules andenhancing traditional medical underwriting information withexternal data. This rules-based approach extends the processefficiency gains and produces underwriting decisions in a fastertime frame and within a company's existing underwritinginfrastructure.

As early adopters of predictive modeling in the life insurancesector have shown, external data provides an important set ofpredictors. These additional data sets include prescriptionhistory, motor vehicle reports and family history, as well asrelevant lifestyle data, like exercise and diet.

In evaluating any data source or element used in underwriting,consideration needs to be given to the importance and acceptance ofthose data sources. The data source needs to be predictive, butalso meet certain public acceptance thresholds and associated legalrequirements. 

Capturing The Value

The value proposition for predictive modeling for life insurersstarts with more efficient underwriting.

With automated underwriting and predictive modeling, the goal isto deliver instantaneous underwriting decisions for at least 60-80percent of new life policies issued. Achieving this goal eliminatesthe cost of superfluous medical tests and reduces resource needsthrough the automation of manual tasks leading to greater costsavings.

Premium growth and increased sales are an equally important partof the value proposition. Generally speaking, there is highcorrelation between the amount of time it takes to underwrite alife policy and the non-taken rate. Policies that are the mosttime-consuming and expensive to underwrite also are the leastlikely to be taken. Generating more policies through a moreefficient underwriting process will reduce the non-taken rate andincrease new business production. In addition, by differentiatingyour speed to issue relative to your peer group, you have helpedyour distribution system to drive new business growth and,therefore, made your company more attractive to new producers.

Consider a company writing 100,000 new life policies with anaverage premium of $800 and underwriting and medical costs of $150per policy. Cutting those costs to $90 and reducing the non-takenrate from eight percent to five percent would generate $6 millionin cost savings and $620,000 in additional profit from increasedpolicy retention. That is an eight percent performance gain on theoriginal premium. In other words, predictive modeling can boostboth the top and bottom lines.

Predictive models can be enhanced as more data becomesavailable. Therefore, companies conducting feasibility studieswould be able to address near-term gains and simultaneously take alonger-term view of the future value to be created as models becomemore sophisticated.

Looking more closely at the top line, there is also anopportunity for life insurers to bolster their target marketing andlead sourcing programs. Consider the capabilities of banks, creditproviders and consumer packaged goods companies to deliver tailoredproduct offerings to individuals just after major events, like homepurchases or the birth of a child. Insurers could similarly targetfully underwritten insurance options for customers at specificevents and milestones, provided they have the capacity to identifythe individuals most likely to buy.

Further, predictive modeling will enable lead sourcing programsto effectively balance potential policyholders in terms of risk,retention, and profitability profiles. The resulting advantagesinclude more efficient allocation of marketing budgets, improvedalignment between marketing and underwriting and optimizedacceptance rates of policies that are least likely to lapse.Finally, life insurers can gain clearer insight into the geographicregions with greatest sales and profit potential.

Taking the First Steps

Life insurance companies ready to explore predictive modelingshould consider the necessary steps, which have been provenenablers for smooth, efficient deployments at P&C carriers, aswell as the leading-edge adopters in the life insurance sector.

First, gain an understanding of the value chain componentsdriving the company's profit and growth agenda in order to craft afeasibility study to build an objective business case. It is oftena good idea to test profitability targets by looking back at thelast two years of underwriting data to see how modeling might haveinfluenced expense savings, expected mortality, and premium growth.Other operational areas (e.g., lead sourcing) may also producebenefits.

Second, brainstorm with senior leaders to chart a course andidentify potential initiatives where predictive modeling candeliver the greatest value to the organization. This can alsocreate the necessary cultural consensus to drive large-scalechange. Predictive modeling can represent a fundamental shift fromthe traditional ways of doing business. Therefore, senior executivesponsorship is critical.

Third, life insurers should assess their current data andtechnology assets and any specific infrastructure enhancements thatmay be required. But most importantly, life insurers must recognizethe imperative to act. Since adoption rates are likely to increaserapidly, predictive modeling will soon reach a tipping point in thelife insurance sector as it moves from competitive advantage forearly adopters to standard operating practice for all lifeinsurers.  The time for action is now.  TD

Gary T. Ciardiello is a principal in the Insurance and ActuarialAdvisory Services practice of Ernst & Young LLP's FinancialServices Office. Gary is based in New York City and can be reachedat 212-773-1377.

David W. McLeroy is an executive in the Insurance and ActuarialAdvisory Services practice of Ernst & Young LLP's FinancialServices Office. David is based in New York City and can be reachedat 212-773-0690.