bringing predictive models to life cfo mag

4 Actuarial Software Now American Academy of Actuaries

Bringing Predictive Models to Life

Using predictive models, life underwriters could lower the cost, increase the consistency, and potentially improve the accuracy

of their underwriting processes.

By James Guszcza, Mike Batty, Alice Kroll, and Chris Stehno

The great physicist Niels Bohr fa-mously joked that “prediction is very difficult, especially about the

future.” Bohr had a point, but it’s also true that modern predictive modeling techniques are enabling businesses in a wide array of industries to innovate, be-come more efficient, make more accu-rate and consistent decisions, and grow profitably.

The predictive algorithms power-ing the recommendations of Amazon, Netflix, and Google leap to mind. But, as recent books such as Michael Lew-is’ Moneyball, Ian Ayres’ Super Crunch-

ers, and Stephen Baker’s The Numerati document, the phenomenon goes well beyond the Internet and database marketing. Indeed the transformative power of predictive modeling cuts across such a wide swath of industries that Chris Anderson, the editor of Wired, proclaimed that one of today’s most important cultural trends is “the explosion of data about every aspect of our world and the rise of applied math gurus who know how to use it.”

Anderson’s comment might be hy-perbolic, but a few examples give a sense of just how ubiquitous the phe-nomenon has become. (See “Analyz-ing Analytics,” in the July/August 2008 Contingencies.) Baseball teams

American Academy of Actuaries Actuarial Software Now 5

such as the Oakland A’s and Boston Red Sox now use data-driven analytics to select baseball players who are undervalued by the market. Simple regression models have proven supe-rior to eminent wine critics at identi-fying excellent vintages of Bordeaux. Statistical decision tree models are able to help emergency room doctors identify patients with the highest risk of heart attack. We can add to this list our own company’s work in property/casualty predictive modeling: In the past decade, Deloitte has introduced predictive models used by commer-cial insurance underwriters to bet-ter select and price all major lines of commercial insurance. These models have proven so successful that predic-tive models in commercial insurance have rapidly gone from being regard-ed as innovative “secret weapons” to table stakes. Furthermore, those companies that were early to embrace predictive modeling gained a valuable early mover’s advantage in the ongo-ing process of creating and refining their analytics-based pricing and un-derwriting capabilities and strategies.

Can predictive models be similarly applied in the life insurance indus-try? Our recent experience leads us to answer with a decisive “yes.” We

have built predictive models with a proven ability to help life insurers more effectively target markets; re-tain customers; eliminate underwrit-ing requirements while issuing at fully underwritten rates for significant seg-ments of the population; make under-writing decisions more economically, accurately, and consistently; and even refine product pricing.

The Art and Science of Predictive ModelingAt its most basic level, predictive mod-eling is the process of using known quantities to predict unknown quan-tities. More precisely, such statistical and machine learning tools as regres-sion and generalized linear models, decision trees, neural networks, and graphical data visualization tools are able to identify linear and non-linear combinations of predictive variables that serve as “leading indicators” of unknown quantities such as the pro-pensity to purchase a product and the likelihood of lapse or mortality.

Certain core predictive modeling techniques, most notably regression analysis, have been around for many decades. Because of this, the basic science of predictive modeling is very well understood and has stood the test

Predictive models in commercial insurance have rapidly gone from being regarded as

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of time. What is new is the accumu-lation of massive stores of historical data, the proliferation of inexpensive computing power, and major innova-tions in recent decades in machine learning and statistical science. These developments have enabled innova-tive entrepreneurs to apply predic-tive modeling in the creation of new

products and services, and to profit from market inefficiencies in mature industries. A well-known example in the insurance domain is Progressive Insurance, which was able to grow profitably through the improved risk segmentation enabled by its use of predictive analytics.

Predictive modeling is a science in that it is founded on the principle of statistics and yields predictive al-gorithms that can be validated on out-of-sample data. However, a suc-cessful predictive modeling project relies as much on art as science. In the case of life insurance, this art refers to the many interdisciplinary activi-ties needed to bridge the gap between textbook statistics and the end-to-end design, construction, validation, and business implementation of a success-ful predictive model. Deep subject-matter expertise of life insurance, as well as skills in project management,

programming, third-party data sourc-es, statistics and statistical computing, information technology (IT) infra-structure, and change management are all required.

In short, while textbook knowledge of statistics is necessary to the success of a predictive modeling project, it is not sufficient. Predictive modeling

projects are, after all, strategic busi-ness initiatives, not backroom techni-cal projects. Models must be designed both to reflect the organization’s busi-ness strategy and to comport with the practices and accumulated wisdom of the underwriters and other decision-makers for whom they are designed. In addition, models must be “brought to life” in three distinct senses. First, business rules must be fashioned to convert both quantitative and quali-tative model indications into recom-mended actions. Second, the techno-logical implementation of the model must be designed, ideally in parallel with the model construction, and ex-ecuted to enable the model’s dispa-rate data elements to be seamlessly gathered, transformed, and combined to feed into business rules. Finally, decision-makers must be trained to understand the models’ assumptions, strengths, and limitations to ensure

that the models are both embraced and sensibly used within the organization.

Another sense in which predictive modeling is an art involves the pro-cesses of designing and building the models themselves. One occasionally encounters the misapprehension that predictive analytics and modeling projects are fairly mechanical exercis-es in number crunching, perhaps per-formed by backroom personnel using spreadsheets. In reality, a great deal of creativity and tacit knowledge is required to create a predictive model that will bring value to an organiza-tion. Strong institutional and subject-matter knowledge is needed to under-stand how a model can be designed to improve an existing business process. For example, a statistician with little knowledge of underwriting is un-likely to know enough to gather all of the data elements needed to serve as model inputs. Collaboration among actuarial, underwriting, IT, legal and compliance, and statistical experts is essential. Ideally, this collaboration will occur within the context of exec-utive-level sponsorship.

In short, a successful predictive modeling project is a strategic busi-ness initiative that requires collabora-tion, not a purely technical project. Seamless integration with business processes is crucial, and predictive modeling is therefore only part of a predictive modeling project.

An additional thought is that both art and science are needed even dur-ing the technical, model-building phase of the project. At its core, building a predictive model is an ex-ercise in induction. The best modelers

While textbook knowledge of statistics is necessary to the success of a predictive

modeling project, it is not sufficient.


creatively find new ways to visualize, explore, and discover latent relation-ships in large databases. In our experi-ence, the power of a predictive model is ultimately derived as much from the quality of the data exploration and variable creation phases of the proj-ect as from the statistical techniques actually used to build the model. Of particular importance is the construc-tion of insightful synthetic variables from more basic data elements. An

innovative approach to data explora-tion and synthetic variable creation ensures that the resulting model will total more than the sum of its parts. If Martha Stewart were a modeler, she might say that predictive modeling is closer in character to baking a soufflé than tossing a salad.

Life Insurance ApplicationsHow can life insurers capitalize upon predictive modeling? A common de-nominator among many successful predictive modeling applications is the use of models to improve subjec-tive or judgment-driven business pro-cesses. This is a major theme of Mon-

eyball and Super Crunchers, and it jibes with our own modeling experiences.

Predictive models can help life in-

Because they are not subject to fatigue, emotions, computational limitations, or cognitive biases, computer algorithms outperform human

experts at combining multiple pieces of informa-tion that are relevant to coming to a decision.

surance underwriters make decisions more accurately, consistently, and economically. Because they are not subject to fatigue, emotions, computa-tional limitations, or cognitive biases, computer algorithms outperform hu-man experts at combining multiple pieces of information that are rel-evant to coming to a decision. For this reason, the psychologists Richard Nis-bett and Lee Ross commented many years ago that “human judges are not

merely worse than optimal regression equations; they are worse than almost any regression equation.”

Therefore, predictive models are useful in suggesting improved math-ematical combinations of traditional underwriting elements. But this is only the beginning. In the age of com-puter-aided underwriting, traditional underwriting data sources can readily be augmented by third-party sources containing a plethora of household, lifestyle, financial, purchasing, survey, and demographic information. Liter-ally thousands of such data elements are available for existing and poten-tial insureds. Taken together, these external data elements can be used to paint a statistical picture that provides

valuable and predictive insights into the lifestyle and health status (and therefore mortality risk) of individual applicants.

For example, individuals who order a deluxe cable package and simultane-ously pursue few sporting or exercise activities are more likely to live seden-tary lifestyles and ultimately suffer a higher incidence of various lifestyle-based diseases. On the other hand, among people with elevated body mass indexes (BMI), those who pur-chase diet and weight-loss equipment are perhaps more likely to become serious about maintaining healthier lifestyles in the future.

These simple, stylized examples illustrate the concept but don’t do justice to the subtlety and variety of the patterns captured by well-crafted multivariate predictive models. Our experience indicates that integrating multiple data elements from a variety of data sources—and, in particular, creatively deriving synthetic variables from more basic data elements—en-ables one to build predictive models with the greatest segmentation power. These models contain dimensions that serve as proxies for such elusive attributes as lifestyle, mortality, and morbidity.

The end result of the modeling process is a scoring equation that seg-ments individuals based on expected mortality. The algorithm is a tool that can be employed in a variety of ways. Different carriers might emphasize different applications depending on culture and competitive strategy. Typ-ical applications include:

Expense reduction—A fast-track


underwriting process can be es-tablished for risks that score well. For many potential insureds, this avoids the need for expensive and time-consuming lab tests, medical examinations, and physician statements.

Consistency and quality assur-ance—Just as no predictive model is complete, no expert underwriter is infallible or perfectly self-consis-tent. A company might therefore elect to compare the decisions of experienced underwriters with the indications of a predictive model. Decisions that are consistent with model indications can be made with greater confidence; other de-cisions can be further investigated.

Refined underwriting and pricing—Computer algorithms can evaluate finer gradations of, interactions between, and syntheses of existing underwriting dimen-sions. Furthermore, they can also integrate potentially hundreds of additional, non-traditional data elements into the decision-making process. Therefore, predictive mod-els can be used to determine a finer range of underwriting gradations and price points.

In-force management—Armed with periodically updated assess-ments of health status, primarily using publicly available third-party data, one can manage in-force business long after underwriting has worn off.

Marketing efficiency—Models can help selectively attract applicants who will qualify for insurance and therefore avoid wasting resources on those who won’t qualify or will decline offers. Similarly, they can identify those customers most likely to lapse, thereby focusing retention efforts.

It is important to emphasize that a predictive model is a tool to be used strategically by underwriters, not a replacement for their expertise. There can be no computer-based replacement for the expertise, judg-ment, and experience of seasoned underwriters. But we have found that as in so many other disciplines, pre-dictive models enable underwriters to up their game. Furthermore, just as a creative approach should be used to build better models, creativity should be brought to bear in finding innova-tive ways of implementing them. The above ideas don’t exhaust the possible applications of predictive models to the life insurance underwriting pro-cess, nor to other non-underwriting applications.

So, Does It Really Work?All of this might sound intriguing, but how well does it really work? A unique virtue of predictive modeling is that precise answers to such ques-tions can be given by testing models on out-of-sample validation data.

The short answer is: the models work quite well. Figure 1 displays the risk segmentation achieved by build-ing a scoring model that uses only third-party marketing data. Risks in the holdout sample are rank-ordered

Sample Life Insurance Prospecting ModelExpected Mortality Lift Curve

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by the model score and divided into equal-sized deciles. (Note that in this example, higher scores and deciles are indicative of improved expected mor-tality.) Within each decile, estimated mortality is computed using a weight-ed average of the insurer’s mortality estimates for each traditional under-writing class. The weights are the pro-portion of risks from the given decile within each underwriter class.

It is striking that inexpensive exter-nal marketing data alone—data that were not used to make underwriting decisions—can be combined to yield such segmentation power. Because people who ultimately wouldn’t qual-ify for insurance (or who wouldn’t accept poor ratings) would be dispro-portionately represented in the worst deciles, this model can be used for marketing purposes. Adopting such a “prospecting” model in this way would result in a more efficient and economical marketing and underwrit-ing process.

Moreover, this model could profile the in-force book of business. Insurers routinely make policy-level decisions years after underwriting information has become obsolete. An updated assessment of risk would tell them whom to target for wellness or reten-tion programs. In addition to policy-level analysis, an insurer could use the model to profile an entire block of business to provide another piece of information when considering an ac-quisition or sale, or refining mortality assumptions for reserving or capital calculations.

Even more impressive segmenta-tion power results when easily avail-

able underwriting data is added to the modeling process. Figure 2 displays a sample model’s mortality curve against the estimated mortality curve resulting from the company’s existing underwriting process.

Note that this comparison is not apples to apples because the model incorporates only easily available un-derwriting data, not more expensive data elements such as lab tests, exams, and physicians’ statements. Since the model’s results can be available in near-real time upon receipt of an ap-plication, the model clearly has the ability to greatly streamline under-writing for a large percentage of risks, saving considerable time and expense in the process.

Bottom-line savings always draw

attention, but this evolution in under-writing also improves the applicant and agent experience. The typical time for application processing and underwriting is measured in weeks rather than minutes or hours. During this time, applicants may purchase in-surance from the first carrier that re-sponds or become frustrated with the process altogether. In addition to in-creased placement rates, research in-dicates more satisfied applicants tend to refer friends and family members. Of course, faster processing time also leads to faster payment of com-missions. Insurers would be happy to have speed on their side while enticing independent agents to place business or when recruiting career agents.

While the above comparison be-

Sample Life Insurance Underwriting ModelExpected Mortality Lift Curve

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tween the model’s mortality curve and the company’s pricing mortality curve is compelling, it still doesn’t il-lustrate the full power of the model. The comparison suggests that models can be used to anticipate underwriter decisions using data elements that are relatively inexpensive and easily avail-able. Further analysis suggests that the model also has the ability to potential-ly refine underwriting decisions.

The plots in Figure 3 exemplify this idea. Here, rather than analyzing the

full model validation sample, we focus on the subset of validation data points within the carrier’s standard risk class (approximately one-third of its book). We apply the model to the standard risk class data points and profile the re-sulting model deciles using such famil-iar dimensions as applicant age, BMI, and various proprietary medical and disease propensity indexes. The outer green and red horizontal lines repre-sent the average value of these quanti-ties in the ultra preferred and declined

risk classes, respectively. The inner blue horizontal lines represent the av-erage value of these quantities in the standard risk class being analyzed.

In each case, the average values of these quantities in the best model deciles (9, 10) are comparable to the average values found in the ultra pre-ferred risk class. In other words, the model identifies risks currently classi-fied as standard that appear compa-rable to other risks classified as ultra preferred. Analogously, the standard

Using Model Score to Refine StandardRisk Class

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risks in the worst deciles (1, 2) appear comparable to other risks that have been declined.

Many other variables were used to profile the standard risk class and each of the other risk classes. In nearly all cases, the plots suggested the poten-tial for underwriting refinement. This is prima facie excellent evidence that the model can go beyond current un-derwriting standards and be used to set refined under-writing and pricing guidelines. Case-by-case spot-checks and comparisons of model deciles with underwriters’ deci-sions further con-firmed the integrity and reliability of the model’s indications.

Addressing Legal and Societal ConcernsThe potential benefits of predictive modeling for the life insurance indus-try are intriguing, but what about the legal and ethical questions? Regarding the former, it is important to note that the third-party data used by our mod-els are not regulated by the Health In-surance Portability and Accountability Act or the Fair Credit Reporting Act, and do not require signature authority. Furthermore, our models exclude any data elements corresponding to infor-mation that life insurers are precluded from using in traditional underwriting.

What about societal and ethical con-cerns? Some might suggest that using third-party data is too invasive. Yet the use of such data is already a fact of life within property/casualty insur-

ance and many other industries. For example, banks routinely analyze hun-dreds if not thousands of variables to determine whether to offer a customer a credit card. Furthermore, good mod-els don’t stand or fall based on a hand-ful of silver-bullet variables. Rather, a plethora of data elements, which are readily available but often not ex-tremely predictive when taken individ-ually, are combined in a way that tells

a story that an underwriter would ulti-mately uncover through more labori-ous, fallible, and time-intensive meth-ods. A final speculative point is that a widespread adoption of lifestyle-based scoring models by life insurers could have a salutary effect on society: Just as banks’ and insurers’ use of credit-scoring models encourages people to pay their bills on time, more accurate and scientific life insurance underwrit-ing processes could further encourage individuals to live healthier lifestyles.

Predictive modeling works, and this is why it has become ubiquitous in many domains. Still, the life insurance in-dustry has a unique culture and set of norms. One might be concerned that no model could ever capture the nu-

ances, tacit knowledge, and extensive expertise of experienced underwriting professionals. We agree. Our models have held up quite well, even on a poli-cy-by-policy basis, to the scrutiny of ex-perienced underwriters. But the models should be regarded as no more than tools that enable underwriters to fast-track straightforward risks, improve the cost, accuracy, and consistency of the company’s underwriting processes, and

potentially refine its existing under-writing and pricing standards. With these improvements in place, underwrit-ers have additional time and resources to evaluate more complex risks. With predictive models in hand, underwrit-ers are therefore better able to act

as managers of a portfolio of risks. Fur-thermore, expert underwriters would be better able to add their insights to future iterations of the modeling process, there-by continuing a virtuous cycle of under-writing excellence that marries human expertise with statistical rigor.

James Guszcza, a fellow of the Casualty Actu-

arial Society and member of the American Acad-

emy of Actuaries, is a senior manager at Deloitte

Consulting in Los Angeles. Mike Batty is a fel-

low of the Society of Actuaries and a senior con-

sultant with Deloitte Consulting in Minneapolis.

Alice Kroll, a fellow of the Society of Actuaries

and a member of the American Academy of Ac-

tuaries, is a director with Deloitte Consulting in

Chicago. Chris Stehno is a senior manager with

Deloitte Consulting in Chicago.

The models should be regarded as no more than tools that enable underwriters to fast-track

straightforward risks, improve the cost, accu-racy, and consistency of the company’s under-

writing processes, and potentially refine its existing underwriting and pricing standards.

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