Insights. Predictive Modeling. Predictive Modeling Proving Its Worth Among P&C Insurers. Highlights. The Current State

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1 Insights February 2012 Predictive Modeling Predictive Modeling Proving Its Worth Among P&C Insurers Highlights Predictive modeling using statistical modeling approaches to better understand and price individual risk is increasingly proving its worth in the property & casualty (P&C) insurance industry, according to results of Towers Watson s 2011 Predictive Modeling Benchmarking Survey. While respondents say its potential to advance critical areas of P&C pricing and risk assessment is still in its early stages in some commercial lines of business, they also say adoption will continue to accelerate over the next two years, prompted in part by insurers dissatisfaction with current risk assessment and rate-setting approaches. The optimism expressed by the senior executives responding to the survey suggests the range of future uses for predictive modeling is broad, and will include not only pricing and product innovation but also new refi nements in areas such as underwriting/risk selection, claim applications and target marketing. There is good reason for optimism: More and more participants that use predictive modeling report it is improving both their top- and bottom-line results. This profitability improvement encourages P&C insurers to find even more ways to extend predictive modeling applications and further access the benefits it can offer. The survey results indicate both personal lines and commercial lines carriers are moving along the same path toward a more comprehensive use of predictive modeling to more fi nely calibrate their operations. Personal lines carriers have taken the lead and are much further along than commercial carriers, but the survey fi nds this gap will tighten as commercial insurers, especially standard commercial lines carriers, pick up the pace and aggressively implement predictive modeling. The survey suggests insurers recognize they would benefi t from a deeper and broader understanding of all the ways predictive modeling can be used to improve their businesses. Auto carriers that have not embraced telematics an application of predictive modeling that uses driving behavior data to set rates individually and provide value-added services to insureds should consider doing so as a practical way to identify and take advantage of new opportunities. Telematics can also be used by commercial fl eet owners to the benefi t of both their businesses and the carriers that represent them. The data gathered make fl eets more effi cient, safer and economically friendly while helping carriers reduce claim costs. The survey findings further indicate there are substantial commercial lines opportunities to improve on pricing accuracy by improving upon and supplementing standard industry exposure bases and class plans. Commercial carriers have limited confidence in both the exposure bases as accurate exposure measures and classification plans in appropriately segmenting risk. Predictive modeling will help these insurers sharpen their precision and develop more confidence in their ability to measure and classify risk. The Current State Almost all (97%) U.S. personal lines respondents indicate they view sophisticated underwriting and risk selection as essential or very important. As a result, most U.S. personal lines insurers are already committed to predictive modeling, with approximately 85% of them saying they use or are planning to use it. Standard commercial lines insurers have been slower in recognizing the importance and moving to adopt predictive modeling but are now starting to pick up the pace, and roughly 70% indicate they either currently use or plan to use it in underwriting and risk selection, and/ or rating and pricing within the next two years. More and more participants that use predictive modeling report it is improving both their top- and bottom-line results.

2 In particular, while standard commercial insurers currently using predictive modeling are in the more modest 25% 41% range (indicating significant growth from a year ago), those planning to start programs are in a more robust 31% 48% bracket. The greatest growth potential appears to be concentrated in the commercial property, commercial multiperil (CMP) and business owner policy (BOP) lines of business, where almost half (48%) of respondents indicate they intend to implement predictive modeling for risk selection and/or pricing. Commercial automobile carriers are also showing interest, with 37% responding that they plan to use predictive modeling. This response may also reflect a growing interest in telematics on the part of commercial lines carriers. Even specialty commercial lines carriers, with a small range of usage of 12% to 15%, are showing more interest in predictive modeling, with between 24% and 37% responding that they intend to adopt it in the near future for pricing and risk selection (Figure 1). The use of predictive modeling is, at least in part, driven by the importance respondents place on sophisticated underwriting and risk selection, and/ or rating and pricing, in achieving performance and success in today s market. This is particularly true of personal lines carriers, where 83% of respondents deem these performance drivers as essential and another 14% as very important, for a resounding total response rate of 97%. Rate and price sophistication are now deemed essential or very important by 87% of participating commercial carriers serving the small to middle market, with 22% viewing these factors as essential. A more temperate 42% of respondent carriers serving the large account/specialty lines market view predictive modeling as essential or very important. Insurers using predictive modeling are increasingly benefiting from its use, and respondents said it is pivotal in achieving both strong top- and bottom-line results, with large carriers leading the way (Figure 2). Nearly half of respondents (49%) cited a positive impact on the top-line benefit of an expansion of underwriting appetite. Bottom-line benefits of rate accuracy, loss ratio improvement and improved profitability all received positive responses of nearly 75% or more from survey participants. All of these percentages are up roughly 10% to 20% over last year s results, indicating the enduring sustainable benefits of predictive modeling. Commercial lines carriers are more likely to see positive bottom-line improvement than personal lines carriers, while large carriers are more likely to see more substantial improvement than small carriers. Figure 1. Most personal lines carriers use or plan to use predictive modeling; standard commercial lines carriers are quickly following suit Personal lines Personal automobile Homeowners Standard commercial lines Workers compensation Commercial property/cmp/bop Commercial automobile Long-tail/specialty commercial lines General liability 15 Specialty lines Currently use Plan to use Do not use and no plans to use Figure 2. Most carriers report significant positive bottom-line impacts from predictive modeling; almost half also see favorable top-line impacts 37 Bottom line Rate accuracy Loss ratio improvement Profitability Top line Expansion of underwriting appetite Renewal retention Market share 37 Positive impact No impact Negative impact towerswatson.com

3 Appropriateness of Standard Industry Exposure Bases and Class Plans Commercial lines respondents expressed significant skepticism when asked about the appropriateness of standard industry class plans and exposure bases. Most of these carriers have limited confidence in the ability of standard industry class plans to segment business appropriately. This lack of confidence was particularly true of medical malpractice (67%), directors and officers (D&O) and employment practices liability (EPL) (65%), and commercial automobile (56%). There was slightly more confidence among carriers representing lines of business including commercial property, CMP/BOP and general liability, and even more confidence among workers compensation carriers. With the exception of workers compensation, at most, 12% of respondents stated they were confident or highly confident in the standard industry rating plans (Figure 3). Lukewarm may be the best way to describe respondents view of the appropriateness of standard industry exposure bases for commercial lines. With the exception of workers compensation, less than 35% of carriers across all lines of business expressed confi dence in the exposure bases. On the other hand, 35% or less of carriers across all lines expressed minimal confi dence in all exposure bases except for energy (43%), leaving the largest population of respondents only somewhat confi dent in the standard industry exposure bases for all lines of business (Figure 4). The implications are clear that there is plenty of room for predictive modeling techniques to identify interactions between existing rating variables, or to surface additional internal or external variables to better align price and exposure for all commercial lines of business. Commercial lines carriers have limited confidence in the ability of standard industrial class plans to segment business appropriately. Figure 3. Commercial lines carriers have limited confidence in the ability of standard industry class plans to segment business appropriately by exposure Medical malpractice D&O/EPL Commercial automobile Commercial property CMP/BOP Energy General liability Workers compensation Not very/not at all confident Somewhat confident Confident/Highly confident Figure 4. While there is somewhat more confidence in the appropriateness of standard industry exposure bases, it is lukewarm at best Energy (sales) CMP/BOP liability (sales/square feet) D&O/EPL (revenue/market cap) Medical malpractice (bed equivalents/physician FTEs) General liability (sales/payroll) Commercial automobile (number of vehicles) Commercial property (building value) CMP/BOP property (building value) Workers compensation (payroll) Not very/not at all confident Somewhat confident Confident/Highly confident 3 towerswatson.com

4 Interestingly, the large carriers, which are market leaders in this space, tend to have the least confi dence in the standard industry rating plans and exposure bases, as perhaps their predictive modeling learnings help illuminate the weaknesses in more traditional pricing approaches. The clear exception is workers compensation, where large carriers are more vested and involved in the standard industry pricing approach through the workers compensation rating bureaus. Hurdles to Predictive Modeling Use Personal and commercial lines carriers share common views on the main hurdles to pricing and product innovation: data availability, implementation and technology support. Not surprisingly, data availability was the largest concern overall, and a much greater concern for pricing innovation (with 77% of carriers listing it as a top-three concern) compared with product innovation (60%). The reverse was true for technology support, which was considered less important for pricing innovation (40%) than for product innovation (57%). Beyond rating and pricing, most carriers plan to use predictive modeling in risk selection, catastrophe management, target marketing strategy and claim administration. For example, an overwhelming 89% of respondents either use or plan to use predictive modeling for underwriting/risk selection. However, respondents were least likely to use predictive modeling for agency placement/distribution management (30%) and agency management/ compensation (33%). In terms of modeling techniques, the survey very clearly indicates that generalized linear models (GLMs) are the technique of choice, with 74% of respondents currently using GLMs. While many carriers employ a variety of techniques, the next highest current use is generic vendor models at 27%, with other techniques following, such as classifi cation and regression trees, decision trees, generalized additive models, mixed models, neural nets and, fi nally, machine-based learning at 3% current use. While personal and commercial carriers generally share common concerns, there are differences by size of carrier. For instance, implementation hurdles are more signifi cant for large carriers, while availability of expertise and skill sets are more signifi cant issues for small carriers. What Is Predictive Modeling? Predictive modeling is the application of statistical techniques and algorithms to individual risk data to better understand the behavior of a target variable based upon how multiple variables interact. Rather than just relying on an understanding of individual risk elements, predictive modeling allows insurers to consider many (including new and unexplored) factors simultaneously. This analysis permits more accurate and objective risk selection and pricing decisions. Another next-generation pricing tool among national personal and commercial lines carriers is telematics, a specifi c application of predictive modeling. Telematics, also known as usage-based insurance, is implemented by installing a GPS tracking device in a vehicle that records information on speed, location and stresses on the vehicle over time. The data are then used to score driving behavior and more accurately price auto coverage while providing many collateral benefi ts. The technology can be used for personal vehicles or for commercial fl eets. 4 towerswatson.com

5 Telematics Personal lines insurers committed to predictive modeling early in its development and seem poised to add depth to their programs by introducing or expanding their use of telematics. Currently, personal lines respondent carriers that use telematics are focusing on only a few areas, including measurement of annual mileage, tracking how and when a vehicle is being driven, who is driving the vehicle and where it is being driven. But more expansive plans are under way: 89% of personal lines respondents that either currently use or plan to use telematics have plans to use the data prospectively in rating, and 83% plan to use it to provide information to insureds to help improve driving behavior. Commercial carriers use of telematics is focused on the same four tasks. Fully 87% of commercial lines respondents that currently use or plan to use telematics have plans to track mileage, where and when the vehicle is being driven, as well as who is driving the vehicle. Improved Modeling Sophistication Standard commercial lines carriers have made the most progress in improving predictive modeling sophistication over the past year. However, U.S. personal auto and homeowners carriers still lead in their degree of underwriting and pricing sophistication, with 68% of personal auto insurers and 48% of homeowners insurers rating their sophistication on average as very high, high or medium. These fi ndings speak to a contradiction in respondents answers. Standard commercial insurers report they are not happy with current methods for assessing risk and setting rates, yet they are still not embracing predictive modeling to the degree they could. Although the survey responses show optimism for adoption over the next two years, they also suggest insurers need to better understand how a strong predictive modeling program can help them refi ne their underwriting, risk selection and pricing. Investment in predictive modeling will improve top- and bottom-line results and enable profi table growth, with the initial investment opening the opportunity for signifi cant and sustainable potential returns. Although the survey responses show optimism for adoption over the next two years, they also suggest insurers need to better understand how a strong predictive modeling program can help them refine their underwriting, risk selection and pricing. About the Participants Towers Watson conducted a web-based survey of U.S. and Canadian P&C insurance executives in October and November A total of 60 U.S. and nine Canadian executives responded, including chief actuaries, pricing offi cers, claim offi cers and other senior executives. Responding companies represent a signifi cant market share of U.S. personal lines carriers (21%) and commercial lines carriers (32%). Thirty-five percent of U.S. respondents had 2010 annual direct written premium greater than US$750 million, 33% between US$200 million and US$750 million, and 32% under US$200 million. The full report of fi ndings is available to participants only. To fi nd out how to participate in the 2012 survey, please contact: Klayton Southwood klayton.southwood@towerswatson.com Brian Stoll brian.stoll@towerswatson.com 5 towerswatson.com

6 About Towers Watson Towers Watson is a leading global professional services company that helps organizations improve performance through effective people, risk and financial management. With 14,000 associates around the world, we offer solutions in the areas of employee benefits, talent management, rewards, and risk and capital management. Copyright 2012 Towers Watson. All rights reserved. TW-NA towerswatson.com