Operationalizing Analytics:

Transcription

1 Operationalizing Analytics: The True State of Predictive Modeling in Insurance An White Paper Author: Mark Breading, SMA Partner Published Date: July, 2013 Page 1

2 Table of Contents About This White Paper This white paper is based on insights from the SMA Operational Analytics survey conducted in 2Q13 and sponsored by SAS. Additional insights originate from SMA s annual research series on data and analytics in insurance. The sponsor has paid for distribution rights, and has not influenced the survey data or analysis. The content of this paper is a synopsis of SMA s Predictive Modeling: Changing the Game 3 The Next Play More Power with Predictive Modeling Current State of Modeling in Insurance 4 The True State of Modeling Penetration Business Areas and Business Problems Addressed Operationalizing Analytics 5 Managing Models Insurer Challenges Insurer Plans and Priorities 8 Investments in Predictive Modeling Key Project Areas Capitalizing on the Opportunity 11 analysis and insights. About Strategy Meets Action 11 About SAS, the White Paper Sponsor 11 The content of this SMA White Paper is based on the SMA Operational Analytics survey conducted in the second quarter of 2013 and insights from SMA s annual research series on data and analytics in insurance. The sponsor of this white paper is SAS. Page 2

3 Predictive Modeling: Changing the Game Insurers of all stripes are seeking new insights for competitive advantage insights for improved awareness of customer needs and behaviors, a deeper understanding of risks, more intelligence regarding day-to-day operations, and innovative approaches to managing costs. The notion of running an insurance company based on detailed analysis of data is nothing different. The industry has been doing that since its inception. f Although traditional business intelligence is still extremely valuable, the momentum is shifting toward more advanced analytics in the quest to find approaches that are truly gamechanging. And the top area of activity in advanced analytics for insurers is predictive modeling. The Next Play While the insurance industry has long been recognized for analyzing data, the new news involves the overwhelming amount of data that is now available for analysis and the sophistication of the technology tools that can be used to perform the analysis. The opportunities for advanced analysis are many and the potential business impact is enormous. Insurers must be strategic and disciplined in their investments and initiatives to be certain they are capitalizing on all of the possibilities. Today, many opportunities still exist for improving operations with the use of traditional business intelligence. Many insurers are finding great value in implementing or enhancing capabilities for interactive reporting, executive dashboards, and performing what-if analyses. While these areas are extremely valuable, the momentum is shifting toward more advanced analytics in the quest to find approaches that are truly game-changing. The top area of activity in advanced analytics for insurers is predictive modeling. More Power with Predictive Modeling Model building is on the verge of a revolution. For years, the approach to building an analytical model in insurance has been to blend art and science in a long cycle of creation, testing, revision, retesting, and eventually deploying the model in a production environment. Multiplying that process many times for models representing various products, territories, and scenarios makes the modeling world complex, a world that only a small number of elite professionals truly understand. But all of that is about to change in fact it is changing already. Predictive modeling is certainly not new to insurance. But lately, it is rapidly expanding from having a narrow role to a broad one, with applications across many business areas. Even in areas where predictive models have been used for some time, such as CAT modeling and pricing, insurers are rapidly progressing in the sophistication of their capabilities. As model usage becomes more pervasive, the critical issue is whether insurers are able to operationalize the analytics, and continue to leverage back-office or offline analytics to identify macro trends and opportunities, as well as routinely embed them in real-time, operational systems. The business value and impact of predictive modeling is increasing very quickly due to: the availability of vast stores of proprietary and public data more sophisticated tools for creating, managing, and running models a wider range of critical business challenges and opportunities that can benefit from predictive modeling Page 3

4 j Survey Background f The quantitative information presented in this white paper, sponsored by SAS, is based on a survey of 46 insurers in North America. Respondents included both P&C and L&A insurers, and an even split of insurers over $1B in premium and under $1B. Two-thirds of the respondents were business executives and onethird were IT executives. Many insurers understand the potential of predictive modeling and are aggressively investing and pursing new initiatives. The current buzz in the industry around predictive modeling is very high, but the important question is: What is the true state of predictive modeling in insurance? Essential areas and questions to consider when looking at the state of the industry include: Is predictive modeling being employed by just a few leaders or by a large portion of the industry? What segments and industry tiers are most advanced? How long does it typically take to create and deploy models? How long does it take to realize the value? Which aspects of predictive modeling do insurers do well? Where do they lack experience? What key challenges do they face? Where are insurers investing in predictive modeling? What are the specific types of projects that are currently underway or being planned? How are insurers operationalizing analytics in the key business areas of marketing and sales, actuarial and underwriting, and claims? The following sections of this white paper address these questions. Insights presented have been gleaned from SMA s recent operational analytics survey of insurers and the ongoing interactions that SMA has with insurers across North America. Current State of Modeling in Insurance j Significant activity is underway in the life and annuity segment and the property and casualty segment, in both large and small companies. SMA research indicates that almost half of all insurers are investing in new predictive modeling projects in 2013, compared to approximately one-third in External forces impacting the industry have caused the pace of change to accelerate and have magnified the intensity of that change. Insurers are being motivated to look for new ways to do business, and many see analytics as the key to improving operations and gaining an edge on the competition. Insurers in North America have decidedly recognized the importance, as evidenced by their annual investments of more than $10B USD in data and analytics 1. Insurance is no longer the quiet, slow-moving industry it once was. Many insurers view data as one of their most valuable corporate assets. Combining their internal data with the exponentially increasing amounts of available external data is positioning insurers to mine for trends, patterns, and nuggets the kinds of insights that can help them surge ahead of their competitors. The True State of Modeling Penetration Predictive modeling is alive and well in insurance. Significant activity is underway in the life and annuity segment and the property and casualty segment, in both large and small companies. However, the magnitude of the investments, the types of business problems being tackled, and the approaches to modeling vary considerably by line of business and company size. 1 Breading, M. (2013). Data and Analytics in Insurance: P&C Insurers Plans and Priorities for 2013 and Beyond, and L&A Insurers Plans and Priorities for 2013 and Beyond, published May 2013 and June 2013, respectively. Strategy Meets Action: Boston, MA. Page 4

5 There is no question that the large insurers are investing more and have additional experience in building and operationalizing models. But the landscape is changing, with sophisticated technologies available via the cloud, experienced business partners available to assist, and senior executives with more appetite to try novel approaches in the current hyper-competitive environment. k Operational Analytics Defined j Analytics consists of a broad set of technology tools and approaches to identify trends, uncover key relationships, and predict future activities. Predictive modeling is one class of analytics used to predict the probability of a future outcome. Operational analytics is the embedding of models in transaction systems across the organization for real-time decisioning. Business Areas and Business Problems Addressed Insurers are quite familiar with analytical models. The entire actuarial profession is built around sophisticated mathematical and statistical models. These models are essential in determining the probable loss costs for defined combinations of perils, understanding how to price insurance products to achieve profitability, and evaluating how much to set aside in reserves to cover future claims. Catastrophe models predict the maximum probable loss from various disaster scenarios in specific geographic locations. Other areas of the business have also used analytical models in the past. What is new is that the high percentage of insurers that are investing in new projects are moving beyond the traditional approaches and technology tools and harnessing advanced predictive modeling capabilities. Initiatives in marketing and sales, actuarial, underwriting, claims, and other areas are gaining prominence in the plans of many insurers. Whereas the original scope of analytics projects tended to be related to risk, the problem set is now much larger in fact there are applications for which advanced analytics are being used across the entire value chain for both L&A and P&C. The focus on analytics for these different areas marketing/sales, actuarial/underwriting, and claims is reflected in the benefits insurers expect to gain from investments in predictive modeling. SMA s survey shows that both P&C and L&A insurers expect analytics to deliver the same three top benefits: risk reduction, cost control/reduction, and revenue growth. However, P&C insurers cite risk reduction as the top benefit they are hoping to achieve from advanced analytics, while L&A insurers are more focused on growing top line revenue. It is no surprise that cost control/reduction is ranked second by both groups. Insurers that write over $1 billion in premium are most focused on control/reduction; those writing under $1 billion rank risk reduction highest. Operationalizing Analytics Investors are demanding strong financial results every quarter. Regulators are taking a closer look at business processes and day-to-day operations. Legislatures are churning out an incessant stream of new laws. Customer and business partner expectations and demands are expanding. These pressures are causing insurers to create a more agile and responsive organization, one that is able to make informed, real-time decisions and that requires operationalizing analytics. Managing Models The SMA survey reveals that the time needed to create and deploy models is still relatively long. On average, it takes an insurer about six months from the time model building begins until the time it is put into live production use. Page 5

6 For the majority of insurers, it takes another six months or more to fully realize the benefits associated with the models. While this might be slightly faster than a few years ago, it seems like eons by today s real-time standards. But it still reflects the handcrafted approach to painstakingly building each model. Assessing the industry averages is useful, but SMA research also indicates that there is a wide variance in modeling development cycles within the industry (see Figure 1). About one in four insurers say that their cycle yields live models in three months or less, while approximately one-third cite times longer than nine months. In the meantime, the emerging technology of high performance analytics is making it possible to collapse those times from months to days. Figure 1. Time Needed to Create & Deploy an Analytical Model Over $1B 44% Under $1B 38% 38% 31% 25% 24% < 3 months 4-9 months > 9 months While model creation and deployment is taking too long, model decay is another serious challenge. Retaining poorly performing models can result in inaccurate projections which lead to poor business decisions. The survey found that 40% of insurers monitored the performance of their predictive models at least quarterly. However, nearly half of the insurers validated their models, at best, on an annual basis. Failure to update a model frequently enough can result in loss of revenue and earnings as competitors observe and act on changing trends. Insurer Challenges Source: SMA Research, Operational Analytics in Insurance 2013, n=46 Although activity and investments are moving full steam ahead, insurers have some key challenges to overcome before they can maximize the benefits. Figure 2 lists the top three challenges faced by P&C and L&A insurers, and by large and small insurers (over and under $1B USD in premium). Page 6

7 Figure 2. Biggest Challenges to Deploying Models P&C L&A Over $1B Under $1B #1 Data Management Lack of Talent Unknown ROI Data Management #2 Lack of Talent Unknown ROI Data Management Lack of Talent #3 Unknown ROI Access to the Right Data Lack of Talent Unknown ROI The top three challenges are very similar for the segments shown in Figure 2, although the priorities vary. Insurers must work through issues with data, talent, and return on investment in pursuing predictive modeling projects, as described below. Data Management/Access to the Right Data The fact that data management and data access surface as key inhibitors is not surprising. Models can only be as good as the data that is fed into them. The insurance industry was an early pioneer in data processing, continually evolving core processing systems since the 1960s and 70s. Combine that with the requirements to manage and analyze years and even decades of historical data and the result is a vast amount of data, strewn about in various databases around the enterprise. Great progress has been made in enterprise data modeling and master data management in terms of getting more control over the data, but the task of provisioning the right data in a timely manner for use by predictive models is still a challenging one. Lack of Talent Insurers arguably have more analytical talent than any industry on the planet. Actuaries, underwriters, and CAT modelers are highly skilled, with deep analytics knowledge at the core of their professions. However, the promise of analytics and big data is splashed across the headlines as the key to competitve advantage for every industry. The battle for analytics talent has just begun, and insurers must compete with retail, entertainment, travel, and many other industries. Insurers will have to enact plans at the industry and company levels in order to recruit, train, and mature the deep analytics talent required to build, manage, and interpret models for many types of insurance business problems. Unknown ROI Source: SMA Research, Operational Analytics in Insurance 2013, n=46 At first glance it might seem surprising that insurers chose unknown ROI as a key challenge to operationalizing analytics. Given the high visibility, incredible promise, and increasing investments in predictive modeling, it seems as if the return on investment must be well understood. The truth is that the industry is at an inflection point for modeling. Many executives sense that they must leverage analytics much more broadly and at much faster speeds than ever imagined in the past. They see their competitors investing and understand that analytics may be THE competitive differentiator for the Page 7

8 industry. While many are making the decision to invest, the transitional nature of the analytics and modeling capabilities today still makes it difficult to estimate the precise ROI that can be expected. What it really comes down to is making a bold decision to invest in capabilities that will yield new insights for every dimension of the business customers, risks, products, channels, losses, and all the rest. Insurer Plans and Priorities d One big transition that the industry has embarked upon is the move from long to short cycles for model building and deployment. A second major transition, enabled by the first, is to move from back-office or offline analytics to real-time embedded analytics. One big transition that the industry has embarked upon is the move from long to short cycles for model building and deployment. A second major transition, enabled by the first, is to move from back-office or offline analytics to real-time embedded analytics. This second development allows insurers to truly operationalize analytics. Rather than running a model in a back room and then interpreting the results, formulating plans, and taking action, the new model with its fast results will drive automated decisioning at the transaction level. Certainly, both types of models will still be needed the offline models to analyze aggregate risk portfolios, spot customer trends, and identify economic factors, and then the embedded models to support underwriting decisions, flag suspicious claims, and make suggestions for customer interaction. Investments in Predictive Modeling That insurers are investing in predictive modeling is not in question. SMA research indicates that almost half of all insurers are investing in new predictive modeling projects in 2013, compared to approximately one-third in The focus and investments in this area are definitely increasing. The question is whether insurers are beginning to operationalize analytics. Are there projects underway that will embed analytics into the operational systems in various parts of the business? The following sections will demonstrate that the answer to this question is yes. Insurer plans and priorities for operationalizing analytics are reviewed for three key areas: marketing and sales, actuarial and underwriting, and claims, with these groups representing analytics for customers, risks, and services. The plans for insurers over and under $1B in premium are profiled, revealing some distinct differences in priorities. Key Project Areas Marketing and Sales One of the most exciting, active analytics areas in insurance is customer-centric analytics. Insurers are seeking to understand more about customers at the macro level, individual customers, and customer interactions. Marketing and sales are areas where the larger insurers are more active in using analytics technologies to identify patterns and trends, gauge the marketplace perceptions of their brand, and determine actions to take for individual policyholders. Figure 3 shows six categories of projects in which insurers are planning to embed more analytics over the next two years. Many insurers are leveraging analytics for the often complex task of achieving a single view of the customer. Just as 2 Breading, M. (2013). Data and Analytics in Insurance: P&C Insurers Plans and Priorities for 2013 and Beyond, and L&A Insurers Plans and Priorities for 2013 and Beyond, published May 2013 and June 2013, respectively. Strategy Meets Action: Boston, MA. Page 8

9 important to the larger insurers is understanding the performance of agents and advisors. As insurers strive to generate more demand and cross-sell to existing customers, they are increasingly using analytics for real-time assessments of campaign effectiveness. Figure 3. Insurer Plans to Embed Analytics in Marketing and Sales Systems (Percent of Insurers Planning Projects, Next 24 Months) Over $1B Under $1B 40% Single view of customer 31% 40% Channel and/or agent performance 19% 34% Customer analytics 34% 30% Campaign marketing and analysis 23% 25% Best next action 15% 20% Brand sentiment 15% Actuarial and Underwriting Source: SMA Research, Operational Analytics in Insurance 2013, n=46 The two primary areas of the insurance business associated with a deep understanding of risk are actuarial and underwriting. Five very different types of project areas were identified as candidates for operationalizing analytics for risk planning and evaluation, as depicted in Figure 4. Insurers of all sizes are concentrating on underwriting operations, providing automated support for underwriting decisioning. Half of all insurers under $1B plan for embedded analytics for telematics. This may seem surprising, but is consistent with other research showing that a high percentage of property and casualty insurers plan to use telematics for usage-based insurance over the next few years 3. Figure 4. Insurer Plans to Embed Analytics in Actuarial and Underwriting Systems (Percent of Insurers Planning Projects, Next 24 Months) Over $1B Under $1B 30% Underwriting Operations 39% 30% Enterprise Risk Management 27% 15% Telematics 50% 15% CAT Modeling 12% 10% Product Pricing 19% Source: SMA Research, Operational Analytics in Insurance 2013, n=46 3 Breading, M. & Welch, R. (2013). Telematics/Usage-Based Insurance: Insurer Priorities and Plans. Strategy Meets Action: Boston, MA. Page 9

10 The percentage of large insurers that are planning to embed analytics for telematics might seem surprisingly low. This is likely because they are already well along the path of analytics deployment. Claims Claims is a complex area with a wide variety of activities, data, and internal and external parties involved. As the largest expense category for any insurer, claims demands intense scrutiny to determine where loss costs can be reduced and resources more efficiently utilized. Figure 5 shows six specific areas where insurers are planning to embed analytics over the next two years. The smaller insurers are placing more emphasis on analytics for severity and resource planning. The larger insurers see the biggest opportunity area in the early identification of claims that are likely to be litigated. A greater percentage of small insurers are planning to focus on fraud detection and loss reserving (primarily because many larger insurers have already invested in these areas). Figure 5. Insurer Plans to Embed Analytics in Claim Systems (Percent of Insurers Planning Projects, Next 24 Months) Over $1B Under $1B 40% Litigation propensity 23% 30% Resource planning 35% 30% Claims recovery 12% 25% Claims severity 39% 20% Fraud detection 31% 20% Loss reserving 31% Source: SMA Research, Operational Analytics in Insurance 2013, n=46 Page 10

11 Capitalizing on the Opportunity A new movement is underway. Insurers are on the verge of gaining new and differentiating insights from their data. The operationalizing of analytics changing the speed from months to days, and beginning to embed analytics into real-time operational systems will change the game for every insurer. Insurers should seize the opportunities presented by predictive modeling, especially as they look to embed insights and actions into systems in marketing, sales, actuarial, underwriting, and claims. Now is the time to capitalize on this opportunity. Gain experience with the technologies, identify resources within the company with the ability to leverage their talents outside their chosen area, and work with technology partners that have the solutions and bench strength to support the initiatives. Apply the experience and expertise in modeling to new business areas, with innovative thinking about new value that can be derived from analyzing both new and old data. The true state of predictive modeling? It is shifting rapidly as this is written. This is the time for bold moves by insurers to capitalize on the opportunities offered in this exciting new era of analytics. About Strategy Meets Action Exclusively serving the insurance industry, Strategy Meets Action (SMA) blends unbiased research findings with expertise and experience to deliver business and technology insights, research, and advice to insurance companies and IT solution providers. By leveraging best practices from both the management consulting and research advisory disciplines, SMA s services are actionable, business-driven, and research-based where strategy meets action enabling companies to achieve business success. Additional information on SMA can be found at Mark Breading, SMA Partner can be reached at mbreading@strategymeetsaction.com or Follow Mark on About SAS, the White Paper Sponsor SAS IS THE LEADER in business analytics software and services, and the largest independent vendor in the business intelligence market. Drawing on more than 35 years of experience in financial services, SAS helps more than 1,200 insurers worldwide with data management, fraud detection, risk management, regulatory compliance, customer intelligence and other critical needs. The insurance industry contributed 12 percent of the total company software revenue of $2.87 billion in Learn more at Page 11 SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies _S