Best Practices for Leveraging Business Analytics in Today s and Tomorrow s Insurance Sector

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1 Best Practices for Leveraging Business Analytics in Today s and Tomorrow s Insurance Sector Mark B. Gorman Principal, Mark B. Gorman & Associates LLC January 2009 Executive Summary Sponsored by

2 Report Coverage It started as most things do, rather innocently a simple research project on the use of business analytics in the insurance industry. The three gentlemen on the research interview call were adamant. We know we have a ways to go, but we re getting there. We re making progress. We ve come a long way. My question in return was straightforward: How do you define there and how will you know you ve arrived? After several seconds of silence one of them said, That s a good question... This report is an attempt to answer that question based on interviews in late July, August and September of The interviews were with 15 thought leaders in business analytics in the insurance industry. These individuals, all with significant experience in starting, growing, using or managing business analytics in their organizations, shared their insights and expertise in describing the cultural, organizational, technological and personnel challenges and changes required for their organizations to more broadly adopt and utilize business analytics. My goal is not to describe what the insurance market is using business analytics for, but how the use of business analytics is changing the insurance market, and what organizations need to consider to make that transformation more powerful, more palatable and more profitable. While the intellectual stimulation, the challenges to traditional thinking and the enthusiasm for the topic were undoubtedly the interviewees, the conclusions that follow are mine. Background Profitable growth is an elusive goal in today s increasingly competitive insurance industry. Rapid development and deployment of new products and product features, balancing broader distribution channel opportunities, managing risks across the organization, responding to increasingly demanding regulatory and reporting agency demands, and providing more precise pricing levels require effective decisions to be made with greater accuracy, efficiency and transparency. Personal experience, while still valuable, is often insufficient in meeting the requirements for consistent, accurate and effective decisions in a rapidly changing marketplace. Leading organizations are increasingly turning to business analytics as a key means of survival. Business analytics solutions are being used by insurers to reduce the time required to react to competitive pressure, to respond efficiently to market changes, to increase effectiveness of business managers in improving financial results and driving value for the organization, to more effectively manage the many risks the enterprise faces, and to improve the precision and efficiency of operational decisions. Yet, far-reaching, enterprisewide programs are still an anomaly. Cultural issues are often the reason for limited enterprise programs. Why and what can be done are the focus of this report. 1

3 For this report, the primary forms of business analytics include (see Figure 1): a. b. c. d. e. Ad Hoc Management Reporting and Dashboards. These are business analytics solutions where analysis and reporting tools are used to provide automatic feedback on the achievement of key performance criteria. They are also used to do ad hoc reporting on data from a variety of data sources in order to improve management s ability to make better and faster decisions. Common examples include claim reporting and settlement lag time, call center response times, achievement of service standards, etc. Profiling and Segmentation. These business analytics solutions involve data mining to determine the historic behavior of a group, or the performance of a grouping of people, risks or transaction types. Common examples include customers by profitability, claim types by severity or frequency, customers by product preference, etc. Forecasting. This business analytics solution allows an insurer to attempt to determine a time series estimate of what will happen in the future based on statistical evaluation of current and historic aggregate data. Predictive Analytics. This business analytics solution attempts to predict future behavior or performance based on an analysis of historic transactional data, thirdparty data (like loss history, motor vehicle, geodemographic data, credit data, etc.) or derived data often calculated from one or more data elements. The analysis often results in a score or recommended action being assigned during the processing of a transaction. Examples include: determining the loss ratio relativity of a risk being underwritten, the pricing adequacy based on anticipated loss experience, the propensity for fraud on a reported claim, etc. Optimization. This business analytics solution focuses on optimization of business decisions usually based on multiple scenarios or multiple predictive analytics models. For insurance, optimization is almost always constrained optimization. For example, maximizing response to a direct response campaign, constrained by a marketing budget. 2

4 Figure 1: Primary Forms of Business Analytics: Data Insight and Business Value HIGH Data Insight Required LOW LOW Business Value Derived HIGH In the following paragraphs, I use the terms line of business and functional units quite often. As most readers will know, the granularity of the term line of business differs from organization to organization. For one insurer, line of business may mean personal lines insurance. For another, personal lines auto and personal lines homeowners are both lines of business. Since the granularity matters less than the amount of autonomy granted to most line-of-business units in insurance, I m referring to the broad concept that encompasses both examples. The same holds true for the term functional units. Examples of functional units in insurance include claims, underwriting, marketing, finance, etc. Here too, functional units may operate very autonomously from one another. It is the autonomous nature of these operations that I m referring to below. 3

5 Summary The use of business analytics continues to permeate the insurance marketplace. New tool capabilities, new business models and new data sources are constantly emerging. The successful organizations will be those that concentrate as much on the successful implementation and deployment of business analytics as on the technology. For these firms, creating a culture where thought leadership has an equal place with operational excellence, and where business analytics solutions are both centralized and decentralized within the organization, will be the focus. The franchise model provides a straw man for having this discussion internally by removing the either/or elements from the discussion. Finally, those organizations that develop job families for analysts and that can recognize and compensate based on contributions toward success will be the most successful in attracting, hiring and retaining the resources required for future success. To access the entire research report, please go to SAS Institute Inc. World Headquarters To contact your local SAS office, please visit: 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. Copyright 2009, SAS Institute Inc. All rights reserved

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