Time to Query Your Quotes?

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1 Time to Query Your Quotes? Overlooked Data Delivers Powerful Business Insight ORACLE WHITE PAPER FEBRUARY 2015

2 Table of Contents Introduction 1 Discover a Hidden Treasure Trove 2 Removing Roadblocks 3 Think Holistically 3 Conclusion 4 TIME TO QUERY YOUR QUOTES?

3 Introduction As property and casualty (P&C) insurers seek a competitive advantage in a challenging market, information and insight are essential. As such, in recent years analytics have played an increasingly vital role in carriers ability to compete profitably. Insurers have long worked to analyze customer, claims, and profitability data to effectively manage their enterprises from assessing risk to uncovering new market opportunities to identifying potential instances of fraud. There is one important area, however, that has largely been ignored quote data. The 2,600 P&C insurers in the United States wrote net premiums totaling $456 billion in 2012 alone 1 a figure that represents millions of quotes collectively. In most organizations, quote data is, at best, siloed, and, at worst, discarded resulting in a lost opportunity for valuable business insight. Insurers that focus on making the most of quote data can realize several important benefits, including more competitive products and pricing, cost reduction, new opportunities for cross-selling and upselling, expanded insight into agent productivity, as well as faster identification of potential fraud. 1 Industry Overview Insurance Information Institute. 1 TIME TO QUERY YOUR QUOTES?

4 Discover a Hidden Treasure Trove Quote data represents a valuable trove of information that can deliver significant insight into four aspects of the P&C business:» Product/Pricing Strategy Careful analysis of quote data specifically the binding ratio can yield important visibility into the effectiveness of a P&C insurer s product and pricing strategies. For example, an insurer might discover that its agents are writing quotes for entry-level policies in regions with large populations of recent immigrants, but are not binding the policies. Careful analysis of this trend might reveal that pricing is too high or the products offered do not meet local market needs. In another instance, a rise in the number of renewal quotes a few months before policies are set to expire, followed by a higher than normal non-renewal rate, may indicate that an insurer s pricing is no longer competitive. Further, quote data can provide insight into population movement from urban to suburban areas or vice versa, as well as illustrate larger national demographic trends.» Expense Control Analysis of quote data can support various expense control initiatives an ongoing priority for P&C insurers. For example, by proactively managing attrition after endorsement quotes and renewal quotes, insurers can increase policy renewals and reduce underwriting costs. Historically, costs to renew a policy are much lower than to secure a new customer and issue a new policy. Renewal customers also tend to be more profitable the longer they remain customers. In addition, insurers can leverage quote data to assess the productivity of agents, gaining insight that they can use to adjust commission rates for non-performing agents, further reducing operating costs.» Cross-Selling and Up-Selling Undisclosed risk is a perennial challenge for insurers on several fronts. It complicates pricing and can compromise a firm s ability to effectively underwrite policies. In addition, it represents unrealized revenue. In the P&C sector, undisclosed drivers are one of the most common examples of undisclosed risk. Using analytics, an insurer could readily identify instances in which a policyholder requests a quote for adding a new teen driver to a policy, but then never follows through with a policy amendment. With this important insight, the insurer (or agent) could follow up with the customer in a timely manner to resolve the issue and ensure adequate coverage. Analytics can also boost the effectiveness of cross-selling initiatives. Consider, for example, a customer who requests a homeowner s insurance quote for a new property that he is purchasing. Analytics could help an insurer identify tandem opportunities with that customer, including a need for mortgage insurance or a life insurance policy.» Fraud Identification Fraud is a growing concern across the insurance industry, and the P&C sector is not immune. According to the Insurance Information Institute, Insurance industry estimates generally put fraud at about 10 percent of the property/casualty insurance industry s incurred losses and loss adjustment expenses each year, although the figure can fluctuate based on line of business, economic conditions, and other factors. Using this measure, over the five-year period from 2008 to 2012, property/casualty fraud amounted to about $33 billion each year. 2 Fraud can take many different forms, including double-insuring an asset. By analyzing quote data, an insurer can quickly determine if multiple individuals are requesting quotes on the same asset, which could point to possible fraud. In addition, analyzing quote data can help to identify agents involved in rate evasion such as setting up a policy for automatic withdrawals to secure a lower down payment for a client without a checking account and then removing the automatic withdrawal indication before the first payment is due.» Agent Management Quote data can reveal important insight into the performance of an insurer s agent base, such as the number of quotes generated and quote-to-bind ratio for specific agents, offices, and even regions. In addition, analysis of quote data can help an insurer to identify situations in which an agent might be abusing free underwriting reports, such as C.L.U.E. Personal Property reports, C.L.U.E. Auto reports, motor vehicle driving reports, and inspections, to shop and bind with another carrier. Equipped with this information, an insurer can address the issue and potentially adjust the agent s contract if a binding rate threshold is not met. In addition, an insurer might determine a trend in which the majority of endorsement quotes associated with policies sold by a specific independent agent are coming directly to the insurer as opposed to coming via the agent. This trend may flag a customer service issue and merit a discussion with the agent and possible commission adjustment. 2 Insurance Fraud, Insurance Information Institute, March TIME TO QUERY YOUR QUOTES?

5 Removing Roadblocks Few insurers would disagree that quote data can be valuable to their business. Most, however, fail to put it to work for their enterprises because of long-standing challenges in managing and turning quote data into usable and actionable information. Data volume is often a large hurdle. Insurers produce millions of quotes annually, often creating more than a dozen configurations for a single policy and customer. As a result, it is difficult for many enterprises to determine what quote information is valuable to retain and which data can be discarded. With expense control a top priority in a tight market, many insurers retain only the final quote iteration due to IT system memory and storage considerations, and the costs associated with them. As a result, insurers are literally throwing away valuable business information and opportunities. These challenges are far from insurmountable with a modern analytics environment that enables a holistic view of enterprise performance and empowers line-of-business managers to make decisions based on accurate data in a timely and cost-efficient manner. Think Holistically When assessing a legacy analytics environment and identifying requirements moving forward, insurers should keep the following considerations top of mind:» Can our environment support an enterprise approach to analytics? This capability is a threshold criteria for an effective analytics environment. Enterprise information such as quote, claims, profitability, attrition, and risk data and much more cannot be analyzed in isolation as there are complex relationships between these factors. To expand business insight, insurers require the ability to explore these complex relationships across the enterprise and the flexibility to analyze them at various levels, including by product, region, line of business, and more.» Do we have a comprehensive, industry-specific, and unified data model? Any insurer that has undertaken a data warehouse initiative understands that creating the data model is one of the most critical parts of any successful enterprise analytics initiative as it forms the foundation for all insight. Historically, it also has been one of the most complex, expensive, and time-consuming components of such projects. Firms can save considerable time and costs with a commercially available data model. It is important to remember, however, that a one-size-fits-all approach does not apply to data models. Instead, it is essential that insurers seek a model that is purpose built for the industry and incorporates the vendor s vast experience in the sector.» Is our analytics environment easy for business managers to use, and does it deliver the information they need? Today s insurance professionals require timely insight on demand. As such, analytics solutions must allow line-of-business managers to rapidly create their own queries and reports without relying on the IT team for support. In addition, the ability to quickly create robust personalized dashboards, which incorporate drill-down capabilities, further extends executive insight and should be a fundamental component of any analytics solution.» Can our analytics environment handle many different types of information? Modern enterprises, including insurers, are not only dealing with unprecedented data volumes, they also have more types of data both structured and unstructured than ever before. An analytics solution must effectively integrate data from various enterprise systems such as quotes, policy administration, claims, customer service, and financials. It also must accommodate a rapidly growing universe of unstructured data, including video, and even social media content. As a rule, pre-built integrations as well as service-oriented architectures reduce complexity, speed time to value, and reduce total cost of ownership.» Does our analytics solution provide pre-built statistical models that can be managed centrally and reused across the enterprise? Pre-built models save time and reduce costs, and firms can have confidence that they are industry proven. Similarly, the ability to build a statistical model once and reuse it many times allows firms to realize important efficiency gains and mitigate risk. A model library, which enables insurers to store and manage models from a central location, is also paramount as it promotes accountability, efficiency, and consistency. 3 TIME TO QUERY YOUR QUOTES?

6 Conclusion Today s P&C insurers are eager to embrace change and think creatively as they seek new ways to achieve a competitive advantage in a challenging business climate. No stone can go unturned, and this includes cultivating untapped sources of business insight such as quote data. Historically, quote information was disregarded due to the sheer volume of data and the complexity of building an environment to analyze it. Times and technology have changed. Today, when looking at the powerful benefits that insurance quote data (and many other kinds of enterprise data) can deliver combined with the availability of highly robust commercial solutions that deliver rapid time to value the case for deploying an enterprise-wide analytics environment that can accommodate may different data types and sources is irrefutable. 4 TIME TO QUERY YOUR QUOTES?

7 Oracle Corporation, World Headquarters Worldwide Inquiries 500 Oracle Parkway Phone: Redwood Shores, CA 94065, USA Fax: CONNECT WITH US blogs.oracle.com/nsurance facebook.com/oracleinsurance twitter.com/oracleinsurance oracle.com/insurance Copyright 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only, and the contents hereof are subject to change without notice. This document is not warranted to be error-free, nor subject to any other warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantability or fitness for a particular purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without our prior written permission. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Intel and Intel Xeon are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are trademarks or registered trademarks of Advanced Micro Devices. UNIX is a registered trademark of The Open Group TIME TO QUERY YOUR QUOTES?

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