Harnessing the power of big data and analytics for insurance

Transcription

1 IBM Software White Paper Insurance Harnessing the power of big data and analytics for insurance

2 2 Harnessing the power of big data and analytics for insurance Data has always been the core of the insurance industry. To design products, assess risks, set policy rates, protect against fraud, ensure sufficient reserves, and retain customers and producers, insurance companies collect extensive data on claims, transactions, demographics, channel usage and more. But the nature of that data is changing. The volume, variety and velocity of data available to insurance companies are increasing rapidly. Real-time weather feeds, geospatial data, public records, and data captured by devices and sensors is being augmented by unstructured data from social media posts, videos, s and other fast-changing sources. How can insurers take advantage of unstructured data to gain fresh insight? What if an insurer could tap into call recordings, s, notes in medical records or comments in an application, service request or claims file across the organization, regardless of line of business? Could an insurer make better decisions or better understand policyholder behavior or motivation with this data-enhanced insight? Insurers have enormous amounts of data, but to fully capitalize on it they must be well prepared to accommodate traditional and nontraditional sources and become big data consumers and have the right analytical tools to make the data meaningful. Big data and analytics offer substantial opportunities for insurance companies to better inform actions and predict outcomes. For example, companies can use insights drawn from big data to: Determine the next best action in customer relationships Improve claims operations and fraud detection Use telematics data for more than just pricing Perform near-real-time catastrophe risk modeling Gain enterprise insight for actionable decision making Enhance producer effectiveness Transform contact-center operations The benefits of big data and analytics extend to every level of an insurance organization. Producers can maximize cross- and up-sell opportunities with real-time, personalized offers. Claims analysts and underwriters can analyze streaming and entity analytics to quickly flag fraud risks and fast-track legitimate claims. Actuaries can improve catastrophe risk modeling, applying analytics to trillions of records to improve reinsurance pricing and set limits while reducing exposure. For all these reasons, big data is becoming an important natural resource for insurance companies. By combining it with analytics, insurers can generate new insights that help transform the business, including: Acquiring and retaining customers Optimizing operations and countering fraud and threats Improving IT economics Managing risk Transforming financial management processes Creating new business models This white paper explores the value of big data and analytics in the insurance industry, and highlights how the IBM Big Data and Analytics portfolio is designed to deliver the capabilities insurers need to derive new and more complete insights from their valuable data resources. Customer analytics and insight Big data and analytics enable an insurer to market to a group of one rather than a group of many. Many insurers still focus on providing the best, most complete view of the customer in order to allow for the right analytics. A comprehensive view of the customer, analytics and insight enables companies to provide in-context offers and customer service support that is personalized for the policyholder and is relevant to the immediate situation.

3 IBM Software 3 For example, big data and analytics can help identify whether a customer is a retention risk or if the customer should be presented with a new product. This identification can take place as an agent or call-center representative is interacting with the policyholder. While a big data approach to customer analytics and insight provides many opportunities for action, some of the most visible outcomes include: Customer retention: By knowing customers, their needs and their propensity to churn, carriers can maximize retention efforts by suggesting the next best offer. Cross-sell and up-sell: Carriers can propose the right action to the right policyholder at the right time to maximize crosssell, up-sell, strategic lifetime value profitability and loyalty. Customer service: Insurers can leverage analytics to improve customer service delivery, resolve service issues and increase overall satisfaction with a better understanding of the policyholder s motivations. Boosting policy purchases by using customer data to tailor offerings AEGON Hungary Composite Insurance Co. Ltd, a subsidiary of the AEGON Group and one of the largest insurance providers of life insurance, asset insurance and pension products for individuals and businesses in Hungary, had huge amounts of raw customer data but lacked the ability to turn it into insight and cross-selling opportunities. Using powerful statistical analysis and modeling solutions from IBM, the company developed an innovative methodology to connect life events and situations to insurance needs; based on aggregate profiles and predictive behavior models, the insurer can craft insurance offerings to individual requirements. As a result of these efforts, the company improved customer response 78 percent through a targeted direct marketing campaign and increased policy purchases by 3 percent. Improving retention by identifying the right customers A large US insurer conducted extensive analysis on customer information files, transaction data and call-center interactions to identify customers who would respond positively to contact with an agent. Based on the analysis, the company then developed new product offers. The result was a significant increase in offer response rates and up to a 40 percent retention rate improvement. The goal for any insurer is to have policyholders feel that the carrier understands them as individuals and is responsive to their changing needs. Imagine someone who is unable to complete a policy inquiry on the company website and calls the contact center. The next best action might be for the agent to start the call with an apology for the inconvenience and then help the customer complete the inquiry using virtual chat. Timely, appropriate actions like these can significantly improve customer satisfaction and help keep valuable policyholders. Predictive analytics can even determine the likelihood that a customer will move to a competitor, allowing time to identify the next best action for retaining the customer. A big data enabled view of customer analytics and insight can also help insurance companies anticipate customer needs to maximize cross-sell and up-sell opportunities. Analyzing customer actions or tracking important life events enable a company to offer new types of policies or new levels of coverage that match a customer s current or future needs. For example, a company could offer a discount on bundled auto insurance as a policyholder s child nears driving age, or recommend a convertible life insurance policy after discovering that the policyholder has been comparing types of policies online.

4 4 Harnessing the power of big data and analytics for insurance Highly targeted offers can be automatically delivered through all channels of communication with the customer including agents, contact-center support, social media, chat, mobile, and regular mail or provided to agents in real time at the point of customer interaction. Creating personalized offers and delivering them as preferred by the policyholder help improve customer sentiment and increase profitability. Increasing cross-sell with improved segmentation After several years of rapid growth, a large Korean non-life insurer wanted to boost revenues by improving its competitive position. The company needed to enhance the effectiveness of its large, distributed network of affiliated agents by providing them with the insights and tools to identify opportunities and implement more targeted and relevant cross-selling offers. The company deployed a comprehensive customersegmentation and market-targeting solution built on IBM technology. By applying analytics and predictive modeling to customer account data and transaction histories, the solution enables agents to conduct segmentation based on the probability customers will adopt complementary or higher-value insurance services. By analyzing customers consumption of services, the company can optimize cross-selling strategies, fine-tune marketing messages and deliver targeted offerings. The insurer can more accurately predict which insurance products are the most appropriate for each customer. Offering the right mix of services improves the effectiveness and efficiency of the company s sales force, while the more personalized touch helps agents forge closer bonds with customers, which enhances loyalty. Each insurer will have a different approach for implementing big data and analytics for customer relations, but it is critical that big data remain at the core of the analysis and be drawn from as many sources of data as possible. This enables the insurer to deliver the optimal offer at the right time and in the right way for each policyholder. Claims analytics, optimization and fraud detection Big data and analytics can play a critical role in addressing the increasing prevalence of claims fraud. In a 2013 FICO study, more than one-third (35 percent) of respondents estimated that fraud represents 5 to 10 percent of total claims, while 31 percent said the cost is as high as 20 percent. And those percentages are rising; more than half of insurers expect to see an increase in fraud losses on personal insurance lines. 1 Insurance companies need ways to quickly identify potentially fraudulent claims, enhance the efficiency of investigations and prosecutions, and facilitate rapid reporting and visualization to improve ongoing antifraud efforts. Big data and analytics can be incorporated into any existing fraud environment to quickly gain additional insights. At the underwriting stage, insurance companies can employ big data and analytics solutions that scrutinize applicant identities by searching and analyzing large volumes of information rapidly. Companies can determine whether applicants and people associated with those applicants have been linked to fraud in the past. Through a review process, companies can avoid fraud by denying applications for disability, health, homeowner or automobile policies with high fraud risks.

5 IBM Software 5 Identifying insurance fraud with big data analytics The Insurance Bureau of Canada (IBC) is the national insurance industry association representing Canada s home, car and business insurers. Because investigation of cases of suspected automobile insurance fraud often took several years, the company s investigative services division wanted to accelerate its process. The IBC worked with IBM to conduct a proof of concept (POC) in Ontario, Canada that explored new ways to increase the efficiency of fraud identification. The POC showed how IBM solutions for big data can help identify suspect individuals and flag suspicious claims. IBM solutions also help users visualize relationships and linkages to increase the accuracy and speed of discovering potential fraud. In the POC, more than 233,000 claims from six years were analyzed. The IBM solutions identified more than 2,000 suspected fraudulent claims with a value of CAD41 million. IBM and the IBC estimate that these solutions could save the Ontario automobile insurance industry approximately CAD200 million per year. To learn more about the IBC POC with IBM, visit: ibm.com/software/success/cssdb.nsf/cs/jhun-8y4j5a During claims intake, companies can access and leverage information in unstructured areas of applications, and supplement it with social media posts or geospatial data to inform investigations and policy decisions. Streaming data can help insurers discover, for example, whether policyholders are being honest about accident details or if medical or repair services rendered are legitimate. Predictive analytics solutions can help categorize risk and deliver fraud propensity scores to claim intake specialists in real time so they can adjust their line of questioning and route suspicious claims to investigators. For ongoing analysis of fraudulent claims and their impact on the business, companies can use solutions to analyze reports and create visualizations of data patterns. Another option is to pursue new and emerging sources of analyzable information to conduct periodic analysis of emerging trends. Combining structured and unstructured data, as well as internal and external information, can identify emerging trends in fraud and leakage. These new indicators can be applied against open claims to determine if additional research and investigation are warranted. While claims fraud is certainly a key use of big data and analytics in claims operations, the value of big data and analytics extends further. Insurers see a measurable improvement in customer satisfaction through a reduction in false positives with higher accuracy. Legitimate claims can be processed much more quickly, to the delight of policyholders. Preventing fraud while improving customer satisfaction Infinity Auto Insurance created a way to score claims to provide a more systematic, efficient and accurate way to pinpoint fraud and the company is using the same intelligence to create a smarter claims processing workflow. Claim information is automatically fed to a scoring engine that drives the processing workflow, including referrals to fraud investigators. The ability to identify express claims as they are received speeds resolution, improves customer satisfaction and lowers the cost of adjusting claims. Infinity realized a 400 percent ROI within six months of the implementation, and increased its success rate in pursuing fraudulent claims from 50 percent to 88 percent.

6 6 Harnessing the power of big data and analytics for insurance Insurance telematics Usage-based insurance (UBI) or telematics products are necessary for insurers to remain competitive for both personal-line insurance carriers as well as commercial carriers that insure fleets. By analyzing driving data and routes and incorporating them into insurance pricing, UBI and telematics provide benefits in the form of reduced premiums for lower-risk drivers. Big data and analytics solutions enable an insurance company to manage and analyze the large amounts of telematics data generated each second by every vehicle in motion. The big data solution must be able to receive and manage data provided by multiple telematic service providers, smartphone apps, wireless carriers and OEMs. In addition, the solution must address data quality variances due to connectivity levels and the devices generating the data. Big data and analytics allow insurers to support the varied amounts and types of data for all UBI-style products, from pay-as-you-drive (PAYD) to pay-how-you-drive (PHYD). An insurer can start with a basic UBI product and evolve to a more sophisticated pricing model over time, such as including driving behavior, without changing the big data platform. This approach also factors in non-use for leisure vehicles such as yachts, motorcycles or vintage cars. These capabilities place even more precision into the underwriting process. In the case of commercial insurers, premiums are based on the duration and risk at each stage and are unique to each commercial shipment. But what about using the data captured to go beyond pricing models? That is becoming more common as well: insurers are now able to use telematics data to improve actuarial risk patterns, automate first notice of loss processes based on incoming telematics, improve customer driving behavior, support gamification and protect against certain types of fraud such as car thefts. Big data and analytics enrich telematics data with geospatial data, topographical road data and weather data to provide context. Dashboards provide business analysts with data for modeling activities that were previously impossible. Catastrophe risk modeling Big data and analytics solutions designed to handle the volume and variety of data available today also help insurance companies improve catastrophe risk modeling, which is used to determine the exposure of current policies and predict the probable maximum loss (PML) from a catastrophic event. Catastrophe risk modeling is vital for setting policy prices and risk financing using reinsurance and the capital markets. With big data and analytics capabilities, insurers can model losses at the policy level, calculating the effect of a new policy on the portfolio while it is being quoted. They can also accelerate the speed and increase the precision of catastrophe risk modeling. In the past, actuaries were restricted by the inability to run frequent, fast models. Now, companies can use solutions for big data to integrate historical event data, policy conditions, exposure data and reinsurance information. With a powerful data warehouse built for big data, insurance companies can analyze trillions of data rows and rapidly deliver results that underwriters can use to price policies by street address, proximity to fire stations or other granular parameters, rather than simply by city, county or state. A big data enabled solution allows pricing models to be updated more often than a few times a year, and specific risk analysis can be determined in minutes rather than hours.

7 IBM Software 7 Reducing losses and increasing pricing accuracy A large global property casualty insurance company wanted to accelerate catastrophe risk modeling in order to improve underwriting decisions and determine when to cap exposures in its portfolio. The current modeling environment was too slow and unable to handle the large-scale data volumes that the company wanted to analyze. The goal was to run multiple scenarios and model losses in hours, but the current environment required up to 16 weeks. As a result, the company conducted analysis only three or four times per year. A proof of concept demonstrated that the company could improve performance by 100 times, accelerating query execution from three minutes to less than three seconds. The company decided to implement IBM solutions for big data, and can now run multiple catastrophe risk models every month instead of only three or four times per year. Once data is refreshed, the company can create what-if scenarios in hours rather than weeks. With a better and faster understanding of exposures and probable maximum losses, the company can take action sooner to change loss reserves and optimize its portfolio. A solution built to analyze streaming data enables companies to capture and analyze real-time weather, geographic and disaster data for risk mitigation on both personal and commercial lines. Predictive modeling solutions can help companies anticipate those losses before disasters strike or as events unfold. Enterprise portfolio management The goal of portfolio management is to provide business decision makers with the tools to understand profitability and improve their business mix by identifying risk aggregations, and both outperforming and underperforming business segments. Traditionally, staff performing various portfolio management functions had to spend much of their time managing data and little time gaining insight and taking action. Many types of data were generally limited to manual analysis. As a result, insights might not be available until business was already placed, reinsurance acquired and losses incurred. Today, insurance and reinsurance carriers can use big data, discovery and analytics capabilities to rapidly understand their total exposure and performance drivers, and then leverage this insight to proactively manage the portfolio for the desired business results. Proactive portfolio management provides the ability to segment the book of business, identify performance improvement opportunities and take action collaboratively within and across profit centers, regions and countries. Linking data sources provides access to attributes across key insurance data domains and enables an understanding of profitability over various dimensions, including product, location, channel, reinsurance details, policyholder demographics and exposure. Carriers can prioritize their big data efforts according to the many possible use cases enabled by a big data approach to portfolio management. The IBM Big Data and Analytics suite allows the ingestion of massive amounts of data without a data schema or significant transformation required, providing a repository that is available, granular, explorable and costeffectively built on a foundation of open-source software and commodity hardware.

8 8 Harnessing the power of big data and analytics for insurance Producer analytics and effectiveness solutions Producer effectiveness solutions focus on two elements: more effective support models for existing producers and more effective analytics to attract, retain and optimize the producer workforce. Traditional optimization measures show a limited view by focusing on premiums written and loss ratio management. Leveraging big data analytics, companies can now estimate the potential of a market and then evaluate a producer s contribution of that share of the business. This analysis can also be used for target setting and training to improve overall market penetration. Whether a producer is a captive or an independent agent, a financial advisor or an intermediary, the use of big data analytics can help drive top-line and bottom-line growth. Companies can leverage analytics across a broad range of producer information, including interaction history with the carrier and customers, social media capabilities, claims, payments and agent histories to understand which characteristics predict successful behaviors. The findings can be used to search and screen for new producers and to help existing producers increase their performance. These efforts can be supported by next best actions delivered through value-added activities such as training and lead sharing, or existing business flows such as quoting. Big data and analytics can also help make producers smarter about their customers, so they can anticipate customer needs more effectively and help retain business. Sharing cross-sell offers and sentiment analysis with the producer community can add to the producer s business and drive incremental revenues to the carrier company. Analytics allow for the matching of producers and customers to drive cross-sell and up-sell opportunities, help carriers maintain wallet share and reduce policy exchanges or cancellations. Transforming contact-center operations The contact center is a critical interface with customers and a key element in customer satisfaction and retention. By leveraging big data and next best action analysis, insurance organizations can transform the contact center to achieve improved timeliness and quality of service, greater cost-efficiencies, improved regulatory compliance and revenue uplift. In many contact centers, current processes are timeconsuming and labor-intensive. The customer service representative (CSR) must ask the customer questions to understand the situation and customer need. The CSR then researches customer questions by consulting policies and procedures, looking up data, consulting other agents and possibly transferring the call. After clarifying the question based on research and providing the best possible answer, the CSR tries to ensure the customer is satisfied and probes for other needs before closing the inquiry. Contact-center transformation focuses on optimizing information, people and processes to meet customer needs. CSRs are empowered to search a highly organized knowledge base to better predict customer needs and help drive a differentiated experience that sets the company apart. The knowledge base is built in part by analyzing customer complaints and survey data to understand key customer issues, measuring the influence of social media on customer behavior and influence, and analyzing data from a variety of other sources such as agent-to-agent internal memos, customer interaction notes and s.

9 IBM Software 9 Companies can implement a knowledge management solution in phases first establishing a searchable knowledge database organized by need, with structured actions and procedures for the CSR to follow. The next phase is to incorporate interactions and transactions into generating a list of potential reasons for a call and prioritizing next best actions corresponding to those reasons. Finally, systems can be used to automatically provide suggestions for specific customer inquiries as well as suggestions for customer treatments based on interaction history. With less reliance on CSR-only knowledge and manual processes, insurance companies can reduce errors and time-toresolution while driving new revenue opportunities with higher rates of conversion. Overall call volume can be reduced because the company can anticipate issues and contact the customer with an NBA recommendation before the customer feels the need to call. Proactive problem identification and resolution help the customer avoid frustration and increase loyalty based on a positive experience. As a result, the customer is more willing to increase total wallet share and influence social circles to consider the company. The IBM Big Data and Analytics portfolio Big data and analytics implementations are determined by an organization s specific requirements. IBM offers the services, solutions, platform and infrastructure necessary to deliver a wide variety of big data and analytics initiatives. IBM Big Data and Analytics platform capabilities The IBM Big Data and Analytics platform can handle all types of data, support all types of decisions and pursue every business opportunity. Organizations can infuse analytics into the entire business and continue to support governance, privacy and security requirements. Depending on its needs, a company can start with a small big data and analytics strategy, and then expand at its own pace. The platform includes the following components and capabilities: Decision management automatically delivers high-volume, optimized decisions to systems and front-line workers through predictive modeling, business rules and optimization. Content analytics find, organize, analyze and deliver insight from textual information that is found in documents, , web content and more using intuitive natural language recognition and categorization. Planning and forecasting enable more dynamic and efficient planning cycles such as target setting, forecast rollout, reporting, analysis and reforecasting. Cognitive systems navigate natural language, explore vast disparate data sources and learn from every interaction in an intuitive experience. Discovery and exploration capabilities provide a contextrelevant view of a business through federated navigation, visualization and interaction with a broad range of internal and external data sources and data types. Business intelligence delivers insight to users with dashboards, reports, analysis and modeling on desktops, the web and mobile devices. Predictive analytics perform statistical analysis, data and text mining, and predictive modeling to reveal patterns and trends from structured and unstructured data. Data management lets companies take advantage of industryleading database performance with multiple workloads while lowering administration, storage, development and server costs. Content management allows comprehensive content lifecycle and document management with cost-effective control of existing and new types of content with scale, security and stability. Hadoop systems bring the power of Apache Hadoop to businesses; IBM solutions combine Hadoop administrative, discovery, development, provisioning and security features with the analytical capabilities of IBM Research.

10 10 Harnessing the power of big data and analytics for insurance IBM offers services and solutions tailored to big data and analytics needs in any industry IBM has significant industry and domain experience to help companies develop their own big data and analytics strategies. The business analytics and optimization services provided by IBM include consulting in many areas: Business strategy and transformation Industry use cases and value accelerators Digital front office and customer analytics Finance, fraud and risk analytics Operations and supply chain analytics Services managed by big data and analytics Analytics centers of excellence Data exploration and visualization In addition, IBM provides organizations with an outcomedriven portfolio of solutions, original IBM Research projects and 60 different use cases that apply to 17 industries. To craft a strategy that addresses an organization s specific needs, IBM developed the following set of critical solutions that best display the capabilities of big data and analytics: Customer engagement advisor IBM Social Media Analytics IBM Performance Management Antifraud, waste and abuse IBM Risk Analytics IBM Customer Analytics and network analytics IBM Organization and Workforce Transformation Sales performance Case management IBM Predictive Maintenance and Quality Defensible disposal Stream computing analyzes massive volumes of streaming data in near-real time by deploying advanced analytics in a highly scalable runtime environment. Data warehouses allow companies to gain speed with capabilities optimized for analytics workloads. Systems that can be installed and running in hours enable organizations to quickly realize the benefits of data warehouses that are optimized for big data and analytics. Information integration and governance features build confidence in big data and analytics with the ability to integrate, understand, manage and govern data appropriately through its lifecycle. An integrated, high-performance big data and analytics infrastructure The IBM Big Data and Analytics infrastructure includes a well-integrated set of server, storage, networking and systems software technologies. It is capable of accelerating the flow of data and insights, providing shared and highly secure access to all types of data regardless of location, and significantly improving the availability of information. Scalability: Organizations can choose to scale-in, scale-up or scale-out infrastructure to support the complexity and breadth of analytics workloads. Parallel processing: Parallel processing enhances data processing and ingestion through workload and data layer parallelism that uses distributed analytics processing. Low latency: The IBM Big Data and Analytics infrastructure provides discrete speed enhancements to accelerate analytics workloads. To learn more about IBM services, visit ibm.com/services/us/ gbs/business-analytics. To learn more about IBM big data and analytics solutions, visit ibm.com/software/data/bigdata

11 IBM Software 11 Data optimization: Companies that optimize their data assets can implement storage solutions that provide speed, scale, quality of service and reliability gains for data-iterative applications. Security: When security is enhanced with big data and analytics solutions, companies can use that insight to manage risk from cyberattacks through cloud and mobile environments. Distribution of these security capabilities is enabled by advanced analytics from security intelligence. Cloud: Companies can choose between private, public or hybrid cloud delivery for simple, powerful cloud solutions. Serving customers, preventing fraud and reducing uncertainty IBM solutions for big data and analytics offer insurers valuable capabilities for enhancing customer service, identifying fraud and improving pricing. With enhanced information insight, companies develop a greater understanding of customer needs, better anticipate behaviors and deliver targeted offers in real time at the point of contact. They can rapidly prevent, predict, detect and investigate potentially fraudulent claims and enhance the efficiency of ongoing antifraud efforts. Big data and analytics can also help improve the performance and speed of catastrophe risk modeling to increase the precision of policy pricing, optimize reinsurance placement and determine when to cap a book of business based on risk. With the IBM big data platform, insurance companies are better equipped to harness big data to develop a more customer-centric enterprise, reduce risks and sustain a competitive edge. IBM big data platform at a glance The IBM big data platform is designed to help organizations gain insight from big data by delivering enterprise-class data management and advanced analytics. It includes the following components: IBM InfoSphere BigInsights provides an integrated solution for analyzing hundreds of terabytes, petabytes or more of raw data derived from an ever-growing variety of sources. InfoSphere Streams is a state-of-the-art computing platform that can help companies turn burgeoning, fast-moving volumes and varieties of data into actionable information and business insights. InfoSphere Data Explorer offers federated discovery, search and navigation over a broad range of data sources to help organizations get started quickly with big data initiatives and acquire more value from their information. Robust IBM data warehouse software and integrated systems help simplify and accelerate the delivery of insights derived from your data. IBM PureData System for Analytics is a highperformance, scalable, massively parallel system that enables clients to uncover useful insights from their data and perform analytics on enormous data volumes. IBM PureData System for Operational Analytics part of the IBM PureSystems family is an expert integrated data system designed and optimized specifically for the demands of an operational analytics workload.

12 For more information To learn more about how IBM solutions can help insurance companies capitalize on big data and analytics, please contact your IBM representative or IBM Business Partner, or visit: ibm.com/big-data/insurance Copyright IBM Corporation 2013 IBM Corporation Software Group Route 100 Somers, NY Produced in the United States of America December 2013 IBM, the IBM logo, ibm.com, BigInsights, InfoSphere, PureData, and PureSystems are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at Copyright and trademark information at ibm.com/legal/copytrade.shtml This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions. THE INFORMATION IN THIS DOCUMENT IS PROVIDED AS IS WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON- INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. The client is responsible for ensuring compliance with laws and regulations applicable to it. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the client is in compliance with any law or regulation. Actual available storage capacity may be reported for both uncompressed and compressed data and will vary and may be less than stated. 1 FICO Insurance Fraud Survey Highlights wp-content/secure_upload//fico_insurance_fraud_survey_2973ex. pdf#page=1&zoom=auto,0,792l Please Recycle IMW14672-USEN-01