Harnessing the power of big data and analytics for insurance

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

2 Harnessing the power of big data and analytics for insurance
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.
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, emails 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,
emails, 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

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 cross-
sell, 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.
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.
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.

Highly targeted offers can be automatically delivered through
all channels of communication with the customer—including
agents, contact-center support, social media, chat, mobile,
email 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.
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.
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 customer-
segmentation 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.

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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.
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
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.

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.

IBM Software 7
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.
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.
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 cost-
effectively built on a foundation of open-source software and
commodity hardware.

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 time-
consuming 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 emails.

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-to-
resolution 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, email,
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 context-
relevant 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 industry-
leading 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.

• 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.
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 outcome-
driven 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
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

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 high-
performance, 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.

© Copyright IBM Corporation 2013
IBM Corporation
Software Group
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Somers, NY 10589
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 2013. www.fico.com/en/
wp-content/secure_upload//FICO_Insurance_Fraud_Survey_2973EX.
pdf#page=1&zoom=auto,0,792l
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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

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