Time to Query Your Quotes? - Overlooked Data Delivers Powerful Business Insight
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Time to Query Your Quotes? Overlooked Data Delivers Powerful Business Insight ORACLE WHITE PAPER | FEBRUARY 2015
Table of Contents Introduction 1 Discover a Hidden Treasure Trove 2 Removing Roadblocks 3 Think Holistically 3 Conclusion 4 TIME TO QUERY YOUR QUOTES?
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 up-
selling, expanded insight into agent productivity, as well as faster identification of potential fraud.
1
Industry Overview – Insurance Information Institute. http://www.iii.org/es/node/32054
1 | TIME TO QUERY YOUR QUOTES?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 2014. http://www.iii.org/issue-update/insurance-fraud
2 | TIME TO QUERY YOUR QUOTES?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?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?
Oracle Corporation, World Headquarters Worldwide Inquiries
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Redwood Shores, CA 94065, USA Fax: +1.650.506.7200
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