The property and casualty insurance industry has seen declining profits in recent years due to lower investment returns and high claims costs. Fraud represents a significant portion of claims costs, estimated at $30 billion annually in the US alone. Predictive analytics can help insurers more efficiently identify fraudulent claims, recover costs through subrogation, optimize staff scheduling, and improve loss reserving. Early adopters of predictive analytics in claims processing are seeing returns of over 100% and improved customer retention compared to companies that have not adopted these techniques.

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FROM THE JUNE 2012 ISSUE OF TECH DECISIONS • SUBSCRIBE!
Finding Profits
The property and casualty industry has not had a return on equity (ROE) better than the Fortune 500 since 1987. The last times it was even close were 1991
and 1993. Even if the Fortune 500 is not considered the right benchmark, the industry average ROE has declined from a high of 12.7 percent in 2006 to an
estimated 3.9 percent in 2011.
Even more relevant, the industry ROE has been measurably lower than the average cost of capital since 2008. Running a business returning less than its
cost of capital is rarely a sustainable model, as evidenced by the number of property & casualty impairments occurring since 2007.
Looking closer, private passenger auto (PPA) has been the consistent premium giant, responsible for over one-third of the industry’s premium. PPA has
been running basically at “break even” with a combined ratio hovering around 100 for several years. With the exception of inland marine and medical
malpractice—both of which have combined ratios notably below 100—the rest of the product lines are experiencing the same or worse in terms of
underwriting results.
For those familiar with the industry, this is not surprising as underwriting has accumulated losses of nearly $500 billion since 1975. Industry profitability
has been consistently coming from investment income, which unfortunately is at an unexpected all-time low shaped by ten-year U.S. Treasury Notes
running below four percent since 2008 and around two percent now.
Looking further at industry profitability quickly shows that claims, including loss adjusting expense (LAE), represent roughly 80 percent of an insurance
company’s costs. Expense reductions are unlikely to provide enough savings, likely will impact service, and represent an often irreplaceable loss of
expertise.
Rate increases are occurring and will continue, but given intense competition, are likely to be managed rigorously. That leaves claims, a tremendous offset
to revenue where the potential for improvement is rapidly gaining attention. Consider the following, many fueled by the difficult economic conditions faced
by consumers as well:
• The National Insurance Crime Bureau estimates insurance fraud has escalated by 19 percent since 2009.
• The Coalition Against Insurance Fraud estimates insurance fraud costs Americans $30 billion a year.
• Insurance industry studies indicate 10 percent or more of property & casualty insurance claims are fraudulent.
• One out of every three bodily injury claims from car accidents involves fraud.
• U.S. insurance fraud runs an estimated 10 to 20 percent of premiums; in other markets like Brazil 25 to 30 percent.
• One in 10 Americans says they would commit insurance fraud if they knew they could get away with it.
• Fraud is costing the average household an estimated additional $300 per year
BY STEVEN M. CALLAHAN
June 4, 2012 • Reprints

2. • Missed subrogation opportunities could represent up to $15 billion annually just in the U.S.
Cost of Business
Historically, fraud has been difficult to identify and catch amidst the hundreds of thousands of submitted claims, particularly with the pressures for shorter
cycle times, higher customer satisfaction, and lower staff expenses. As a result, it is often considered a cost of doing business to be pursued responsibly.
Given the industry’s current financial situation, the best solution to the ROE problem would seem to rest in claims, combining aggressive pursuit of fraud
with close operational management. That said, the focus cannot be reducing the number of legitimate claims paid. Claims payments represent the delivery
on the promise, an insurer’s most important opportunity to achieve customer satisfaction and competitive differentiation.
Fast, easy and efficient should be the mantra of the forward-thinking claims operation. This is especially true in today’s world of easily accessible choices
and the amplified messaging provided by social media.
Technological advances including predictive analytics and text mining have contributed to an insurer’s ability to more efficiently and effectively process
claims. More specifically, whether dealing with dishonest body shops, unscrupulous providers, ethically challenged consumers, construction scams driven
by the many catastrophic events or well-coordinated, cross-country schemes, the use of predictive analytics to detect and escalate potential fraud is
providing significant improvement.
The results include fewer false positives as well. As a contrast, consider how traditional rules-based systems would tag claims that met set criteria for
review. Once a “hole” in the rules was found by payment of a fraudulent claim, that “hole” would get aggressively exploited until a new rule was put in place.
Predictive analytics operate much more intelligently. Instead of static rules, claims are evaluated based on all available history for all involved parties and
service providers along with the stored outcomes, and then enhanced by correlating millions of similar claims. As the number of claims decisions, involved
parties, service providers and characteristics increase, predictive analytics learn and adjust future outcomes.
Another advantage to this automatic adaptation is the ability to retrospectively revalidate claims, potentially identifying previously undetected cases of
fraud based on links to the new case. Fraud schemes involving “cash for crash” and intentional collisions are more quickly surfaced via this type of link
analysis. Some of the obvious immediate benefits include:
• A more efficient and wider “net” that quickly identifies more fraudulent claims with fewer false positives.
• Faster escalation of questionable claims, reducing often unrecoverable interim payments.
• Dynamic adjustment to new fraud schemes with the ability to retrospectively filter approved claims.
• Focused and more efficient use of SIU expertise, measured by higher savings per salary dollar.
• Lower unrecoverable payments by prioritizing investigations based on probability and financial impact.
• Improved customer satisfaction resulting from significantly faster payment of “clean” claims.
Real Benefits
According to estimates from FICO, the functionality represented by these new tools could find up to 50 percent more fraud than the traditional rules-based
tools. Based on the cost of fraud mentioned earlier, and the fact that claims and LAE represent 80 percent of revenue, these kinds of results are clearly a
material improvement.
As an added benefit, insurers that have successfully integrated predictive analytics into their claims operation are also seeing an increase in the consistency
of how claims are handled—always a challenge in large and/or geographically dispersed centers—and even more notable is an increase in accuracy of loss
reserves. Given both the impact of error and the resulting attention given to loss reserving, improved accuracy as a side benefit to claims analytics further
strengthens the importance to rapid adoption.
Claims subrogation and recovery represents another area where predictive analytics has been successfully deployed with meaningful positive results. Often,
these opportunities are buried under mounds of data and claims payment activities. Equally as likely is when the indicators of opportunity are in
handwritten notes in the file.
These challenges are compounded by the complexity and fluid nature of the rules and regulations that guide these efforts. The result is usually either not
enough resources to pursue, a missed identification of the opportunity, or a long, drawn-out manual process that ends up taking up more time and effort
than the recovery brings in.
By combining predictive analytics with text mining tools, these issues can be greatly reduced and the value of recovery efforts significantly increased. By
outlining, and updating as necessary, the rules surrounding subrogation and recovery as well as the triggering conditions, cases fitting recovery parameters
are identified early and efficiently for marking or discounting.
Late stage opportunities are found via text mining that searches the specific note fields for trigger words, bringing cases forward that fit the definitions. A
feedback loop ensures that both mechanisms—predictive analytics and data mining—constantly update their search criteria based on successful discoveries.

3. Once discovery is profiled, predictive analytics can provide insights into which path and vendors will likely lead to the quickest recovery of the most funds.
Much of the tedious manual labor expended in looking for the opportunities, browsing the notes, determining the applicable options based on regulatory
parameters, and selecting the right vendor(s) is eliminated, leading to increased recoveries and reduced total claims costs.
Be Efficient
Improving operational efficiency represents an opportunity for leveraging predictive analytics. The size and cost of the claims department is typically a
significant investment in extremely important intellectual capital. Optimizing the use of this expertise will not only increase employee satisfaction but will
improve productivity, consistency, and customer satisfaction.
The application of analytics tools for resource scheduling and work allocation has been proven to have a positive measurable impact on cycle time, staff-to-
claims ratios, and average staff cost. The predictive element comes into play in extrapolating not only the arrival and complexity pattern for FNOL, but for
subsequent documentation, field and customer follow-up contacts, and escalation patterns to name the main key variables.
Staff skill sets, schedules, productivity “sweet spots”, and secondary expertise are then integrated with the projected work patterns to calculate the optimal
allocation of individuals and skill sets by time-slice. Practical rules have to be incorporated to prevent every-other-hour schedules, while at the same time
allowing for split-shifts and shared part-time as allowed by the company.
While there are a number of call center products that model and monitor call arrival patterns across skill-sets, the intention here is to take that concept and
extend it across a broader range of mixed complexity, alternative source work activities. The more complex the model, the more efficient the eventual use of
resources; and as an indirect benefit, the more satisfied those same resources, and their customers, are as a result of the closer pairing of time and need to
availability and skill.
Additional noteworthy advances are being made in claims analytics tools beyond fraud detection, claims recovery, and schedule optimization. Text mining
is one in particular that seems to be gaining the fastest traction. Simply put, text mining involves searching unstructured data like handwritten notes,
translating findings into data that can be meaningfully manipulated, and then either operating against the results to generate predictive outcomes or
incorporating them into the applicable database.
In March, Text Analytics World was held with an impressive range of topics and real-life applications for both text analytics and the expanding field of
sentiment analysis. Specific to insurance claims was Accident Fund’s presentation on the use of text analytics to extract information about claimant co-
morbidities—particularly obesity and diabetes—from workers’ compensation adjuster notes.
Here, the data did not exist in digital form, but it had been proven to correlate to claim outcomes and total costs. Given that the average combined ratio for
the workers compensation industry is estimated to reach 120 this year—shored up by a continuing increase in medical claims costs—the ability to leverage
text analytics in determining and better managing these claims could prove an extremely valuable application.
Top Challenges
According to a 2011 study by Bloomberg, the number one challenge preventing companies from more fully adopting analytics was executive concern over
data quality, acquisition, and integration. Within the insurance industry, it is likely that this challenge is often expressed as the “legacy systems” barrier,
that hauntingly familiar roadblock to innovation and transformation. While a valid concern for insurers who have yet to either start or complete their
transition to new systems, it is worth noting that analytics tools can operate against data warehouses. And data warehouses can be fed by a multitude of old,
new, external, and in transition systems.
Unlike many transformations that are utterly dependent upon a successful migration to a new system or modular replacement of a legacy system, analytics
can be implemented across diverse platforms of varying age. A well planned roadmap can be designed to start realizing benefits in as little as six months, as
experienced at Chartis in the case study presented at IBM’s Financial Services Summit in September of last year. Despite having many business segments
and a variety of systems, a repository was used to capture the relevant data, analytics tools were engaged for a very specific purpose to operate on that data,
and the project’s first of many stages was implemented on time with positive impact from day one. At that same conference similar examples were provided
by AIG, Oppenheimer, and Chubb.
The three key criteria in common across the all-senior-executive panel, as well as several other panels and presentations since, are as follows:
Have a focused roadmap that clearly outlines what resources will be involved, specifically where analytics will be used and when, what tools will be used,
and how will success be measured. The roadmap may change with time and knowledge, but requires from day one executive sponsorship and business buy-
in on who, what, where, when and how.
Use clean data that is comprehensive, accurate, and current; this does not mean that 100 percent of the data has to meet these criteria, as some have used
as little as 70 percent of available data. The bottom line is that the data that is used should be representative and should meet these criteria. This is where
starting small and building big has the greatest advantage as representative subsets of data can be used with proper thought.
Staff with talented and engaged people who completely understand the business problem and are proficient with analytics. Note that one person does not
have to have both qualifications; it can be a team where some are experts on the business problem and others experts in analytics. Both skill sets have to be
represented.
According to an IDC study, the average ROI for a predictive analytics project was 145 percent and a nonpredictive analytics project was 89 percent.
According to research by the Aberdeen Group, insurers integrating predictive analytics into their key decision processes achieved a one percent
improvement in profit margin and a six percent improvement in year-on-year customer retention versus a two percent drop in profit margins and a one
percent decrease in year-on-year customer retention for insurers who did not adopt predictive analytics.

4. Like
No matter how it is measured or what study is reviewed, the bottom line is that the use of predictive analytics, particularly in the claims area for p&c
companies, can provide near-term positive impact despite the challenging economic times. Looking at the field of those already engaged in the process, it is
clear that tomorrow’s market share leaders are being defined by today’s actions.
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