Insurers are missing opportunities to improve claims processes and reduce costs through claims technology. While insurers face challenges like legacy systems and limited resources, claims technology can deliver significant gains in efficiency, service and loss cost reductions. Analytics in particular can help at various stages of claims handling from fraud detection to subrogation to improve outcomes. Insurers need to prioritize developing strategies to address talent shortages and leverage technology to maintain efficiencies as claims experts retire.

1. Property Casualty 360
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Insurers Missing the Boat with Claims Technology
Insurers can ill afford to put aside claims technology challenges as carriers realize improved combined ratios are easier
than organic growth in an uncertain economy.
Claims operations are getting headline coverage lately, driven in no small part by recent references to Accenture’s claims survey listing core systems
replacement, data and analytics, and workforce growth as the top three priorities and Celent’s claims fraud report highlighting reduction in fraud as one of
the key levers to near term profitability.
Executives appear to be in agreement that improving combined ratios will be more easily achieved than increasing underwriting profits, at least until the
economy returns to a more reasonable footing. At the same time, increased competitive intensity has executives asking their claims departments to
improve their delivery of service excellence and their operational efficiency. Apparently the previously achieved efficiencies have been quickly absorbed by
increased investments in technology, putting most claims departments back where they started. The race is on to pair carriers with the solutions needed to
deliver on these demands.
Despite the challenges of multiple legacy systems, data quality issues, overworked staff and limited financial resources, claims has the potential to deliver
significant gains in service, efficiency, and reductions in loss costs. Advances in technology and the availability of package solutions that can operate as
standalone enhancements to information management comprise the quickest path to delivering on the challenge.
Success depends on five factors: an approved plan, executive support, adequate resources, focused effort, and unwavering persistence to staying the course.
The thoughtful executive will ensure each of the aforementioned bases is covered before endeavoring to make any major changes. Taking the time to
establish internal strategic alignment across the executive team will greatly reduce the impact of inevitable bumps in the road.
Taking an outside-in approach, one only recently gaining in popularity as availability of mobile devices increase while costs decrease, involves putting
improved toolsets in field adjusters hands and enabling claimants to self-report FNOL. Improving field adjuster effectiveness represents one of the
quickest ways to move down the path of best practices. Whatever the brand used, empowering field adjusters means providing mobile technology in their
hands that includes:
•Optimization of routes for multiple claims adjustments;
•Access to key information in advance, including, where appropriate, street-level views, coverage history, claims data, financial information, loss
details, incident histories, red flag indicators, and contact information;
•Ability to review policies and add notes directly into core systems;
•Advanced device resident estimation toolsets that leverage rules based engines to assist in the process while documenting the results and eliminating
duplicate entry;
•Preferred provider directories to use to generate screen displays and automatic email of service referrals for claimants, combining customer
convenience with leveraging pre-negotiated arrangements; and,
•Uploading of photographs and other relevant images linked to the accident and adjustment process, auto feeding them into the claims document
processing system without the need for redundant scanning or indexing.
The Right Equipment
Properly equipped, the adjuster would receive claim notification via an email accompanied with an alert and including contact information. This direct-to-
field process enables a quick response to new claims. A call is made, an appointment set, maps accessed, and the GPS navigator engaged. Upon arrival,
accident scene and damage photos are taken along with key party information all fed directly back to the home office claims system.
One of several estimating tools already available is then accessed, automating the diagramming process using laser measurements along with damage or
square footage calculators, depreciation allowances, location mapping and the creation of statements of loss. Aerial imaging allows incorporation of
birdseye views into the claim data when roofs are deemed too steep or unstable to risk direct access.
As an aside, these same aerial imaging systems are available from centralized home offices or regional claims centers to confirm collected information or
determine any need for manual refinements. And depending upon the service provider being used, historical pre-damage views might also be available for
comparative purposes. While many of these services are available today on much bulkier laptops, the integration of a field adjuster solution suite onto a
mobile phone or pad moves the process to an even more convenient level.
BY STEVE CALLAHAN
May 15, 2013 • Reprints

2. Providing customers with the ability to self-report FNOL via their phone or other mobile device creates a competitive differentiation at a time when
customers are the most aware of how they are being treated. The creation of an app that collects pictures, key data items, GPS information and even aerial
views of the accident location and then stores and forwards them to a carrier is not an overwhelming task, as evidenced by the several carriers who have
already put those apps in their customer’s hands. Not only does this accelerate the FNOL process, but it improves the accuracy and timeliness of accident
information and reduces the field adjustment burden and home office records collection and indexing effort.
Add in the ability to request the nearest contracted tow service and line up a rental car, and the result delivers on both the service excellence and
operational efficiency goals set out at the beginning. It may come as a surprise to some that the cost and effort required to put even approximations of this
type of functionality in place—and something is better than nothing in today’s market—is less than might be imagined.
As an example, an award winning equine insurance app was developed that assists in determining coverage applicability and levels and submits FNOL
information when a covered race horse is injured. An added feature includes the ability to search out the nearest qualified vet along with contact
information and a map from current location to their office. This real life example only cost the carrier roughly $25,000 to develop, bringing memorable
differentiation to discerning market segment.
On the Inside
Moving to inside the claims operation, the opportunities created by effective use of analytics offer rapid payback across a broad range of options. Consider,
for example, ISO’s price analyzer as one relatively straightforward example of a closed loop use of analytics. In this case, loss costs are tracked at a much
more discrete level than territory, providing expanded insights in the intra-territory fluctuations in claims. For companies using the territory average loss
costs in pricing algorithms, the premium for a given coverage set may be $800. With the use of discrete loss analytics, that same territory can be
segmented into identifiably unique sub-territory risk profiles priced from $400 to $1200.
This approach results in the creation of an extremely competitive premium for a predominantly low risk segment offset by an appropriately higher
premium for the predominantly high risk segment. Not only does this drive away higher risk business to competitors using the older pricing models, but it
also provides a solid competitive advantage over companies using the average territory loss approach to pricing. Overall business quality improves as well
as selective territory penetration and market share.
Keep in mind that many analytics solutions are modularized and can stand independently, acting upon data that is extracted and supplied to it or is
resident in accessible data warehouses. These types of analytics engines can improve the claims process from beginning to end in a variety of ways. Starting
with when a claim is assigned, complex pattern analysis far beyond the capacity of individuals can detect and flag higher probability occurrences of
geographically dispersed organized fraud rings. These files can be tagged for special handling, removed from the standard workflow, and sent to specialists
or SIU for review and next steps.
The use of automation that learns as patterns adjust greatly reduces the risk of false positives, which translates into fewer diverted claims, more fast-
tracked claims, and better utilization of SIU expertise. As the rules are refined and additional data captured, the tagged files can be sorted and handled in
priority order based on expected impact. This shifts claims exception handling from a FIFO approach to a priority based one. This same process can be
used to concurrently identify claims with a high probability of being padded, once again using an in-depth analysis of prior claims patterns and claimant
attributes.
Another opportunity for analytics within the standard claims process is at each point at which a claim is updated with new information. Incorporating the
new information into the analysis process, analytics engines can use each update as a trigger to reanalyze and rescore for fraud, padding or litigation
propensity. Newly tagged files are then routed based on tag reason and impact to the appropriate specialty unit, allowing the remaining claims to flow
through the process faster and more smoothly.
Removing exceptions clears the way for taking positive action based on analytics feedback as well. Claims that share key attributes like claims type,
location, age, and complexity can be clustered together and routed by skillset and productivity to the optimal adjuster for handling. Process efficiencies
amplify when claim clusters based on similar loss characteristics, locations or other information is used to determine adjuster expertise.
Clusters of claims can then be routed in a batch to adjusters with the highest efficiency for these types of claims. By clustering and fast-tracking these
claims, inventory is reduced, customer satisfaction improved, reserves are kept more accurately, and claims operational efficiency is enhanced. The result is
reduced cycle time, enhanced quality, improved consistency and increased adjustor utilization rates. Typically, these triggered reevaluations also
incorporate a relook at durations, loss reserves and litigation propensity in order to identify any necessary adjustments. The continual revalidation also
increases the likelihood of capturing organized fraud schemes as patterns develop over time.
The Key Role
Analytics can play a key role at later stages in the process as well. For example, there are established models in use that identify and flag high probability
salvage and subrogation opportunities. Similarly, given that claims involving attorneys can typically cost over two-times similar, non-counsel driven claims,
analytic models help to separate out claims that are likely to result in litigation by looking not only at the specific claim characteristics and similar claims
performances, but also at the demographic characteristics of the claimants In this instance, the goal is threefold: early problem detection, removal from
the fast track flow, and assignment to specialized expertise for expedited resolution.
Along these same lines, as companies seek new ways to generate revenue, models have been developed that are triggered at point of settlement to
determine if the settlement was favorable and if so, whether there are opportunities to cross-sell another product based on their satisfaction levels; or, if
not, whether there is a need to initiate predefined retention efforts. In this example, the claims process is used as a trigger to enhance the revenue side of
the equation. It is interesting to note how few companies have integrated this particular trigger into their analytics engines; the opportunity is material,
especially if the claim identified a gap that would benefit from further exploration.

3. Not surprisingly, retention has been shown to be directly related to satisfaction at point of claim settlement. A JD Power study concluded that customer
satisfaction dropped from by 854 to 828 if claims turnaround went from eight to fourteen days, and dropped even further to 772 if the claim took over two
weeks to settle. Measurable value exists with these customers to the extent the lower scores indicate either higher lapses or fewer renewals.
Fraud detection leverages extensive use of analytics as well, combining complex scoring systems with external data whenever possible in order to accurately
categorize claims by probability and impact. For example, a fraud red flag system could create tiers to explore based on criteria that might include:
•Soft tissue mentions with pre-existing conditions being rated a level 1 (lowest);
•Above plus an attorney rep moves the rating to level 2;
•Continuing on a cumulative basis, adding an uncooperative client shifts the rating to level 3;
•Missing injury details warrants level 4;
•Not seeking treatment, level 5;
•Not going to the ER, level 6;
•X-ray is normal, nothing broken, level 7;
•No x-ray taken, level 8; and,
•Existing history of prior claims becomes a level 9.
The higher the level the more probable it is that fraud exists. By combining level with claim exposure, a priority list for investigation is developed that
ensures the optimal allocation of limited SIU resources. Building data-driven risk combinations early in the process helps accelerate the triggering of
mitigating actions as well as establish proper reserves.
It is also worth noting that the same logic that is used to detect probable fraud can be used to identify claims for potential fast tracking. If none of the
factors are present, and there are not any other complicating factors, then instead of falling into a regular queue, the claim could be fast tracked, improving
customer satisfaction and reducing average cycle time. The point being that when using analytics, it is wise to consider both sides of the probabilities to
ensure no opportunities are missed.
Re-investing Funds
With claims fraud increasing as a result of the economic shift, the exposures are significant if not properly reviewed. Unfortunately, for many companies,
SIU and supporting resources was one of the areas cut during the cost reduction waves, leaving fewer resources to address an increasing problem.
Analytics can help sort through the massive amounts of data to generate priority-ranked exception reports, but in the end the highest risk exposures still
require review by someone properly trained.
Properly supported with solid analytics, it is likely that most SIU units could recover a multiple of their total annual costs if given the chance; and, as noted,
the proper analytics support. Recent studies indicate the industry understands this potential. Trending along a 44 percent spending growth curve from
2011 to 2016, expenditures on analytics and claim scoring systems are estimated to be as high as $360 million by 2016. Given Aite Group estimates fraud
cost the property casualty industry as much as $64 billion in 2012, the potential benefits are clear. Each of the following categories represents significant
claims leakages that are difficult to capture without well designed predictive analytics and claim scoring systems.
What about subrogation? If an insurer operating at $100 million a year in recoveries could improve the recovery rate by only one percent, the result
represents a $1 million reduction in loss payments. Given recent ISO research indicating that about 15 percent of closed claims contained an overlooked
subrogation opportunity, incorporating the use of analytics to identify high probability subrogation recoveries represents another solid opportunity.
Along those same lines is the gap that exists with comparative liability. Again, research by ISO indicated that missed opportunities to apply comparative
liability ranged from 12 percent to 15 percent; in fact, in reviewing a sample of 1,700 closed claims, of which 1,600 had zero claimant liability, it was found
that 1,000 of the 1,600 could have had some degree of liability applied. Given time pressures and the volume of claims being processed by the average
claims department, identifying and capturing these kinds of offsets is extremely difficult without modeling analytics that provide very small flagged subsets
of claims to review.
One last exposure is worth mentioning, especially given economic performance these last few years, is the industry exposure to property claims on
foreclosed property. ISO estimated this at a possible $10 billion in 2010 using an average claim of $8,200 and their analytical models estimate of two
percent property claims on foreclosed properties. In 2012, 3.2 percent of homes with mortgages were in foreclosure, indicating the industry’s continued
exposure to this risk. Given that a study by the Insurance Research Council indicated 18 percent of respondents felt padding a claim to be an acceptable
practice, carriers should be sure to incorporate a review of property foreclosure data as part of setting up a new property claim. While not a definitive
problem, it is indicative of the need for additional review.
Over the next few years, the critical importance of claims functions to the bottom line is likely to drive structural changes and raise executive levels
commensurate with the potential impact. Chief Claims Officers should not be an uncommon occurrence, especially given the breadth of potential impact.
At the same time, there are dark clouds on the horizon as the well-known and inevitable talent drain fast approaches. Recovering markets combined with
the continued aging of Baby Boomers brings the time when the demand for talented adjustors will far exceed both the supply and interest.

4. Even the efficiencies created by both the discussed and other technologies along with continued process improvements are insufficient to offset the loss of
intellectual capital and expertise. Whether rapidly expanding self-reporting and self service capabilities, partnering with international resources, or
collaborating on the formation of organizations staffed by consolidation of claims expertise from across partnering companies, something will need to be
done to address the coming gap. Unfortunately, in many cases the leaders being asked to build the strategies are part of the pool of retiring talent, making
implementation of whatever is developed even that much harder.
Carriers that have not formally recognized this inevitability and put in place resources to investigate alternatives and develop recommendations are missing
the boat. It was recognized by executives participating in Accenture’s survey as the third most critical priority for a reason; make sure your company has it
as a priority.
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