5 Bases To Cover

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 first notice of loss (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.
• 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 the incorporation of birds-eye 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.

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 efforts.

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 a 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 into 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 the SIU for review and the 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 one that is priority based. 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 the 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 second look 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 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 identifies a gap that would benefit from further exploration.

Not surprisingly, retention has been shown to be directly related to satisfaction at point of claim settlement. A J.D. Power study concluded that customer satisfaction dropped from 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 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
• Existing history of prior claims becomes a level 9

The higher the level the more probable it is that fraud exists. By combining the 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 no 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 claims-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 leakage 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 small flagged subsets of claims to review.

One last exposure worth mentioning—especially given economic performance these last few years—is the industry exposure to property claims on foreclosed property. ISO estimated this number at $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 uncommon, especially given the breadth of potential impact. At the same time, there are dark clouds on the horizon as the inevitable talent drain fast approaches. Recovering markets combined with the aging of Baby Boomers brings the time when the demand for talented adjustors will far exceed both the supply and interest.

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.