Reshaping the Customer Experience
How insurers are turning data and analytics into actionable information.
By John Lorimer
First things first. The term analytics is thrown around a good bit, but it’s worth clarifying what it actually means. The generally understood meaning centers on analysis of data, and that is true. However, the benefit of what such analysis is designed to provide is where the understanding of analytics often comes up short.
For the insurance claims industry, analytics turns data into actionable information, and that information shifts how business is conducted. Analytics allows for claims decision-making (the new way) versus claims processing (the old way). And with that come some tremendous benefits for carriers, including improved efficiencies, reduced cycle times, reduced claims costs, and increased customer satisfaction.
It may not sound intuitive, but the use of data and analytics has become a major driver of improving the customer experience. For the most part, the days of traditional one-on-one relationships between an agent and customer are long gone. With the push toward speed and efficiency on the part of both customers and carriers, such relationships are fleeting.
Today, claims representatives are often the primary touch point for most customers. It should stand to reason that the claims management process ultimately shapes the overall customer experience. For customers, if a claim is bogged down by laborious questioning, encumbered by multiple interactions to accomplish tasks that could be completed within a single interaction, or is not handled as quickly as they perceive it should be, their experience isn’t one that will enhance loyalty or lead to referral business. Easing a process that, for most, stems from an unfortunate incident, can create more value in the mind of the consumer than any other touch point with an insurance carrier.
Laying the Foundation
Think of a pipeline between the carrier and the data and analytics provider. Through this pipe, the carrier can send inquiry requests and data to the provider, and in turn, the provider can feed relevant information and analytic “signals” back, creating a two-way flow of data from system to system.
Using an auto accident as an example, the way analytics, used on top of data, impacts decisions can be explained in a number of ways. Say, for example, a customer has an accident and calls a representative for the first notice of loss. The representative will be able to pull up a good amount of data on the insured based on the customer’s policy number, e.g., vehicle make and model, address, contact information, history with the carrier, and the like.
However, what about the other party or parties involved in the accident? What the system will not show without a comprehensive data solution is all the information related to the incident beyond the customer’s knowledge that may be gained with a couple of simple details. For example, the license plate number of the other vehicle involved will allow a data-fill application to load information on that vehicle, the registered owner, and the other insurance carrier involved. Analytics based on both the carrier’s data and this supplemental information provides the representative with guidance on how best to address the claim. This information allows the claims handler to move more quickly toward proper claim resolution, resulting in an optimal initial customer touch point.
The analytics and supplemental data allow a claims representative to make better decisions and reduce the number of claims that are reassigned. One such decision made early in the life of a claim is whether or not a case can be fast-tracked. For those customers who can and should be fast-tracked, the process allows for greater confidence in the decision, significantly reducing the claim’s lifespan and providing a better outcome for the customer. Equally important, analytics may prevent a case from being fast-tracked when it requires more investigation. Analytics can uncover concerns over vehicle theft, past incidents of fraud with other carriers, collusion, or a subrogation need.
Transforming the Claims Process
It is clear from this example that data the carrier collects at first notice of loss starts the process, but much additional information is required to advance the claim. The process of obtaining supplemental data is generally one of the most time-consuming parts of a claims handler’s job. The claims handler is responsible for gathering information about the party’s insurance carrier and coverage, detailed information about the accident, information about medical and other providers, information about prior claims history and other potentially influencing factors, previous evidence of fraudulent activity, and much more.
What if this information could be delivered to the claims handler proactively at the most opportune point in the process? The right data and analytics provider can supply much of this information directly through that same system-to-system connection, significantly reducing the effort on the part of the claims handler and shortening the claim cycle time. All of the relevant information about a party, vehicle, and provider can be rapidly assembled and made available to the claims handler at the optimal time.
Here the ability to identify individuals and entities accurately becomes critically important. For the data to be meaningful, it must be timely, and it must be connected with the right individual. A mistaken connection at this point can introduce information that will confuse and delay the case. Highly accurate identification of individuals based on a profile built from extensive public records and other information makes this possible.
However, proactively providing data is only part of the equation because simply providing large amounts of information becomes a “data dump,” which can actually increase the claims handler’s workload. Here is where the combination of data and analytics provides the key advantage. With an integrated analytic approach, as each claim progresses, the data about the claim and relevant external data, such as accident report information, public records information, prior claim history, and policy information, are continuously re-evaluated through various analytic tools. Predictive modeling has become a key analytic tool in this process, but a robust solution combines predictive modeling with other analytic methods including text mining, rules, matching, social network analytics, and more. Because claims are complex, a one-size-fits-all approach from an analytic tools perspective doesn’t work.
Analytics turns raw data into actionable information and returns decision signals: “this claim may have the propensity to become unexpectedly severe,” “this claim appears to have subrogation possibility but isn’t being handled for subro,” and “this claim appears to be potentially fraudulent, refer it to the SIU.”
One role of analytics is to eliminate unnecessary and irrelevant data—it helps to separate out the nuggets of information from the mass of raw data. But more than that, it can facilitate decisions directly, sometimes allowing for certain types of processing without direct claims handler involvement, freeing them up to focus on more complex and unusual cases.
To take this to another level, when we look across multiple carriers, all submitting data in this way through pooling of data and cooperative data agreements, it becomes possible to extend the benefit of comprehensive analytics across carriers to increase the reach and efficiency of the process. For example, this is particularly evident in fraud mitigation. It is well known that individuals engaged in organized fraud don’t attack only one insurer. In fact, they deliberately look to spread their activities in order to fly beneath the radar. But analytics applied across data from multiple carriers can reveal these nefarious patterns. With the right partner, it even becomes possible to extend this reach beyond P&C insurance and include health care, financial services, government, and more.
This sharing of data across carriers also allows for benchmarking through analysis of the claims payout process, understanding of customer satisfaction levels, and state-by-state comparisons of performance. Ultimately, this helps companies model and refine operations, build out marketing programs that are more effective, staff to more appropriate levels, and provide a better customer experience.
Data and analytics have, to some degree, been used for a while now, so the question is how are things changing? Analytics has traditionally been applied as a spot tool. Recalling the pipeline metaphor used earlier, such pipes connect various functions to a data analytics provider. For example, there may be a pipe to analyze the potential for fraud, another for subrogation, another to analyze severity, and yet another to look at liability. These all represent single, smaller pipes for each distinct functional area, with purchase decisions often at the functional level.
The evolution of analytics will replace all those smaller, single-solution pipes with one enterprise-wide pipe. Analytics applied across the entire claims process will dramatically reshape how carriers are able to operate.
The process starts at the first notice of loss, and data and claims are applied throughout the continuum of the claims process—triage assignment, coverage review, fact gathering, investigation, liability determination, settlement, and recovery, impacting every step in the process.
Applying this approach, the carrier will experience greater efficiency. This is accomplished because a comprehensive, integrated suite of analytics tools can be continually applied to the claims data, providing the carrier with actionable signals that allow it to utilize the correct internal and external resources, allocate the proper amount of focus, and issue appropriate and timely payments for all claims.
To make this happen, carriers will need to rethink how they procure and implement analytics solutions. The shift to an enterprise solution requires a holistic look at the entirety of the claims process and leadership to address purchase decisions and implementation across the entire business. It also requires close partnership with strong data and analytics suppliers.
This combination of robust external data sources, high-performance and high-accuracy linking, contributory data, and a suite of sophisticated analytic tools all delivered through a single data channel and applied through the entire claims life cycle results in the most efficient and effective way to impact claim outcomes.
The insurance industry is truly at a crossroads. Carriers that are going to be brand and industry leaders must differentiate themselves, and claims will lead that charge. While it’s a mind change and a process shift to look at claims holistically, it will be a game changer for the department and the organization, but perhaps more importantly, it will provide the optimal experience and outcome for your customers.