Tag Archives: fnol

The Connected World: How It Changes Claims

Automation is transforming claims processing in myriad ways. Damage appraisals that are completed in only a few hours are becoming the norm―shaving days off cycle time and making the claims process easier than ever before. Insurance customers are getting comfortable with snapping a few photos of their damaged vehicle and sending them to their insurer via a simple mobile claim app. Drones are often dispatched to inspect storm damage on a home, allowing property adjusters to complete virtual damage inspections. Data delivered electronically early in the claims process is revolutionizing the claims workflow, simplifying claim reporting and providing a wealth of actionable data to expedite claim settlements.

What do customers think about the advent of claims automation? How can insurers leverage today’s technology and real time data to wow their customers? These are just a sample of the questions we wanted to answer with our Future of Claims panel of experts at the LexisNexis Customer Advisory Meeting on Sept. 11, 2018, in Scottsdale, AZ. This session, which I moderated, included experts Dave Pieffer (P&C practice lead with J. D. Power & Associates), Jimmy Spears (AVP auto experience with USAA) and Lily Wray (VP emerging technology operationalization with Liberty Mutual).

See also: 3 Techs to Personalize Claims Processing  

Data from the 2018 J. D. Power Claims Customer Service Survey, presented by Dave Pieffer, informed our discussion around the following four themes (with the customer perspective for the themes shown in quotes):

  • Show Empathy―“Listen to Me”
  • Streamline Customer Communications―“Simplify for Me”
  • Improve Service Speed―“Prioritize Me”
  • Optimize and Balance Self-Service Options―“Empower Me”

Show Empathy

The survey found that showing empathy (“Listen to Me”) ―expressed as “ensuring the customer feels more at ease”―scores low, with an industry average of 66%. Pieffer shared that the only empathy category scoring lower was “taking the loss report in 15 minutes or less”―with an average of 59%. The panel explained the importance of listening to customers as a first priority and improving FNOL scripts to be more natural and conversational versus impersonal (such as simply providing a list of questions). Jimmy Spears emphasized the importance of adopting a user-friendly self-service claims reporting process. He introduced the term “digital hug”―an immediate digital response to a customer’s electronic claim report or message. Spears shared that often customers who report electronically will immediately also call to ask, “Did you get my report?” Providing a digital hug gives customers the assurance that they have been heard and action is underway. The panel audience participated in the session by answering real time electronic polling questions from their phones, and in this case responded that simplifying the FNOL process with fewer questions was the most important way to increase customer empathy.

Streamline Customer Communications

On the topic of streamlining customer communications (“Simplify for Me”), Spears explained that “pro-active communication is the key to success.” Pieffer shared statistics showing that customers are most satisfied when the insurer updates them with claim status information. The survey results supported this information through scores indicating deteriorating satisfaction when customers find themselves having to call their insurer or repair facility. The panel agreed that getting the claim to the right person quickly and avoiding multiple handoffs was critical to improving customer communications. This was confirmed by survey data that showed consumer ratings drop by 133 points when customers are asked to repeat information during the claims process. The audience’s real-time polling indicated that typically at least three claims employees touch even the simplest claims.

Improve Service Speed

Customers expect their insurance company to make them a priority (“Prioritize Me”) when they have a claim. While we often think this means fast claims service, Pieffer explained that the survey results indicated that setting an accurate customer expectation at loss report was equally important to processing speed. In fact, meeting customer expectations on time-to-settle increases customer satisfaction scores even more than simply providing a fast claim experience. Spears explained how his company has completely redesigned the total loss claims experience by simplifying not only claims processing but also the car purchasing process via USAA Bank services and the USAA car buying service, which allows customers to be in their next car within a few days versus a few weeks (the industry average). Audience polling revealed that the optimal time to pay a simple claim should be within three days. Pieffer noted that the survey indicated today’s industry average is about six days.

Optimize and Balance Self-Service Options

Our final discussion topic (“Empower Me”) focused on the use of self-service technology. Pieffer shared data showing that Gen X and Gen Y customers (younger than age 50) were most comfortable with submitting damage photos via a mobile app and receiving electronic claims updates. While this was not a surprise, it was interesting to learn that satisfaction with digital FNOL was low for all age groups. The panel spoke about the need to simplify the FNOL process to minimize the clicks it takes to complete a digital FNOL. This was validated by audience polling, which overwhelmingly supported simplifying FNOL apps and minimizing clicks. I shared the value of bringing real-time data into FNOL and self-service applications to electronically verify first-party information to minimize additional inquiry. Furthermore, I noted that real-time FNOL data also allows third-party information to be collected immediately and accurately to simplify the FNOL process and make self-service reporting much easier for customers, which should greatly increase customer adoption.

See also: The Missing Piece for Customer Experience  

The panel discussion and audience poll answers confirm that delighting customers at time of claim is all about listening to, simplifying for, prioritizing and empowering them. As the P&C insurance industry continues to advance in claims automation, these four customer expectations should be front and center to ensure greater customer satisfaction and retention.

Why L&A Insurers Are Now the Smartest

In many quarters, the above title could be fighting words! Because I can’t even watch an Olympic boxing match, much less an all-out fight, let me explain.

For as long as I have been in the insurance industry, life and annuity insurers have been thought of as being significantly behind P&C insurers in terms of technology adoption and innovation. L&A insurers hung on to “build, not buy” strategies long after P&C insurers were advancing “buy” strategies. Technology providers with potentially cross-segment capabilities frequently did not even have a road map for selling to L&A insurers because, in their view, the front door was nailed shut! When STP (straight-through processing) started to become table stakes in personal lines operations, many life insurers seemed to feel that STP was simply a good motor oil. Every application for life insurance needed manual review.

Let me enthusiastically state that those days are gone. L&A has most certainly caught up – and sometimes surpassed P&C – on many fronts. One of the measures of being “smart” is learning from prior mistakes. L&A insurers have had opportunities to learn, and not necessarily from their own mistakes. They have learned from P&C insurer mistakes, as well.

See also: How to Insure the Gig Economy  

SMA recently issued two research documents based on L&A insurer surveys:

Many exciting insights are revealed through the survey data. Not the least of which is that, in 2018, 43% of L&A insurers indicate they are transforming. Eight short years ago, only 13% indicated this to be the case.

While this blog cannot hope to recap all the findings contained in the two SMA research papers, what did jump out quickly was the differences between how L&A has approached some things versus P&C – lessons learned:

  • Digital isn’t all about fancy front ends and apps. When it became apparent that insurers needed to respond to the reality of a digital world, many P&C insurers ran headlong into introducing apps – most usually first notice of loss (FNOL) apps. Click to pay with a credit card on websites was another common feature. More examples could be cited. But to cut to the chase, the problem was that these digital capabilities stopped right at corporate walls, dropping into legacy technology and manual processes. They ceased to be digital. P&C insurers learned the hard way – through disconnected customer experience – that core modernization was necessary! L&A insurers have seemingly learned that lesson, with 55% having policy admin projects in 2018 – the No. 1 project overall.
  • Love the hand that feeds you. The mantra across most all insurer segments, and strongly for P&C insurers, is customer experience. “We Love Our Customers” T-shirts are on every desk. This is absolutely critical, but for many P&C insurers this focus went to the exclusion of distributors. Agent and broker technology fell to the bottom of the top priority lists at many insurers. Given that agents and brokers have not disappeared, and, in fact, are critical as advisers for many consumers, this created a gap. L&A insurers do want to show some “love” to the distributors who play a critical role in customer acquisition and service. 55% of L&A insurers are executing distributor portal projects for both sales/submissions and service.
  • It’s not all about BI. SMA research shows a historical trend among P&C insurers to invest in BI technology. In fact, in relation to other components of data and analytics such as dashboards, data and text mining and predictive analytics, 71% of P&C insurers indicate they are advanced users of BI tools. This is certainly good, but for many years P&C insurers have invested in BI and have not invested to the same degree – or at all – in other capabilities, which stalls advanced execution in this area. L&A insurers are investing in BI, as well, but, in 2018, 22% are investing in behavioral analytics, big data and AI. Getting into the game in these advanced areas is imperative, and L&A insurers understand that.

And there is one lesson that L&A insurers have learned from themselves:

  • Building it yourself is a long and painful road. Over time, L&A insurers have attempted to tweak internally developed technology to step up to new market requirements. Given the rapidly shifting technology landscape, most insurers are not positioned to keep up, both from an IT capacity perspective and in terms of general skills levels. When it comes to emerging technology, 43% of L&A insurers are partnering with others that have emerging technology solutions. Only 29% continue to leverage their own capabilities.

See also: 3 Ways to Keep Training Fresh  

Many exciting things are happening at L&A insurers in 2018. Both of the SMA research reports, which can be found here and here, provide insight into strategic initiatives and projects. Clearly, there are opportunity areas that are challenges, but there is little evidence that L&A insurers are content to support the status quo. Smart L&A insurers are looking over the fence to see what they can learn from P&C insurers. Over time, the opposite may be the trend!

Insurtech Is Ignoring 2/3 of Opportunity

Fifty-six cents of every premium dollar is indemnity (loss costs). A further 12 cents is needed to assess, value and pay those losses. Given that two-thirds of the insurance industry economics are tied up in losses, it would be logical that much of the innovation we are now witnessing should focus on driving down loss costs and loss adjustment expense — as opposed to the apparent insurtech focus on distribution (and, to a lesser extent, underwriting).

This is beginning to happen.

What do you have to believe for loss costs and adjustment expenses to be a prime area of innovation and disruption? You have to believe that the process (and, thus, the costs) to assess, value and pay losses is inefficient. You have to believe that you can eliminate the portion of loss costs associated with fraud (by some estimates, as much as 20%). You have to believe that there is a correct amount for a loss or injury that is lower than the outcomes achieved today, particularly once a legal process is started. You have to believe that economic improvements can happen even as customer experience improves. And you have to believe that loss costs and adjustment expenses can decline in a world in which sensor technology starts to dramatically reduce frequency of losses and manufacturers embed insurance and maintenance into their “smart” products.

See also: ‘Digital’ Needs a Personal Touch  

Having spent years as an operating executive in the industry, I happen to believe all of the above, and I am excited by the claims innovation that is just now becoming visible and pulling all of the potential levers.

We are seeing an impact on nearly all aspect of the claims resolution value chain. Take a low-complexity property loss. Technology such as webchat, video calls, online claims reporting and customer picture upload are all changing the customer experience. While the technologies aren’t having a huge impact on loss adjustment or loss costs, they are having profound impact on how claims are subsequently processed and handled.

One such example, as many have heard, is how Lemonade uses its claims bot for intake, triage and then claims handling for renters insurance. Lemonade’s average claim is a self-reported roughly $1,200 (low value), and only 27% are handled in the moment via a bot as opposed to being passed to a human for subsequent assessment. Still, Lemonade certainly provides a window to the future. Lemonade is clearly attacking the loss-adjustment expense for those claims where it believes an actual loss has occurred and for which it can quickly determine the replacement value.

More broadly, Lemonade is a window into how many are starting to use AI, machine learning and advanced analytics in claims in the First Notice of Loss (FNOL)/triage process — determining complexity, assessing fraud, determining potential for subrogation and guiding the customer to the most efficient and effective treatment.

While Lemonade is the example many talk about, AI companies such as infinilytics and Carpe Data are delivering solutions focused specifically on identifying valid claims that can be expedited and on identifying those claims that are more questionable and require a different type of treatment. These types of solutions are beginning to deliver improvement in both property and casualty. New data service providers — such as Understory, which provides single-location precision weather reports — can be used to identify a potential claim before even being notified, which can reduce loss costs through early intervention or provide reference data for potentially fraudulent claims.

Equally interesting is the amount of innovation and development appearing in the core loss-adjusting process. Historically, a property claim — regardless of complexity — would be assessed via a field adjuster who evaluates and estimates the loss. Deploying technical people in the field can be very effective, but it is obviously costly, and there is some variability in quality.

In a very short time, there are very interesting new models emerging that reimagine the way insurers handle claims.

Snapsheet is providing an outsourced solution that enables a claimant of its insurance company customers to use a service that is white-labeled for clients. The service enables the claimant to take pictures of physical damage, which is then “desk adjusted” to make a final determination of the value of the claim, followed by a rapid and efficient payment.

WeGoLook, majority-owned by claims services company Crawford & Co, is using a sophisticated crowd-sourced and mobile technology solution to rapidly respond to loss events with a “Looker” (agent) who can perform a guided process of field investigation and enable downstream desk adjusting process, as well.

Tractable provides artificial intelligence that takes images of damaged autos and estimates value (effectively a step toward automatic adjudicating). Tractable — like, Snapsheet and WeGoLook — has made great strides. Aegis, a European motor insurer, is rolling out Tractable following a successful pilot. In each of these instances, the process is much improved for customers — whether it be self-serving because they choose to do so (Snapsheet), rapidly responding to the event (WeGoLook) or dramatically reducing the cycle time (Tractable). All provide material improvements in customer experience.

See also: Waves of Change in Digital Expectations  

Obviously, each of these models is attacking the loss adjustment expense — whether through a more consistently controlled process of adjusting at a desk, using AI to better assess parts replacement vs. repair or improving subrogation, among other potential levers.

Today, all of these solutions are rather independent of each other and generally address a low-complexity property loss (mostly in the auto segment), but the possible combination of these and other solutions (and how they are used depending on type and complexity of claims) could begin to amplify the impact of technology innovation in claims.

Telematics: A Claims Adjuster’s New BFF

Nobody can have too many BFFs (that’s best friends forever in today’s texting-driven vernacular).  That statement goes double for claims adjusters who are frequently seen as “bad guys” because of all the difficult-to-understand complexities of the adjusting process. The reality is that claims adjusters do not get enough recognition for the many times they go the far distant extra mile to help a customer after an auto accident. Claims adjusters need all the tools they can possibly get to deliver customer service at the high levels they want to deliver. And telematics is here to the rescue!

Many insurers see telematics only as a new way to rate auto insurance coverages, perhaps even replacing traditional rating criteria as some InsurTech innovators are doing. Other insurers only see telematics as a new way to underwrite auto policies, replacing traditional and sometimes complicated criteria with usage-based facts. These are all real situations. But what most insurers do not yet see is that telematics can be a way to give claims adjusters a customer service tool that, incidentally, improves claims financial outcomes. And who doesn’t love a win-win!

See also: Telematics: Moving Out of the Dark Ages?  

A new claims adjuster, right after getting a company ID badge and signing up for company benefits with HR, learns that the sooner the company is advised of a claim, the better the odds are the company can assure a successful outcome and control costs. That’s Claims Adjusting 101. Many insurers have addressed this by directing the first notice of loss from the consumer through a company contact center or service provider. More recently, companies have developed FNOL apps for mobile devices so that claims reporting can kick off shortly after paperwork is exchanged at the site of the accident. But, what if the FNOL could be generated as the accident happens? As a matter of fact, state-of-the-art telematics can actually do this.

Leading telematics technology can generate the FNOL from the actual impact dynamics. Appropriately implemented, this means that an emergency medical response could be automatically initiated if the impact details warrant it. In the event of a serious crash, this could make a critical difference in treatment outcomes. Towing services could also be initiated, getting the vehicle off the roadway sooner. Body shops and storage facilities could also be looped in as appropriate. Being the technology-enabled “first on the scene,” and providing much-needed assistance at a stressful time puts any claims adjuster on the fast track to BFF status. And, returning to Claims Adjusting 101, it helps with the positive management of claims costs.

The benefits of telematics in auto claims adjusting don’t stop there. Telematics can provide factual details that sometimes elude those involved in the event. When asked what happened, those involved in the accident very frequently respond with “it all happened so fast.” Telematics facts can replace post-loss perceptions of the event, thus helping the adjuster move the claim along faster. The telematics-defined dynamics of an accident can also aid in injury assessment, again, moving the claim process along.

There’s more. Vehicle repair can be an arduous process, particularly if the damage renders a vehicle unusable. Not having a car is clearly a source of frustration for most individuals. Simply getting all the assessment details can hinge on visual inspections, reports, and sending photos. Telematics can provide impact details and dynamics that can speed this process along, leap-frogging traditional claims processes to reunite vehicle and driver sooner. Another BFF moment!

In my role, I have spoken to a great number of claims executives. I have yet to meet any who did not see themselves and their organization as a key driver, if not the number one driver, of customer satisfaction. There are a good number of tools that claims organizations possess to deliver excellent customer service. And you can never have too many customer service capabilities (just as you can never have too many BFFs). Insurers should assess their existing or newly planned telematics initiatives and expand the opportunities for value and customer service beyond rating and underwriting to claims operations. Many technologies benefit one product line, or one discipline, or one process. It is, indeed, a top priority technology initiative that can span the organization at many levels, improving customer service and bottom-line results simultaneously. Telematics should be on the short list.

See also: Lessons From New Telematics Firm  

For additional thoughts on how telematics can be a successful component of an anti-fraud strategy, please read our blog Fraud is Not a Cost of Doing Business – And Emerging Tech is Here to Prove It!

Claims Litigation: a Better Outcome?

Insurance companies have historically struggled with the challenges posed by claims litigation and the threat of attorney involvement in multiple lines of business. According to the Insurance Information Institute, 39 cents of every dollar spent in loss costs in commercial multi-peril went toward defense costs or containment. For medical professional liability, the number increases to 43 cents, and for product liability it is as high as 77 cents. For workers’ compensation (WC), where the employee gives up the right to sue the employer for injuries that happen in the workplace, that number amounts to 13 cents.

In 2014, the California Workers’ Compensation Institute performed an analysis of attorney involvement in California WC claims. Over the six-year period studied, attorneys were involved in 12% of all claims (including medical-only cases), 38% of lost-time claims and 80% of permanent disability claims. Although the report discussed multiple efforts by lawmakers to reform California’s WC laws to help reduce costs, the report noted: “Despite those efforts, the litigation rate has nearly doubled for all workers’ compensation claims, and more than tripled for claims involving lost time.”

With such large dollars at risk, it’s no wonder that companies are investing in claims system technology and the use of advanced analytics to help reduce the impact of litigation spend on their bottom line. This article will share how advanced analytics and data mining can be used early in the life cycle of a claim to help identify litigation-prone claims and triage them appropriately.

Setting the Stage

Cases with heavy litigation expenditures typically involve various parties connected in a complex way with differing and sometimes opposing incentives. The ultimate costs of litigation are driven by numerous, factors including the duration of the settlement discussions and trial, if applicable, cost of medical experts, discovery, depositions, attorney fees, responsiveness of the plaintiff attorney, impact of high/low agreements, the appeals process and more.

Therefore, insurance litigation comes with a number of challenges that have historically made it difficult to predict litigation outcomes (e.g. dismiss, defend, settle, alternative dispute resolution, probability of winning, etc.). Traditional approaches have tended to focus on historical reporting and backward-looking data analyses to understand litigation rates, costs, trends, etc. However, such “hindsight”-focused measures are reactive in nature. In many situations, it has been difficult to segment litigation outcomes, especially in the early days of a claim’s lifecycle when an adjuster can make a real difference in the trajectory of a claim. For that reason, a number of innovative insurers have begun shifting to more predictive and forward-looking solutions, including predictive analytics.

See also: Power of ‘Claims Advocacy’  

The Inspiration for Litigation Analytics

Insurance companies have largely been using data analytics to attack claim severity in lines such as WC, medical professional liability, general liability and auto liability bodily injury. By matching claim complexity with the appropriate resource skillset as early as first notice of loss (FNOL), a great deal of efficiencies have been introduced to help reduce claim durations and costs. Claim predictive models have helped insurers better segment and triage high severity workers’ compensation and bodily injury claims, driving up to 10-point reduction in claims spend.

Models focuse on claim severity can naturally be extended to other business areas including medical management, special investigative unit (SIU) referrals and litigation management. We have seen such claim cost models be used by extension in these other areas as more severe claims also tend to be the most complex. For example, the most expensive 10% of bodily injury claims as predicted by these severity models can turn out to be as much as six times more likely to go to litigation and be more expensive to litigate. In WC, the most expensive 10% of claims can turn out to be as much as three times more likely to go to litigation and be even more expensive to litigate. Clearly, there is plenty of segmentation power to be gained – even more so if the models are specifically developed to predict litigation.

Data Used

Data is the first building block of any analytics journey. The ability of actuaries and data scientists to effectively identify litigation-prone claims can be attributed to the power of advanced analytics, the growth of big data and inexpensive computing power and storage. The data used in developing litigation models is similar to that of claim-severity models. They include internal and external third party data, structured and unstructured data, direct pull fields and synthetically created variables. The large number and diversity of the data sources used, sometimes numbering in excess of a thousand potential candidate variables, provide unique information for segmentation and analysis, thus helping to answer the question: which combination of complex patterns seem to make a claim more prone to litigation?

Some of the data factors typically used in litigation models are quite intuitive and include claimant age and gender, accident jurisdiction, claim history, etc. Unstructured data such as the description of the injury and accident narrative are often valuable sources of information that may help to uncover indicators and behavioral clues that bear a strong correlation to future litigation likelihood. Text mining can be used to delve into such unstructured free form data and help identify co-morbidities that significantly drive up claim severity. Additionally, third party data commenting on the individual’s lifestyle and habits add a layer of information about the claimant that further helps to segment the litigation propensity of the claim.

Analytics Techniques Used

A number of modeling techniques can be used to predict the likelihood for a claim to move to litigation. There are a number of techniques that generally perform well if used in a robust end-to-end modeling process that actively involves the end users from day 1. From multivariate predictive modeling and machine learning techniques to neural networks, various methodologies are available to identify the most predictive variables. However, and as we noted in the article titled “The Challenges of Implementing Advanced Analytics,” it is important to balance building a high precision statistical model with being able to interpret and consume its results. Our experience has shown that it is more valuable to leverage less complex models that are easily interpretable to the end-users than going after highly precise and complex models that are hard to consume and understand.

Models are typically trained on historical data with a defined target variable (i.e. what the model is trying to predict). Example target variables could be a binary 0-1 field (indicating if a claim has indeed moved to litigation “1” or not “0”), litigation dollars explaining how expensive are the claims that are already in litigation, a proxy for each, or a combination of both. Models are also validated on a holdout sample of claims to assess the robustness of the model.

Not surprisingly, models could be built and developed leveraging data available at FNOL or day 1, helping insurers take expedited business actions and make important decisions early in the lifecycle of the claim. As additional data becomes available through time, these models benefit from added information to make their prediction in the weeks and months that follow.

See also: 2 Steps to Transform Claims, Legal Group  

Claims Systems Are Differentiators

With the newest claims systems being implemented, insurance companies are achieving better claim outcomes and spending less on loss adjustment expense. The days of claims systems being only record keeping solutions are passé. The newest technology helps claimants directly verify the status of their claim regardless of the time of day or person’s location, through self-service portals and intuitive websites. But, these capabilities are not just for “external” system users alone. “Internal” system users can now leverage advanced analytics and spend less time on administrative tasks (e.g., manually populating spreadsheets), shifting their focus to working with insureds and improving their claims experience.

Litigation Models in Action

A number of models can be built to identify which claims could be more complex and involve litigation. As an example, an insurance company could build a model that answers the following questions: Of the claims that go to litigation, which ones are likely to be most expensive? If the model returns a high score, it means that the claim has a high likelihood of costing the insurance company a lot of money in litigation expenses. Therefore, it would suggest that the most experienced internal resources and attorneys should be focused on this claim.

Data used and target variables

For the case study at hand, a population of more than 10,000 bodily injury claims spanning multiple accident years was studied. For each claimant, many characteristics and factors about the claim, claimant, accident, injury, suit details (if the claim is litigated) were collected and recorded in a database. External third party data such as the vehicle identification number (VIN) and geo-demographic and behavioral data at the household and census block level were also added to capture more information.

The target variable (i.e. what the model is trying to predict) was calculated as all dollars spent on litigation, including attorney fees and expenses. A predictive model was then built employing a standard train, test, validation methodology.

Model results and output

The resulting models exhibited strong segmentation across the holdout sample. For example, the litigation costs for the highest-scoring 10% of claims were almost double the average population, while the lowest-scoring 10% of claims had litigation costs that were less than half the cost of the average claim. This strong segmentation is even more impressive considering it was realized at day 1, not weeks or months into the life of the claim.

The model contained about 30 predictive variables, some of which were intuitive and readily available (e.g., claimant age and gender, accident location and type – whether parking lot or intersection, etc.). The model also included information sourced from third party vendors (e.g., census employment statistics) and proxies for behavioral factors (e.g., the distance between the accident location and claimant’s residence, lag of time before reporting a claim, etc.). External geo-demographic data about the claimant were also beneficial (e.g., population density in the zip code of residence), in addition to data available from the National Highway Traffic Safety Administration (NHTSA) regarding fatal accidents statistics about the accident Zip code, etc.

Bringing Models to Life

Building a predictive model like the one described above is important but only beneficial if the model helps change behaviors, decisions and actions. The insights derived from these models help insurance companies take direct actions on their claim triage strategies, attorney selection and defense strategies. Business rules can be carefully crafted to help claim examiners in their decision-making process. When an adjuster understands that a high-scoring claim has a higher risk of moving to litigation and costing more, defense strategies can be adjusted accordingly. From assignment of external defense counsel, to settle or defend decisions based on case dynamics, insurance companies can alter their event management, resource allocation and escalation decisions earlier in the lifecycle of the claim.

See also: Rethinking the Claims Value Chain  

Carpe Diem With Analytics

The claim insurance landscape is becoming more complex, competitive, fast-moving and disrupted. There is little doubt that the adoption of big data, data science and analytics is important to becoming more agile in this environment, helping insurance companies make better decisions within days of receiving a claim. With the underwriting cycle indicating another period of softening rates, and interest rates hovering at record low levels, tapping savings in litigation spend might just be what the doctor ordered for insurance companies brave enough to seize the opportunity. As Larry Winget said in his book It’s Called Work for a Reason, “Knowledge is not power; the implementation of knowledge is power.” The knowledge and analytics exist today to improve litigation costs. We believe the time has come to implement that knowledge.

As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.

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