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Transforming Claims for the Digital Era

As insurers undertake digital transformation programs, many rightly turn to the claims function. Claims is a very good candidate for such initiatives because of its importance to the relationship between customers and their insurers. Claimants and insurers both want speedy and fair resolution, based on clear lanes of direct and personalized service. A data-driven, analytics-enabled claims process can satisfy the objectives of all parties.

Continuous improvement to customer experience in claims is critical to any strategy. After all, claims are a real “moment of truth” for insurers, with meaningful impacts on outcomes and customer loyalty. Insurers that craft the right strategies and deploy the right mix of digital technologies will be able to turn their claims operations into a source of competitive advantage, market differentiation and brand perception. While advanced technologies such as robotic process automation (RPA) and artificial intelligence (AI) are very much part of the long-term transformation story, there is much insurers can do that will generate immediate benefits.

What matters to claimants — and how to deliver

EY’s insurance consumer research confirms that speed, efficiency and transparency are among the most important characteristics of a quality claims experience. Better data and analysis can help streamline steps in the claims process, setting the foundation for an enhanced experience. Those analytics also set the foundation for the future where many claims will be resolved via “no-touch” processes.

See also: 4 Ways That Digital Fuels Growth  

Insurers seeking to automate their claims processes or to achieve straight-through processing for basic claims have multiple options, including:

  • Advanced telematics data (including video imagery) can be instantaneously captured during an automobile accident and downloaded from the cloud to automatically trigger a first notification of loss (FNOL) entry. Underwriters can “score” the data to determine the extent of loss relative to the automobile’s current value.
  • Drones and satellites can survey damage and collect information about property damage to initiate claims before a homeowner makes contact.
  • Via intuitive apps or other interfaces, insureds can submit photos of damage to their homes or vehicles to initiate the claims process, provided there is no sign of fraudulent behavior (which analytics programs can evaluate).
  • Property and casualty (P&C) insurers may use historical repair data to dramatically decrease estimating times for different types of vehicles and homes. They may also better manage repair costs and quality based on deeper analysis of these data sets.
  • AI may be used in combination with social media and other data to scan claims for the likelihood of fraudulent behavior.

Insurers also have good options when it comes to personalizing service, which include:

  • Voice analytics that can assess customer sentiment during phone calls, with appropriate classification and prioritization of resolution.
  • Behavioral analytics that can be applied to model likely customer needs and identify high-value policyholders or those likely to dispute a claim.
  • Analyses of customer records that can identify claimants facing renewal as well as good candidates for purchasing additional products.

A redesigned claims experience can pay immediate dividends (e.g., lower processing costs, improving claims resolutions or higher renewal rates). In all of them, insurers can engage at key points during the claims life cycle, with accurate and consistent information delivered on a timely and transparent basis. At the same time, claims teams can focus on high-value interactions, high-risk claims and other exceptions.

The path toward a better claims experience

No matter where insurers fall on the maturity curve today, there is much they can do to transform the claims process. The path to success begins with a series of well-thought-out steps designed to produce useful learning and incremental value. Huge investments in new technology or large teams of data scientists are not required for substantial improvements. Organizational and cultural factors are also part of the claims transformation equation.

Insurers should endeavor to integrate third-party data (such as medical claims, consumer credit and weather data) with existing records. They also have the opportunity to pilot the use of automated notifications via chatbots and to encourage customers to submit photos of damage. While taking these initial tactical steps, they can begin building the business case for, and perhaps even pilot, more advanced capabilities, such as “no-touch” claims handling for specific products, regions, claims types or payments.

Insurers in the intermediate phases of their digital transformation journey should consider expanding automated claims handling to more claims types and larger amounts, broaden their use of chatbots for communication and seek to integrate more external data sources. They can also deploy drones as “adjusters” and establish analytics Centers of Excellence in claims.

More mature organizations will look to leverage new data storage and management technologies as the basis for advanced analytics and real-time visualization. They may also strengthen antifraud efforts by implementing machine learning. The most forward-looking insurers may build out data science teams to probe large and diverse data sets stored in analytics ecosystems. Similarly, they may expand claims volumes handled via RPA-enabled straight-through processing and evaluate medical treatments or repair effectiveness against leading practices.

See also: Digital Transformation: How the CEO Thinks  

As claims organizations become more digital, the benefits of additional data and more effective analytics should extend beyond the customer experience. Machine learning and visualization techniques can help assess and predict claims risk with greater accuracy and certainty. They also provide a consistent claims handling approach relative to unbiased reserving, litigation, subrogation and other claims processes.

It is worth noting that technology enhancements alone will not produce a claims organization for the digital era. A cultural willingness to embrace change also matters. Many insurers must overcome risk-averse cultures to encourage experimentation and “fast failures” in the spirit of learning what works best for their culture and customers.

How do they do that? Test-and-learn approaches are a good start for insurers with limited digital capabilities. Pilot programs for automated claims processing and bot-driven notification systems are an ideal place for many organizations to start.

Customer experience is everywhere

In the digital era, where customers have been trained to expect real-time access to data and personalized service, the stakes for the claimant customer experience have been raised. Insurers must learn to deliver what customers want and expect — and deliver it efficiently, accurately and quickly. Digital transformation makes it possible, while offering insurers significant upside in terms of lower costs, increased customer loyalty and reduced risk of fraud.

How to Create Risk Transparency

There was a time not long ago when a bank originated a loan and kept that loan on the balance sheet until it was repaid. The amount banks could lend was limited to the deposits they had on hand and the banks’ own ability to borrow. Today, credit risk is traded regularly, with specialized data and analytical services giving investors confidence they understand the risks they are assuming. But there has been limited opportunity for investors to deploy capital against specific pools of insurance risks, because of a lack of that sort of transparency. With the vehicles that do exist, it has been difficult to structure the transfer of risk to meet investors’ respective objectives and risk tolerances.

However, insurance may be reaching a point in its evolution where the information gap will begin to narrow. Up until today, insurance risk had often been opaquely and highly subjectively valued. Today, actuaries set reserves based on highly summarized data, and underwriters set premiums based on claims experience that is extrapolated forward using historical loss development patterns and subjective future “trend” projections (or ad hoc substitute measures for risk), neither of which may represent future risk of loss. Outside of property catastrophe risk, where the data elements are generally available in some detail, granular risk data simply has not existed. However, rapid change could now be approaching. Vehicle telematics, wearable sensors, connected machines and other components of the Internet of Things (and Beings) are producing real-time data that allow us to look at risk in real time, rather than relying on current industry practices.

See Also: A Better Way to Assess Cyber Risks?

Credible, data-driven risk indices may create a variety of opportunities, including:

  • Capital Providers: Investment in specific index-based structured insurance pools that are aligned with respective objectives and relative risk tolerances could improve on the alternatives available today, where those who want to invest in insurance risk are often restricted to investing in insurance companies or risk pools that involve assuming underlying exposure to the operational, asset and credit risk, as well as the insurance risk of the originating insurer’s business.
  • Insurance Clients: Clients are likely to observe premium and associated underwriting decisions more transparently and could thus anticipate the cost/benefit implications of decisions taken to reduce risk.
  • Regulators: Regulators could gain greater confidence in the balance sheet valuation disclosed by insurance companies, which has the potential to decrease the regulator’s view of risk capital necessary to support risk.

One could argue that straightforward consumer and commercial loans are much simpler than the risks underwritten by insurers. However, when taking a critical look at the complexity of the financial projects currently being traded by investors, that notion is hard to support. In fact, many of the underlying risks facing lenders are very closely related to the risks facing insurers. Perhaps the biggest differentiating factor is the lack of standardization of contracts, which creates a degree of complexity.

From a contractual perspective, however, complex derivatives, other hard-to-value instruments and non-transparent assets can be at least as opaque and complex. Yet the core elements for assessing risk are available, and credible calculations exist within the valid range of assumptions.

The insurance industry could benefit from the increasing availability of relevant data. That data could be the byproduct of other applications, such as route data from fleet management software; vehicle data from predictive maintenance applications; traffic density data from road management applications; or environmental data from various sources. Or, it could be data that has been custom-generated for insurance applications, such as the data from telematics devices used by personal auto insurers to capture driving behavior. I see the biggest promise in using the data exhaust from other applications. I suspect clients would be averse (in many cases) to additional data capture specifically for insurance but would be open to sharing data already captured—as long as there are appropriate safeguards to ensure that it does not disadvantage them as clients.

The industry will need to invest in new analytical techniques to leverage these new data sources. In many other sectors of the economy, “big data” is having a real impact. This has required new tools and algorithms that might be unfamiliar to most analytical professionals within insurance. David Mordecai and Samantha Kappagoda, co-founders of the RiskEcon Lab at Courant Institute of Mathematical Sciences, which is among the world’s leading applied mathematics and computer science research institutions, explained the necessary evolution:

“The increasingly pervasive proliferation of remote sensing and distributed computing (e.g. wearable tech, automotive telematics), and the resulting deluge of ‘data exhaust’ should both necessitate and enable the emergence of digitized risk management programs. Ubiquitous peer-to-peer interactions between human ‘crowds’ and machine ‘swarms’ promise to dominate commercial and consumer activity, as already observed within omni-channel advertising exchanges and high-frequency algorithmic stock trading platforms. Financing and insurance functions involving risk-transfer, risk-sharing and risk-pooling will increasingly be facilitated by and executed seamlessly within code. Among others, Bayesian statistical and adaptive process control methods (e.g. neural networks, hidden Markov models)—originally employed within the telecommunication, electricity, chemical industry and aviation during the mid-20th century, and more recently adapted for voice, visual and text recognition, along with other supervised and unsupervised data mining and pattern recognition methods—will need to be widely adopted to identify, monitor, measure and value underlying risk factors.”

In my opinion, new data and new techniques are likely to create a degree of transparency in insurance risks that has never existed. That transparency could benefit capital providers (both insurance company investors and direct investors in insurance risk), clients and regulators. A new era is quickly approaching where information and analysis have the potential to remove the cloud engulfing insurance risk. There are likely to be substantial benefits for those forward-thinking companies that exploit the opportunity.

How to Use All the New Data

Most people who purchase an insurance policy are faced with the daunting task of filling out an extensive application. The insurance company – either directly or through an intermediary – asks a myriad of questions about the “risk” for which insurance is being sought. The data requested includes information about the entity seeking to purchase insurance, the nature of the risk, prior loss experience and the amount of coverage requested. Insurers may supplement that information with a limited amount of external data such as motor vehicle records and credit scores. The majority of information used to inform the valuation process, however, has been provided by the applicant. This approach is much like turning off your satellite and data-driven GPS navigation system to ask a local for directions.

According to the EMC Digital Universe with research and analysis by IDC in 2014, the digital universe is “doubling in size every two years, and by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes.” That explosion in the information ecosystem expands the data potentially available to insurers and the value they can provide to their clients. But it requires new analytical tools and approaches to unlock the value. The resulting benefits can be grouped generally into two categories:

  • Providing Risk Insights: Mining a wider variety of data sources yields valuable risk insights more quickly
  • Improving Customer Experience: Improving the origination policy service and claims processes through technology enhances client satisfaction

For each of these areas, I’ll highlight a vision for a better client value proposition, identify some of the foundational work that is used to deliver that value and flesh out some of the tools needed to realize this potential.

Risk Insights
Insurance professionals have expertise that gives them insight into the core drivers of risk. From there, they have the opportunity to identify existing data that will help them understand the evolving risk landscape or identify data that could be captured with today’s technology. One can see the potential value of coupling an insurer’s own data with that from various currently available sources:

  • Research findings from universities are almost universally available digitally, and these can provide deep insights into risk.
  • Publicly available data on marine vessel position can be used to provide valuable insights to shippers regarding potentially hazardous routes and ports, from both a hull and cargo perspective.
  • Satellite imagery can be used to assess everything from damage after a storm to proximity of other structures to the ground water levels, providing a wealth of insights into risk.

The list of potential sources is impressive, limited in some sense only by our imagination.

When using the broad digital landscape to understand risk — say, exposure to a potentially harmful chemical — we know that two important aspects to consider are scientific evidence and the legal landscape. Historically, insurers would have relied on expert judgment to assess these risks, but in a world where court proceedings and academic literature are both digitized, we can do better, using analytical approaches that move beyond those generally employed.

Praedicat is a company doing pioneering work in this field that is deriving deep insights by systematically and electronically evaluating evidence from various sources. According to the CEO Dr. Robert Reville, “Our success did not come solely from our ability to mine data bases and create meta data, which many companies today can do. While that work was complex, given the myriad of text-based data sources, others could have done that work. What we do that is unique is overlay an underlying model of the evolution of science, the legal process and the dynamics of litigation that we created from the domain expertise of our experts to provide context that allows us to create useful information from that data built to convert the metadata into quantitative risk metrics ready to guide decisions.”

The key point is that if the insurance industry wants to generate insights of value to clients, identifying or creating valuable data sources is necessary, but making sense of it all requires a mental model to provide relevance to the data. The work of Praedicat, and others like it, should not stop on the underwriter’s desktop. One underexploited value of the insurance industry is to provide insights into risk that gives clients the ability to fundamentally change their own destiny. Accordingly, advances in analytics enable a deeper value proposition for those insurers willing to take the leap.

Customer Experience
Requiring clients to provide copious amounts of application data in this information age is unnecessary and burdensome. I contrast the experience of many insurance purchasers with my own experience as a credit card customer. I, like thousands of other consumers, routinely receive “preapproved” offers in the mail from credit card companies soliciting my business. However appealing it may be to interpret this phenomenon as a benevolent gesture of trust, I know I have found myself on the receiving end of a lending process whereby banks efficiently employ available data ecosystems to gather insights that allow the assessment of risk without ever needing to ask me a single question before extending an offer. I contrast this with my experience as an insurance purchaser, where I fill out lengthy applications, providing information that could be gained from readily available government data, satellite imagery or a litany of other sources.

Imagine a time when much of the insurance buying process is inverted, beginning with an offer for coverage, rather than a lengthy application and quote request. In that future, an insurer provides both an assessment of the risks faced, mitigations that could be undertaken (and the savings associated), along with the price it would charge.

While no doubt more client-friendly, is such a structure possible? As Louis Bode, former senior enterprise architect and solution architect manager at Great American Insurance group and current CSO of a new startup in stealth-mode observes, “The insurance industry will be challenged to assimilate and digest the fire hose of big data needed to achieve ease of use and more powerful data analytics.”

According to Bode, “Two elements that will be most important for us as an industry will be to 1) ensure our data is good through a process of dynamic data scoring; and 2) utilize algorithmic risk determination to break down the large amounts of data into meaningful granular risk indexes.” Bode predicts “a future where insurers will be able to underwrite policies more easily, more quickly and with less human touch than ever imagined.”

The potential to use a broader array of data sources to improve customer experience extends well beyond the origination process. Imagine crowdsourcing in real time the analysis of images to an area affected by a natural disaster, getting real time insights into where to send adjusters before a claim is submitted. Tomnod is already crowdsourcing the kinds of analysis that would make this possible. Or imagine being able to settle an automobile claim by simply snapping a picture and getting an estimate in real time. Tractable is already enabling that enhanced level of customer experience.

The future for insurance clients is bright. Data and analytics will enable insurers to deliver more value to clients, not for additional fees, but as a fundamental part of the value they provide. Clients can, and should, demand more from their insurance experience. Current players will deliver or be replaced by those who can.

I’d like to finish with a brief, three-question poll to see how well readers think the industry is performing in its delivery of value through data and analytics to clients. Here is my google forms survey.

Reimagining Insurance in 2016

After more than 20 years in the insurance industry, working on three continents in various product lines and capacities, I have seen many changes occur alongside a notable constant: Insurance consumers want to pay less, and insurance company returns don’t satisfy shareholders.

Therein lies the rub. The conventional way to increase returns has been for insurers to increase premiums (based on what is presumed to be a fixed risk level), but that approach is contrary to the client’s desire. Yes, insurers also look to improve operational efficiency and claims handling, but those efforts are yielding diminishing returns.

Why not take a different tack and really focus our efforts on reducing the cost of risk? We’d then diminish the tension between insurers and their clients. Client premiums would drop, and insurers’ profitability would rise.

Like many, I believe that insurance is on the cusp of dramatic change. Insurers that thrive will put risk reduction at the forefront of their value proposition. That risk reduction will translate into lower premiums for diminished risk. Clients, and society at large, will be the ultimate winners.

The increasing availability and variety of data, more sophisticated tools to extract insights from that data and technology to cost-effectively support risk reduction will fuel this evolution. Insurers will need to rebalance their resource deployment away from the evaluation of risk for the purpose of assuming liability (underwriting) to the evaluation of risk for the purpose of reducing risk (risk consulting). Clients will come to expect insurers to provide advice on actions they can realistically employ AND the savings they will be guaranteed if they take those actions.

Whether change displaces current insurers or they evolve remains to be seen. Some insurance executives see a future of insurance that delivers a different value proposition to clients. We see a value proposition that primarily focuses on reducing the cost of risk. Insurers will increasingly supplement expertise with data, analysis and technology focused on reducing the cost of risk. They see a future where the industry unlocks the insights in insurers’ own data, integrates external sources as they become available and closes information gaps that exist. They see a future where clients are empowered with clear, objective risk measures that allow them to control their risk level … and their premiums.

In this future, insurers become tech companies where the insurance policy covers the limited remaining risks and in essence serves as a warranty of the risk services provided.

My discussions leave me optimistic that there are like-minded executives who see a different value proposition for insurers. But most I have spoken with draw the conclusion that neither their company nor any they know has the critical mass of support necessary to drive change.

To adapt and stay viable, insurance companies need to think about how evolutions in technology and data science can benefit clients and reshape business models. My goal is to encourage that debate.

I’ll be introducing a topic and perspective every other week that will focus generally on evolutions in the industry and the power of technology to transform the way risk is quantified, along with associated pitfalls. Each piece will conclude with a polling question and, depending on the volume of response, these results will be published.

Coming topics will include:

New Data and New Tools: When we think of data, most think of text and numbers that has been organized. By expanding our thinking, we can add satellite imagery, sensor-derived data, the Internet of Things (IoT), traffic cameras, customer service phone call recordings, pictures and many other potentially valuable sources. Imagine being able to analyze traffic light cameras to understand real-time risk at intersections. Imagine crowdsourcing the analysis of satellite and aircraft imagery to identify properties affected by natural disasters. Imagine being able to review a snapshot of a damaged automobile and adjust many claims without human intervention. Research, and in some case practical applications, exist in these and many other areas. We need to identify the information we need to know to understand risk and then either find the data that will help us or create our own. How do we ensure that the insurance industry is at the forefront of collecting, generating, integrating and analyzing all forms of data to drive deeper insights?

Data, Data Everywhere but Not a Drop for (Clients) to Drink: Every insurance company collects and generates a tremendous amount of data. Some of that data is structured; a much larger volume is memorialized in pdf files, pictures and customer service call recordings. While potentially useful for clients, the data is rarely made available at all and even more rarely in a format that provides insights. Insurers are investing in using that information to drive better claims outcomes, better risk segmentation and better internal processes. Clients expect to benefit from insurers’ resources but generally don’t get the insight they need to effect change. What would it mean if we insurers transformed our business model so that data-driven insights and risk mitigation strategies replace risk transfer as the core of value proposition?

Risk Mitigation Strategies and New Technologies: Imagine being able to identify the moment a risky behavior is occurring and having the ability to automatically intervene or alert the appropriate person. In some realms, that possibility already exists. Applications exist to alert drivers to their own risky behavior. Active technology exists to automatically apply the brakes to prevent collisions. Yet even where appropriate data exists, insurers are hesitant to make definitive recommendations based on specific technologies. Insurers are unique in that they price risk and ensure the realization of financial benefits from investments in risk reduction. Should we as an industry more actively become creators or advocates of risk technology? Can we have enough faith in our recommendations to integrate benefits immediately in prices? Does the traditional insurance policy become a form of warranty that our risk advisory services are effective?

Transparent Risk Indices: We are about to enter an information age where it is possible to quantify risk objectively in real-time. Creating risk indices, making them transparent and using them as the basis for establishing price would give clients confidence in the objectivity of the process and confidence that if they invest in changing those indices they will immediately get the benefit. The indices will also give non-insurance risk capital providers the opportunity to deploy capital against and trade risks that previously lacked the transparency. What can we learn from other financial services that have developed transparent risk indices that allowed capital to be deployed against those risks from a wider variety of sources?