The debate on insurance innovation has been dominated recently by comments generated as a result of the State Farm TV ad where this insurance giant celebrates the superiority of its thousands of human insurance agents compared with the AI-based chatbots. Lemonade -- a smart U.S. insurtech startup -- has credited itself as the target of this ad, because its marketing story is that a chatbot is just as good – if not better – than any human insurance agent. It does appear that Lemonade's platform does need to learn a bit more about how insurance works, as AIs have regularly paid out more in claims than they’ve collected in premiums. Many comments on various social platforms have called the commercial, “the worst commercial I’ve seen,” creepy, freaky and hilarious. Many blame the insurance giant for releasing this “attack ad.” Even our friend Chunka Mui has written a well-articulated censure to this ad. However, we love this ad. We hope this discussion will encourage two calls to action for insurance companies:

1. Be proud of the way you do business
2. Master the art of communication.

Be Proud of Your Business Model Let us start with the first aspect. In the past years, technology arrogance and a sort of politically correct tech-speak have forced the storytelling of the largest insurers around the world to, on one side, shyly hide that real people generate the vast majority of their business and, on the other hand, celebrate any insurtech proof of concept as evidence of their innovation, even when it has an immaterial impact on profit. It seems insurance companies have felt embarrassed by their agents, brokers and other distribution partners. Most of their innovation efforts have been on solutions that in some way challenge their greatest asset, the human agent and broker. “The last agent is already born” is a slide title we have seen at industry conferences for the last 10 years, but as of today, all around the globe, the sale of P&C insurance continues to be dominated by agents and brokers (excluding a few exceptions like the retail auto business in the U.K.). See also: Digital Survival Tools for Agents   For life insurance, digital distribution accounts for less than 1% of global sales. It is great news to finally see a large insurer that is very proud of its agents. We love this communication because it is not hypocritical and gives a clear message both to customers and to agents: This is the way (through agents) we do business, and this is the reason why we do it this way. We are not celebrating or encouraging “old school” thinking. We are firm believers in insurance innovation – and agree with Chunka that chatbot, machine learning and AI use cases are among the technologies that will have the greatest impact on the future of the insurance sector. However, we are also pragmatic. We want to provide a view about insurtech that is different from the superficial mainstream. We think it is a pity to let the innovation cheerleaders – people raising their pom-poms at any PR released news but are not able to distinguish a loss ratio from a combined ratio – guide the debate about the future of the insurance sector. The mantra of our activities in the insurance sector around the world is “all the players in the insurance arena will be insurtech,” meaning organizations where technology will prevail as the critical enabler for the achievement of their strategic goals. So, we believe insurtech is much more than digital distribution. Our view is that insurtech is a superpower for insurers, a terrific enabler for performing the job of insurance in a better way: to assess, to manage and to transfer risks. The world is full of opportunities for reinventing each step of the insurance value chain through technology and data usage. Moreover, an insurance company has a key opportunity to share these superpowers with its agents, brokers and distribution partners. Many insurers already understand that not involving their distribution system in corporate innovation is a wasted opportunity, so these carriers have introduced technologies that can enhance the capabilities of their human intermediaries. Instead, we have seen only a few players communicating effectively and consistently to support their agents and brokers. Because of this, carrier innovations are frequently perceived as threats by agents and brokers. Insurance companies don’t need to create this kind of barrier. Maintaining this conflict only pleases the innovation cheerleaders who not like and want to get rid of intermediaries. Master the Art of Communication Let’s move to the second call to action. The insurance sector has always experienced bad press and has never excelled at storytelling. The new generation of insurtech startups are demonstrating the power of a consistent and modern communication strategy. The startup that has started the discussion about this ad is the best example of this communication ability. From our perspective, Lemonade's two years of case history must be studied in marketing courses at any university. There is a lot for the current industry to learn. The company has pretended to be the good guys who will be the remedy for a broken business. This home insurance startup has positioned itself as champions of trust. Everyone remembers the company for the fixed percentage of premium it charges – the iconic slice of pizza – while all the rest is used to ensure they will always pay claims, and whatever is left goes to charities. In today’s age of post-truth, only a few people go deeper, study and try to understand fully. Therefore, that slice of pizza celebrated by insurtech cheerleaders has flown tweet to tweet, article to article, conference to conference. Moreover, consistent and well-orchestrated communication has fed this mechanism. https://www.slideshare.net/matteocarbone/iot-insurance   What does “all the rest is used to ensure they will always pay claims” mean? In the long and wordy FAQs, the startup mentions the necessity to cover “internal reinsurance,” reinsurance costs and other expenses. Therefore, at the end, the maximum amount available for the charity giveback is 40% of premiums. The terrific 40% giveback happens only in the theoretical scenario where there are zero claims within the peer group. In a scenario with claims at 40% of the premiums (40% loss ratio) or above, the giveback is zero. This means there is a giveback only if the loss ratio is lower than 40%. See also: Important Perspective for Insurance Agents   Insurance is a contract where someone promises to indemnify another against loss or damage from an uncertain event as long as a premium is paid to obtain the coverage. On average, the U.S. home insurance business line had a loss ratio of 74% in 2017 (an exceptionally high year; 46% has been the lowest loss ratio in the past five years). This means that 74 cents have been used to indemnify the policyholders for each dollar collected as premium. In the age of post-truth, the Insurtech startup we talked about pretends to be the good guy that will fix a broken business model because it guarantees to pay – as claims or giveback – at least 40 cents for each dollar collected as premium within each peer group. It seems clear to us that the insurance incumbents have more arguments for claiming they are the good guys, but they have only to develop consistent and modern communication storytelling. Following are some suggestions on next steps that insurance companies can take:

• Be proud of and support your agents, brokers and distribution partners
• Encourage them to be part of your innovation initiatives
• Develop a frictionless process to help the people who distribute your products better engage with policyholders
• Learn how to tell a better story – about your company and your agents and brokers and distribution partners.

What ideas do you have for helping the industry to help agents and broker better protect their clients?

The past several months have seen increased activity and focus on the promising technology of blockchain and its potential in the insurance industry. Blockchain has also reemerged as an important issue in the European Union (EU) following the go-live date of the General Data Protection Regulation (GDPR) on May 25 of this year. As a side note to U.S. policymakers, including the California legislature, the GDPR was adopted two years before its effective date. There was a reason for that.

There will be a considerable amount of scrambling in Sacramento this year as efforts are made to clarify the scope and limit the unintended consequences of the hastily enacted California Consumer Protection Act 0f 2018 (CCPA). Virtually everyone in the insurance environment – including startup and established insurtechs – need to keep a very close eye on what emerges during this effort in 2019.

Regardless, it is important for all those dealing with technology to understand how the E.U. is dealing with issues such as blockchain. Businesses in California should be paying particularly close attention to how the E.U. is attempting to reconcile GDPR and emerging technologies while the CCPA is moving inexorably to its effective date of Jan. 1, 2020. Multinational companies are already dealing with GDPR compliance given its long extraterritorial reach. Inevitably, how the E.U. is dealing with privacy will serve as at least a partial template for how privacy issues will be dealt with in the U.S. E.U. commissioners are currently attempting to sort out the interaction between GDPR and blockchain technology. It is not a nice fit.

To foster a dialogue on this issue, the E.U. Blockchain Observatory and Forum was created as a European Parliament pilot project. Per its website, the observatory’s mission is to monitor blockchain initiatives in Europe, produce a comprehensive source of blockchain knowledge, create an attractive and transparent forum for sharing information and opinion and make recommendations on the role the E.U. could play in blockchain.

On Oct. 16 of this year, the E.U. Blockchain Observatory and Forum published a thematic report, “Blockchain and the GDPR.” As noted in the report regarding blockchain and GDPR compliance: “The issue of compliance of blockchain with GDPR is an important one. By specifying how personal data is to be protected, the GDPR will play a fundamental role in shaping digital markets in the Union. Considering its strong support of this nascent technology, the European Union clearly believes that blockchain technology has an equally important role in these markets, too, offering new paradigms for the ways we transact and interact with each other.” (Report, p.8)

See also: Blockchain’s Future in Insurance

What is not clear at this point in time is how blockchain can flourish while remaining compliant with GDPR. There are those who think the fundamental structure of blockchain is irreconcilable with GDPR. That opinion is not prevailing at this time.

As noted repeatedly in the report, GDPR compliance is not about the technology, it is about how the technology is used. There are clearly issues, even with private consortium blockchains, that need to be fully understood. The issue isn’t just where the data are housed, the issues also include who controls the data and, as the report repeatedly emphasizes, how that data are used.

The E.U. is ahead of the U.S. in efforts to balance the rights of natural persons regarding their own personal information and the improvements that can come from technological innovation. While various sectors of the economy, including insurance, seem to be gushing about the possibilities of blockchain, there is a singular silence about how this environment will comply with the host of state and federal requirements placed on all the participants in this distributed ledger technology.

This isn’t just about privacy in general and the CCPA in particular, although the CCPA could disrupt blockchain even in the commercial context if there is no further clarification during the 2019 California legislative session. The observatory’s report, however, serves as a reminder that the GDPR deals with personally identifiable information belonging to natural persons and not information that is shared with other business forms provided to businesses.

That is an important distinction but not entirely dispositive. In the world of commercial insurance, there are sole proprietors who must have not only liability coverage but also workers’ compensation insurance. These are “natural persons” who under GDPR and currently under the CCPA could ask their personal data to be removed from a database. This is not consistent with the blockchain’s promise of immutable records. (See: Civil Code Sec. 1798.105)

Earlier this year, industry giants Marsh and IBM, working with Acord, teamed up to develop a commercial blockchain for proof of insurance. Acord is the Association for Cooperative Operations Research and Development, an industry-supported organization that, among many other functions, makes many of the forms used in the property and casualty insurance industry for the transaction of insurance (applications, certificates, etc.). The pilot participant for this is ISN, a global contractor and supplier information management business.

Per Marsh’s announcement earlier this year, “A distributed ledger technology, blockchain is ideally suited to large networks of partners. It establishes a shared, immutable record of all the transactions that take place within a network and then enables permissioned parties access to trusted data in real-time.” IBM and Marsh also recently announced that they are working on making the proof of coverage blockchain accessible to Marsh clients through Salesforce. Recently, The Institutes, best known for its professional designation programs in the insurance industry, has launched its RiskBlock Alliance. Per its Sept. 23, 2018 announcement, “…a blockchain consortium representing 31 risk management and insurance companies, has launched Canopy, the industry’s first end-to-end reusable blockchain framework, using the Corda blockchain platform.”

One of the use cases currently being developed for Canopy is proof of insurance. In remarks on the National Association of Insurance Commissioners (NAIC) Innovation and Technology (EX) Task Force Oct. 15, 2018, conference call, Christopher McDaniel, president of RiskBlock Alliance, said, in response to an inquiry from Oregon Division of Financial Regulation Deputy Administrator TK Keen: “…if regulators have their own node on the blockchain, they could push a button and create a report, as long as the appropriate agreements were in place to share the information.” [NAIC Innovation and Technology (EX) Task Force conference call Oct. 15, 2018, draft minutes dated Oct. 26, 2018]

In a July 12, 2018, blog titled “Ultimate Guide to Blockchain in Insurance” from management consulting firm Accenture, it was noted that blockchain would facilitate “using shared loss histories to obtain data-driven insights on prospective customers for more sophisticated pricing.” I suspect that state insurance regulators would have a keen interest in how that would be accomplished.

Workers’ compensation rating organizations such as the National Council on Compensation Insurance, Inc. (NCCI) or the Workers’ Compensation Insurance Rating Bureau of California (WCIRB), operating under license from state insurance regulators and serving as a critical part of the active regulation of insurance required under the McCarran-Ferguson Act, would most likely have a few questions as well. In other words, while there has been much discussion about the promise of blockchain, that discussion needs to be fully integrated into the discussion of how all that data are going to be secured, shared, and stored within the context of existing and anticipated regulatory compliance requirements.

This goes beyond insurance regulation and, as is the case with the EU, directly implicates the emerging and complex privacy environment as evidenced by the CCPA. Take, for example, the issue of proof of coverage and the issuance of certificates of coverage within the workers’ compensation environment. These are two separate issues that require separate solutions. States maintain coverage verification portals for any person to verify workers’ compensation coverage. These are managed by rating organizations pursuant to statutory mandate and generally by self-insurance regulatory authorities. In some instances, such as with California’s Contractors State Licensing Board (CSLB), there are separate coverage disclosure requirements that are also accessible by the general public. This is not a testament to the accuracy of these systems, but rather only to their accessibility.

For blockchain to be effective in the workers’ compensation environment, therefore, it needs to have some degree of integration with public databases. That isn’t as easy as it may seem. For example, Labor Code Sec. 3715 states, “The nonexistence of a record of the employer’s insurance with the Workers’ Compensation Insurance Rating Bureau shall constitute in itself sufficient evidence for a prima facie case that the employer failed to secure the payment of compensation.”

Does this mean that rating organizations should have a node on the proof of coverage blockchain, as should the Division of Labor Standards Enforcement (DLSE) and the Department of Insurance (CDI)? If that is the case, then what does that mean for purposes of public records laws and whether the blocks in the blockchain are public records? In other words, if the blockchain is to serve a public purpose then it must take into account access issues that may not be present when the ledger is entirely for private transactions.

See also: How Insurance and Blockchain Fit

A certificate of insurance is issued, arguably, by either an agent or broker or an insurance company. For most transactions, this is currently done through a writable .pdf document or done manually. This process is an open invitation for fraud. The work Acord is doing with Marsh and The Institutes underscores a technology solution may help make the certification of insurance coverage – both as to existence and to limits (for liability lines of insurance) more reliable and transparent.

This is not an inconsequential matter, especially in California and considering the particular issue of whether some staffing companies are very much part of the problem. The latter issue regarding staffing firms is a critical one for California. Given the Golden State’s broad regulation of employment relationships, it is at best vexatiously ironic that when it comes to staffing agencies, with some very limited exceptions, there is virtually no regulatory framework to verify the legitimacy of staffing firms and the way they do business.

This is a problem – and a problem that needs to be resolved before applying a technology solution to the issue of bogus certificates of insurance. And that finally leads us back to what the observatory noted in its thematic report: “… start with the big picture: how is user value created, how is data used and do you really need blockchain?”

No single liability is quite as important to insurers as a best estimate of unpaid claims. It drives earnings reports, shapes financial statements and influences a host of other management decisions. But aberrations in data and model risk often cast a shadow over the reliability of reserve ranges from which this point is selected. Traditional development pattern benchmarks have provided some support in estimating these fundamental liabilities, but, even here, the process has long been a one-dimensional exercise, at least until now.

In determining a central or “best” estimate for property and casualty (P&C) reserves, the goal has never been to zero in on the exact final outcome for an insurer’s ultimate losses but to arrive at an estimate that is as likely to be high as it is to be low. Rather than trying to pinpoint one elusive number, the unpaid claim analysis process has focused on understanding or illustrating the variability around the estimate by identifying a range of reasonable estimates using different methods and assumptions.

By producing other reasonable estimates, actuaries moved somewhat closer to the goal of understanding the full breadth of the possible outcomes, but this approach still lacks specificity and provides little more certainty around an unpaid claim estimate. Commonly used “static” loss development pattern benchmarks that use industry data have been helpful in assessing some of the actuary’s assumptions but not all of them. The lack of specificity in these benchmarks has only marginally improved confidence in the selection of a range and central estimate.

The question is how do you overcome these challenges?

A recently developed dynamic benchmarking tool, which includes percentiles at all stages of development, allows for the calibration of a benchmark that better resembles individual portfolios. As such, this rigorously back-tested tool can provide actuaries an added level of confidence in the reasonableness of any entity’s reserve ranges. This next-generation benchmarking tool, known as claim variability guidelines (CVG), is derived from extensive testing that involved all long-tail Schedule P lines of business and more than 30,000 data triangle sets. Using such an extensive database both:

• • Provides for the development of a more extensive and reliable benchmark that is much more surgically focused than traditional industry averages.
  • Instills greater credibility in the loss development patterns derived for each line of business.

Four real-life scenarios The value of this new benchmarking tool stems from its ability to guide an actuary’s decision-making process by providing an interactive means of comparing the assumptions or estimates from a method or model based on real data and results against comparable alternative assumptions or estimates. To illustrate the potential impact of using such benchmarks, four representative data sets were used from randomly selected companies of four different sizes: A) small, B) regional, C) small national and D) large national. Minor changes were made to the data to protect the identities of each company. For all four companies, the commercial auto line was selected as a common denominator for contrasting the effect of the guidelines for different exposure sizes. To illustrate how useful the guidelines are in practice, a unique variety of lines of business was sampled for each carrier. The accident year earned premiums by line of business for each company are illustrated in Figure 1.

See also: Provocative View on Future of P&C Claims  

Figure 2, which shows the incremental and cumulative loss development patterns for commercial auto for Company A, provides an example of the type of CVG output that actuaries could use to guide their thought processes. In this case, the incremental loss development from the user’s model shows a pattern that initially might seem to be relatively smooth, but when compared with output from an industry average or the guidelines its irregularities become apparent. For this company, whose loss development pattern is somewhat volatile, using a benchmark pattern other than the average (shown in the “CVG Average Pattern” row) seems appropriate. But which one?

While the guidelines indicate that the 46th percentile (shown in the “Best Fit” row) is the best fit overall, the 46th percentile is less than ideal at different periods, where "best fits" vary from the 13th percentile in development periods 0 to 12 to the 99th percentile in development periods 72 to 108. In fact, there is considerable variability in the recommended fits—a situation that might be expected, considering the data limitations that a small company often encounters. But does the user’s calculated loss development pattern (shown as “User Input ATA Factors” in Figure 2) reflect the company’s uniqueness or contain random noise that could be smoothed by the benchmarks?

Using the cells in the CVG line, actuaries can select different assumptions and see the impact on their results. Is a dip or bulge in the User Input pattern due to noise, or does it reflect reality? Perhaps the company consistently pays claims faster than the industry average? How different is the mix of business compared with the industry average? Are the User Input Age-to-Age (ATA) factors from 72 to 120 months indicative of salvage and subrogation recoveries that should be included? At any point along the pattern, actuaries can adjust the pattern—using the User Input pattern, the selected guidelines pattern or an alternative—to reflect their understanding of a company’s data. This guided sensitivity testing provides actuaries a way of systematically exploring loss development patterns and deciding how much smoothing is necessary or which pattern is most appropriate.

As the exposures increase, the volatility of the calculated loss patterns decreases. For example, the commercial auto loss pattern for Company B in Figure 3 now meanders closer to the best-fit pattern, a situation that is reflected in the increased consistency among the best-fit percentiles (“Best Fit” row). In this case, the best fit is at the 71st percentile. As the patterns calculated from the user’s method and the guidelines move closer together, as is the case for this regional company, the justification for selecting a pattern other than the average increases. By comparing the development pattern graphs in Figures 2 and 5, the difference between the loss patterns calculated from the data and the guidelines merge ever closer for the small national company, as seen in Figure 4, and for the large national company the loss patterns nearly overlay the average, in Figure 5. This convergence of the loss development patterns on the average, interestingly enough, also illustrates how the static average-based benchmarks are most relevant for large national companies, how the various percentiles around the average become more valuable as the exposure size decreases and how both large and small companies benefit from the additional information available in a dynamic benchmark. Once an actuary decides on a loss pattern, a range of reasonable estimates can be established by, for example, using patterns 20 points on either side of the selected loss pattern, as illustrated in Figure 6 below (assuming our best fit is at the 46th percentile and the table sets' lower and upper benchmarks are at the 26th and 66th percentiles).

In each of these examples of this next-generation benchmarking process, the estimates for the “normal” weighted results (in Figures 7 and 8 below) were done mechanically using common methods and assumptions to prevent personal biases from masking the potential impact of this process. In practice, this step would be an interactive process, with the guidelines influencing the selection of assumptions and methods and vice versa. For the small company illustrated in Figure 7, the guidelines patterns from Figure 6 are used to estimate unpaid claims to be between 850 and 1,210. This result can be compared with the range and weighted average for the five methods used by the actuary, who now has a supplemental process for deciding on a best estimate. That process includes a new tool for deciding whether any of the estimates in the normal range are unreasonable, e.g., is the lowest estimate in the weighted range reasonable?

For even the most volatile lines, this process provides a guided method for inquiry and analysis that can lead to greater confidence in the end results. As each line of business is reviewed, they can also be added together to get a view of the overall range for the company, as illustrated in Figure 8.

See also: De-Siloing Data for P&C Insurers  

Determining a range of reasonable estimates and a best estimate are fundamental building blocks for assessing the financial health of a company, but they are only a small part of a claim variability process. From benchmarking unpaid claim distributions to setting risk-based capital requirements—topics of subsequent articles in this series—the next generation of benchmarks can help actuaries retool their methods of inquiry and build confidence in the numbers shared with management.

Given the ferment in the industry this past year, it's no surprise that the most-read of our blog posts concerned leading-edge innovators and some hard-won lessons from innovators. Lemonade, with its genius for stirring the pot, drew the most attention, especially after it called out State Farm for an ad that disparaged some high-tech offerings, including Lemonade's. The main blogs on Lemonade (focusing on the use of chatbots, with some intriguing comments appended) are here:

• State Farm and Lemonade Throw Down
• State Farm and Lemonade: the Great Debate
• State Farm and Lemonade: an Update

The most popular of the more instructive pieces, from some of us who have the scars to show for innovation efforts, included:

• Ten Signs You're Headed for Trouble in 2019

Among the nearly 600 articles we published at ITL this year, the most read on technology-driven innovation were these:

• Top 10 Claims Trends That Will Affect 2018
• The New Age of Insurance Aggregators
• 10 Insurtech Trends for 2018
• How Robotics Will Transform Claims
• Why 5G Will Rock the Insurance World

Because not all issues relate to innovation and not everything is driven by technology, there was, as always, huge readership for Mark Walls' and Kimberly George's scene-setter from last January on the issues that would drive workers' comp this year:

• 2018 Workers' Comp Issues to Watch

Check it out and see how they did—then get ready, because there will be another early in 2019.

At the risk of overloading you with articles, I am, as always, including links to my six favorite from the past week. And that'll do it for Six Things for this year. I'll continue a mostly regular publishing schedule at the ITL website, and we'll keep sending out alerts on social media, so keep an eye out for anything of interest. We'll then see you back here at Six Things in early January.

I wish you and yours a wonderful Christmas season and a healthy and happy New Year.

Paul Carroll
Editor-in-Chief

Have you ever heard yourself saying those words? “Why bother speaking up? It won’t do any good.” Or, “I’ve tried speaking up in the past, and no one cared.” Or, “Speaking up isn’t valued around here. I’ll just keep my head down and do my job.” I hear you. It’s easy to let past experiences jade us into losing our voice.  It’s tempting to let our assumptions take over and persuade us that we already know the response. After all, we’ve seen this movie before, and it doesn’t end well. So the troublesome issue continues, which validates our thinking: The other guy is a jerk who won’t listen. Trust erodes further. So we speak up even less, further convincing ourselves that it wouldn’t do any good. Overcoming FOSU (Fear of Speaking Up) I was facilitating a two-day training on conflict and collaboration with an interesting mix of scientists and administrators. About halfway through, Hope, an administrator who is also a woman of color, spoke up. “I hear you. And I believe all these techniques will work for someone like Peter (a white male scientist with credentials and position power whose large stature made him hard to ignore), but they would never work for me.” She’d ditched the diaper drama and apparently said exactly what everyone in the room had on their minds. We talked at length about her (and other participants') experiences–which were sad and compelling and real. Some of these stories had happened over a decade ago, with a peer or boss who was no longer around. And yet the fear of speaking up today was palpable. There was a whole lot of not speaking up going on, in a culture that desperately needed the truth. See also: How to Earn Consumers’ Trust   There’s no question in my mind that results suffered, projects took longer and the science was jeopardized due to this FOSU (fear of speaking up). Hope had spoken up to start a conversation. Game on. And then Peter raised his hand.

“I hear you. I really do. I’ve got two stories of my own to share. I also had been told several times by my boss to keep quiet, and not rock the boat. But I saw several errors that I knew would affect the timeline of our project once they were discovered. I took them to my boss who told me under no circumstance was I to say what was going on. When the project got in trouble several months later, the department head, Joe, got involved and asked why I didn’t say anything. I told him I had. He coached me and said that, at times like this, it’s so important to put the project ahead of self-protection. Joe reminded me of what was at stake.  And told me I can always come to him as needed. Which I do from time to time–only when absolutely necessary. I still respect the chain of command most of the time. My boss hates it when I go to Joe. But, I know have to do the right thing. Then one day we were in a meeting with Joe. He told us how frustrated he was that people don’t speak up. And then he said, ‘Peter’s the only one.’ When he asked why, everyone just looked at him without saying a word. Then my boss took me aside and said, ‘See, Joe wants you to stop speaking up! Now stop it!’ I was like, ‘What? Were we in the same meeting? And I insisted that we have a three- way conversation with Joe to check for understanding. Joe was unequivocal. ‘I want Peter and everyone on this team to speak up. That’s the only way we will know what’s ever going on.'”

Okay, I thought, we’re making real progress in this discussion. But, the truth is, it’s still easier for a guy like Peter to pull this off. And then he began his second story. “About a year ago, I had a peer come to me and tell me she thought I was a bully. I was shocked. I was hurt. I don’t see myself as a bully. I asked why. It came down to the fact that I was holding people accountable, and that was uncomfortable, and I knew I couldn’t change that. But I also knew that accountability is one thing, bullying is another. So I went to some of my other peers. And several of them said, ‘Oh yeah, you’re a bully sometimes.’ And I knew I needed to change. I dug deeper on how my behavior was being perceived. I started listening more. I entered rooms more gently. I watched my tone and manner. No work I’ve ever done on my leadership has made a bigger impact on my influence. I’m still holding people accountable, but I’m watching my style. It’s easier for all of us. Can you imagine if that woman had FOSU? I’d still be frustrating her and everyone else. She did all of us a favor by speaking up. I understand the culture we’re in, but I’ve got to tell you. People don’t speak up enough. We have to talk about this stuff for the culture to change. How can we do that better?'” See also: Voice of the Customer: They’re Not Happy   Your Turn: How Can We? And so I turn that question back to you. This is hard, no doubt. But how do we encourage more people to speak up and find their voices? I’d love to hear your stories of overcoming FOSU and the difference it made.

The insurance industry, started in the 17th century at Lloyd’s of London, has made significant progress since then, but it is only now that it is truly at the cusp of disruption. The traditional models of business development through intermediaries, pricing through actuarial models and underwriting on the basis of experience and data collected through physical inspection of risk and proposal forms are being challenged, as are the policy contract issuance and claims handling methods. The digital revolution has led to all types of information and data not only available in public domain, but easy to access and share at lightning speed. People are connected to each other on social media, devices are connected to the internet, homes and cars are becoming smarter through IoT and robotics and AI are increasingly prevalent in our everyday lives. Almost 150 years back, when the first self-propelled car came out and could drive at only 4mph, a person with a red flag would walk in front of the car to ensure road safety. Fast forward, and we are now looking at cars that can drive themselves. While insurance was slower to adapt than other industries, there has been a recent boom of insurtechs. This creates meaningful opportunities and one of the most exciting times in the insurance space. Today’s customer expects information to be available at her fingertips. Information is either a click away on search engines or through voice prompts on your smart phone or smart assistant. We also face changing risk scenarios and heightened unpredictability. Cyber is at the top of the list – and headlines. Increase in terrorism or “lone wolf events” – at places one would never imagine to be faced with such risks. Changing global weather patterns – the recent floods in Houston, hurricanes in Florida, wildfires and mudslides in California and the extreme winter freezes in the Northeast. And changing geopolitical and socioeconomic environments. See also: In Age of Disruption, What Is Insurance?   How do we cope with this rapidly changing risk profile and environment? Will traditional insurance be able to provide relevant protection and, more importantly, will it be delivered, serviced and provided in a manner to match customer expectations in today’s digital age? Insurers are responding to this challenge in two ways:

1. Digitizing the front-end user experience and user interface but still continuing to follow the traditional model of underwriting, pricing, risk selection and claims.
2. Disrupting the whole value chain from the front end of the business to the entire back end.

These responses are happening at both startups and established insurers. The winners will be companies that are willing to disrupt the entire value chain from front to back end. What does that mean? With the amount of internal and external data sources available today, we can get enough information on prospective customers and the risks they face so as to enable us to personalize their insurance and meet their specific needs. How will we do it? By harnessing big data, connected people and devices, smarter homes and cars and so on, and combining this with years of customer and claims data available with insurance companies. Insurers able to use this wealth of internal and external data in a manner to create distinct customer profiles will:

• Know the customer
• Anticipate the risks they are exposed to
• Find the gaps and where they may need insurance
• Give consumers a tailored solution to cover these gaps

As an example, and subject to compliance and other protected privacy considerations, let’s assume the IP address or phone number is linked as the unique identifier of a person. As soon as that person calls or logs in with her device, the insurer should be able to pull up all of her available information. Then, without the person answering all kinds of questions and forms, the insurer could automatically offer relevant insurance: “Welcome, Joan, for your two cars and home in Ohio, here are the coverages and premiums. Please select one.” As permissible, the same data can be used for underwriting, risk selection and pricing. Today, we as insurers have access to much more data and information on the risks and needs of our customers than ever before -- provided we are able to use the data effectively, a big challenge most incumbents face today. As with underwriting and risk selection, we can use data sources and technology such as sensors, AI and IoT at the time of claims. That includes the possible use of parametric insurance for natural catastrophes, and even smart contracts on a blockchain that self-execute the adjusting and settling of certain types of claims – the objective being to pay claims in a frictionless manner. Before we get there, we will need to work with regulators on data privacy laws that are fit for purpose for this digital age. See also: When Incumbents Downplay Disruption…   The other very important aspect of the new age insurer is to offer a full suite of “personal risk management services.” What that means is that, using the insights gathered over years of claims data, coupled with the availability of external data and AI, we should be able to:

• Predict – help insureds prevent a potential loss before it occurs. I believe people do not buy insurance to make a claim -- the real purpose is protection. What better protection is there than someone helping continuously monitor and help prevent bad things from happening?
• Assist – if even after efforts of prevention something unfortunate happens, insurers should be able to tap into their claims handling. Some companies have invested in risk management and loss mitigation units to actually assist the insureds through the time of need.
• Restore – and finally help the insureds to restore the loss or damage. This is the actual claim settlement, which is what most insurers do as the only activity at the time of claims.

In what I propose, the insurance (restoration or loss indemnification) is only one-third of what a new age insurer should be doing. And all of this needs to be seamless and digital with the use of available and developing technology. Time has come to disrupt the centuries-old insurance industry from what some would call a “necessary evil” to “a pleasant experience called insurance.”

The sharing economy is a system in which individuals may rent their possessions or time to other individuals, often through an app or website. Although the term first appeared in the mid-2000s, New Economy Advisor April Rinne says that it didn’t become a household word until recently. Still, the sharing economy has caused radical change in a very short time. While real-estate booking apps like Airbnb and ride-hailing apps like Uber dominate our current understanding of the sharing economy, the options for such sharing aren’t limited to houses and cars, Cointelegraph writer Connor Blenkinsop explains. “You can share someone’s garden if you live in a bustling city, strike up job shares, team up with other travelers to share a tour, swap books and even take someone’s dog out for the day.” Central to discussions of the sharing economy is a sense of disruption, research fellow Chris J. Martin says in a study published by Ecological Economics. This disruption may be framed in positive, negative or neutral terms, but the change itself and its challenges remain a constant topic. The explosion of the sharing economy has brought a new set of challenges for insurance companies as well. Here, we look at some of the biggest obstacles in the industry — and how property and casualty insurers can meet them. The Sharing Economy: Challenges for Coverage Traditional insurance models offer a poor fit for the sharing economy, Wells Media’s Andrew G. Simpson says. “Also new multi-party relationships among platforms, providers and consumers draw further questions around who is ultimately responsible for managing and mitigating risk.” For example, when an Uber or Lyft driver causes a car accident that injures a passenger, who covers the passenger’s medical costs? Who pays for the damage to the vehicle? What if the other car’s driver had insurance? What if the driver didn’t have insurance? See also: How Sharing Economy Is Reshaping Insurance While some sharing economy companies provide coverage for those renting out their homes, vehicles or possessions, such support is usually limited. So when the accident, damage or loss isn’t sufficiently covered by the company, Capgemini Financial Services' Ian Campos says that substantial risk may fall on the individual. For example, Airbnb offers coverage of up to $1 million to homeowners who share their properties, WeGoLook CEO Robin Smith points out. But this coverage applies only to the actual scheduled hours of the visitors’ stay — not to shoulder times in which visitors might arrive early or stay late. Also, $1 million may be insufficient coverage for certain homes or losses, such as total destruction by fire. Finally, the sharing economy is creating challenges to established P&C insurers themselves. Peer to peer (P2P) insurance is a sharing economy phenomenon that allows individuals to skip established P&C companies by pooling their own funds, finance expert at Money Under 30 Sarah Pritzker explains. This model excludes the value-added services an older, more established insurance company can provide — pushing new insurers to communicate that value more effectively to customers. Rising to the Occasion of a Sharing Economy It’s tempting to think of the sharing economy as simply a new model of ownership that requires only a slight change to existing insurance products. Some commentators, however, warn that this view misses the fundamental nature of the disruption the sharing economy represents. “Taken together, the growth of [sharing economy] services suggests that we are entering an era in which consumers will value access over ownership and experiences over assets,” Financial Times reporter Brooke Masters says. Many companies have already made the shift: A focus on intellectual property over tangible real estate or equipment has supported the growth of organizations like Apple and Amazon, for instance. This fundamental shift in ownership and access is problematic for current models of property and casualty insurance. Some types of insurance may not apply to businesses in the sharing economy, and others may be prohibited altogether. Jose Heftye and Robert Bauer, Marsh managing director and AIG managing director, respectively, explain this struggle in a 2018 report. “Where the distinction between personal and commercial use of assets in the sharing economy is blurred, regulators view personal lines of insurance very differently from commercial.” By mixing personal and commercial use, sharing economy companies can cause coverage gaps for participants. For instance, an Uber driver’s personal auto insurance may not cover times the driver uses a vehicle to make money through Uber, but the cost of a commercial policy may be out of reach for someone who just wants to make extra pocket money by driving for Uber on the weekends. Trust is also a significant issue in the sharing economy, both for customers who share their houses or cars and those who use them, Lyle Adriano writes in Insurance Business. For insurance companies, providing flexible products that explain the coverage gaps they address is a key factor in building trust among users. To foster that trust, participants in the sharing economy are putting pressure on insurance companies to provide adequate coverage or to explain why such coverage is unavailable. From adopting new brokers to creating more out-of-the-box services, researcher and writer Esther Val highlights that the sharing economy is prompting insurance providers to offer more flexible insurance solutions. They’re also being pushed to do so by insurtech startups, especially those that are already seeking to provide these services, reinsurance treaty analyst Alex La Palme explains. For instance, Slice Labs is a new insurance provider that provides on-demand policies specifically for home and ride sharing. This helps fill in the gaps for nuanced situations — like damaged furniture or utility issues — that aren’t usually covered, Slice Labs CEO Tim Attia suggests. To compete with these startups and meet customer demand, established insurance companies need to find new ways to cover risks they have not covered—or perhaps have not even seen in the past. One way is to partner with sharing economy platforms, Deloitte insurance consulting partner Nigel Walsh recommends. Another idea is to scrutinize the ways that the sharing economy has changed people’s behavior, understanding and approach to risk regarding insurance. Addressing Customer Needs in the Sharing Economy One of the biggest hurdles to participation in the sharing economy is risk. A study by Lloyd’s of London found that 58% of U.S. and U.K. consumers believe that the risks of sharing their possessions outweigh the benefits. Even for those who do participate, risk is a concern — particularly the risk of events and situations that can’t be anticipated. P&C insurers can help enable participation and growth by clarifying their role in coverage in the sharing economy, Lloyd’s Chief Commercial Officer Vincent Vandendael offers. “Based on our findings, instilling consumers with confidence by clearly defining and protecting against risk can help remove barriers to engagement in the sharing economy.” See also: How Sharing Economy Can Fuel Growth   Ryan Ward, an actuarial analyst at American Modern, agrees, noting that educating insureds about coverage gaps is an essential first step toward providing adequate coverage and mitigating risk. Risk is a concern for insurance companies, as well. Denny Jacob, staff reporter for PropertyCasualty360, writes that the sharing economy requires an entirely different approach to risk understanding and management. In particular, it necessitates that providers gain a deeper understanding of behavioral economics—especially how consumer preferences and attitudes change in the face of risk. Participation in the sharing economy can change a customer’s risk profile. For instance, a customer who drives for Uber is out on the road more often, attorney Jeremy Heinnickel says. Helso explains that some users may be less careful when interacting with shared vehicles, houses or personal property that isn’t their own, which in turn shifts the balance of risk. “It will never be possible to escape the fact that we just don’t treat other people’s belongings with the same care as our own,” Disruption Business writer Sarah Finch points out. “Sharing has therefore opened up the insurance business to a whole new kind of market.” One golden lining? In an era where fewer people are owning cars, houses or large quantities of consumer goods, the sharing economy creates an entire new class of people who need property and casualty coverage. “The gig economy has created the ability for more people to pick up ad hoc, part-time jobs,” Insurance Technologies Corp. CEO Laird Rixford says. “The amount of people that insurers, agents and brokers can now sell additional coverage to has exploded.”

The U.K. spends £97 billion treating diseases but just £8 billion preventing them. This imbalance is set to change according to government proposals. Under a social prevention model, health advice would be tailored to an individual based on several criteria, including personal data, lifestyle and demographics. There are parallels to insurance. The Association of British Insurers has reported that U.K. life insurers paid out £5 billion in income protection, critical illness and life assurance claims in 2017. These claims payments represent amounts paid when people's health has failed. While not every diagnosis or early death can be avoided, providers could offer more to help customers mitigate risk and stay healthy. This predicament is fueling interest in matching insurance programs to fitness data. There are multiple digital-based solutions available to help with engagement post-underwriting -- a white space for insurers to move into. Gen Re is active in researching technology of this type, which has led us to collaborations with a network of established companies and startups in an effort to create a prevention model. One such company is PAI Health. It offers a proprietary, science-based algorithm that uses cardiorespiratory fitness (CRF) to provide personalized guidance on how much exercise is needed for optimal health. See also: New Health Metrics in Life Insurance   In a 2018 article, Mandsager et al. confirmed CRF is a modifiable risk indicator of long-term mortality that is quite independent of age, sex and comorbidities. CRF is also associated with cardiovascular and other health benefits, including reductions in coronary artery disease, hypertension, diabetes, stroke and even cancer. CRF is inversely associated with long-term mortality with no observed upper limit of benefit. Extremely high aerobic fitness was associated with the greatest survival. That said, taking the right dose of physical exercise is very important. Too much exercise means a risk of adverse outcomes leading to the idea of a U-shaped dose-response association between exercise and cardiovascular events. PAI Health works by linking the individual to the dose. This personalized approach is critically important to ensure an insurance program is built around physical activity that appeals to the broadest range of people, and not just those who live in Lycra. In other words, an insurance program that provides benefit to everyman based on achievable yet therapeutic levels of everyday physical activity. It can be challenging to untangle large amounts of data and turn it into meaningful health insights. It’s important that there is evidence to validate the algorithms and "health scores" promoted in apps. All exercise is beneficial to health, but it's well-known that steps lack scientific reasoning. A daily target of 10,000 steps is daunting, even unrealistic, and lacks any calibration to the individual and to physical capability. PAI Health avoids these problems. The Physical Activity Intelligence (PAI) algorithm was invented by Ulrik Wisløff, head of the Cardiac Exercise Research Group and professor at the Norwegian University of Science and Technology. External evidence supports the conclusion that meeting a personal PAI target cuts cardiovascular risk, significantly reduces other lifestyle-related diseases in men and women of all ages and increases life expectancy. This has been shown to also be true in patients with established cardiovascular disease. Preventative medicine is about ensuring people take greater responsibility for their health and well-being. Most insurers could do more to engage policyholders in this way. Research suggests consumers increasingly value experiences above physical things. Can a new breed of protection products offer people more of an experience? A policy that actively involves them in protecting their own health could offer that. See also: More Opportunities for Reinsurers in Health   For any national health service to link care to personal data requires the highest standards of data privacy, and insurance is no different. While a prevention approach to healthcare is unlikely to be without controversy, the major barrier to a social prevention model is diverting funds away from treatment. For insurers, the problems may be less knotty. Elegant solutions like PAI Health are ready to be utilized.

This is the third part of a three-part series. You can finds part 1 here and part 2 here.

Successful industrialization of driverless cars will depend on getting over many significant hurdles. Failure only requires getting tripped up by a few of them. In part two of this series, I outlined seven key hurdles to industrial-size scaling of driverless cars. Overcoming hurdles to scaling is not enough, however.

In this concluding article, I explore the challenges to broader market acceptance. I outline eight additional hurdles related to trust, market viability and managing secondary effects. All must be overcome for driverless cars to truly revolutionize transportation. Trust. It is not enough for developers and manufacturers to believe their AVs are good enough for widespread use, they must convince others, too. To do so, they must overcome three huge hurdles: 8. Independent verification and validation. To date, developers have kept their development processes rather opaque. They’ve shared little detail about their requirements, specifications, design or testing. An independent, systematic process is needed to verify and validate developers’ claims of their AVs' efficacy. Many are likely to demand this, including policy makers, regulators, insurers, investors, the public at large and, of course, customers. The best developers should embrace this—it would limit liability and distinguish them from laggards and lower-quality copycats. 9. Standardization and regulation. Industry standards and government regulation cover almost every aspect of cars today. Industrialization of driverless cars will require significant doses of both, too. Standards, especially those enforced by government regulation, ensure reliability, compatibility, interoperability and economies of scale. They also increase public safety and reduce provider liability. 10. Public acceptance. Most new products take hold by attracting early adopters. The lessons and resources from that initial success help developers “cross the chasm” to mainstream success. The industrialization of AVs will depend on much earlier and broader public acceptance. AVs affect not only the early-adopting customers inside them, but also every non-customer on and near the roads those AVs travel. Without widespread acceptance—including by those who would not choose to ride in the AVs—industrialization is not likely to be allowed. See also: Where Are Driverless Cars Taking Industry?   Market Viability. The next three hurdles deal with whether AV-enabled business models work in the short term and the long term, both in beating the competition and other opponents. 11. Business viability. Analyses of AV TaaS business models are generally optimistic about the possibility of providing service for much less than the cost of human-driven services or personal car ownership. Current cost-per-mile estimates are nowhere near long-term targets, however. Most players are also underestimating the cost to scale. It remains to be seen whether rosy market plans will survive contact with the marketplace. 12. Stakeholder resistance. As the old saying goes, one person’s savings is another’s lost revenue. The industrialization of driverless cars will require overcoming the resistance of a large host of potential losers, including regulators, car dealers, insurers, personal injury lawyers, oil companies, truck drivers and transit unions. This will not be easy, as the potential losers include some of the most influential policy shapers at federal, state and local levels. 13. Private ownership. AV TaaS services are only a waypoint on the path to transformation of the private ownership market. If AVs are to revolutionize transportation, they will have to appeal to consumers who have long preferred to own their own cars. Privately owned cars account for the vast majority of all cars and all miles driven. Secondary Effects. Technology always bites back. The industrialization of AVs could induce huge negative secondary effects. Most will unfold slowly, but two consequences are already concerning and must be addressed as part of the industrialization process. 14. Congestion. Faster, cheaper and better transportation will deliver greater economic opportunity and quality of life—especially for those who might otherwise not have access to it, like the poor, handicapped and elderly. But, it might also cause a surge in congestion by driving up the number of vehicles and vehicle miles traveled. This happened with ride-hail services, including Uber and Lyft. According to a recent study by the San Francisco County Transportation Authority, for example, congestion in the densest parts of San Francisco increased by as much as 73% between 2010 and 2016. The ride-hail services collectively accounted for more than half of the increase in daily vehicle hours of delay. 15. Job loss. Some argue that the history of technology, including transportation technology, shows that new services will create more jobs, not less. Few argue, however, that the new jobs go to those who lost the old ones. There’s no getting around the fact that every AV Uber means one less human Uber driver—even if other jobs are created for engineers, maintainers, dispatchers, customer service reps, etc. The same holds true for AV shuttles, buses, trucks and so on. Early AV TaaS providers will operate under an intense spotlight on this issue. Providers will have to anticipate and ameliorate potential public and regulator backlash on job loss. * * * There’s an old saying in Silicon Valley that one should never mistake a clear view for a short distance. The revolutionary potential of AVs is clear. Yet, we are still far from the widespread adoption needed to realize their benefits. Don’t mistake a long distance for an unattainable goal, though. As a close observer, I am enthusiastic (and pleasantly surprised) by the progress that has been made on AV technology. Leading developers like Waymo, GM Cruise, nuTonomy and their diaspora have raced to build AVs and progressed faster than many, just a few years ago, thought possible. See also: Driverless Cars and the ’90-90 Rule’   Industrialization is a marathon, not a sprint. It depends on overcoming many hurdles, including the 15 I’ve laid out. The challenges of doing so are great—likely greater than many current players (and their investors) perceive and are positioned to address. New strategies are needed. A shakeout is likely. That’s how innovation and market disruption work. That is why most contenders fail and why outsized rewards go to those who succeed. Whoever thought that a phone maker or a search engine company could be worth a trillion dollars? Is it outlandish to believe, as I still do, that driverless cars would be worth multiple trillions?

Property/casualty claims is destined to transform more than any other area of the insurance business over the next decade.

Many may see that as a provocative statement, especially with all the attention on distribution and underwriting. After all, there are so many new entrants, insurtech startups and new technology solutions aimed at disrupting or transforming distribution and underwriting that it may seem difficult to justify the statement that claims will transform even more.

To be sure, distribution, underwriting and other areas of insurance are undergoing transformation and may look quite different a decade from now, with significant variations by line, of course. But, keep in mind that claims is already a complex and sophisticated part of the business, with high levels of expertise, extensive partner networks and major usage of technology. In addition, claims touch points are even more critical in today’s environment of heightened customer expectations and insurer focus on improving the customer experience.

See also: New Power Shift in P&C Insurance  

But back to the initial provocative statement – let me provide some rationale for why I believe claims will be very, very different a decade down the road. No one can predict exactly what claims will look like in 10 years, but here is a view on what is likely to change significantly – in some cases radically:

• New Products: Insurers already have new on-demand, episodic, and parametric-type products in the market. The advance of technology continues to create new risks that insurers are covering (such as cyber risk). Managing claims for these products is often different. Many of the new small-premium, high-volume types of products will require fully automated claims processing, including those triggered by smart contracts.
• Liability: Manufacturers of autonomous vehicles, IoT devices for property and other connected-world devices may choose to take on the liability of their products. In this case, the claims that do occur may be handled by the manufacturer, TPA partners or insurers that may be underwriting the risk. In addition, there are many uncertainties about which parties will be liable in complex new connected-world ecosystems.
• Prevention and Mitigation: The real-time, connected world affords insurers the opportunity to assist customers with risk management. This means that there may be a fusion of loss control engineering and claims as the focus shifts from post-incident indemnity to prevention and mitigation.
• Repair/Replace Changes: The physical objects that insurers insure are becoming more automated, and many new types of devices are appearing in homes, farms, vehicles and factories. This will affect damage estimates and approaches to repairing and replacing lost, stolen or damaged items.
• Partnerships: Insurers are already quite experienced at partnering in the claims area. However, the supplier landscape is changing, and insurers must determine the best way to partner with new providers of connected devices, solutions and services.
• Technology: AI and machine learning for automated damage assessment and fraud detection; visualization and location intelligence for CAT planning and real-time deployment; mobile, self-service FNOL for more types of claims; and other technologies like these will make major inroads into the claims environment.

See also: Keys to Loyalty for P&C Customers  

These areas will all warrant attention by claims executives. The drums of change are now beating – and real change will start to be felt in the next 12 to 18 months. When all these things are considered, I believe that major transformation is in store for claims. And for many insurers, it is going to be earth-shattering.