Tag Archives: thought leadership

Going Beyond the Incremental

Disrupt or get disrupted. More than ever, that’s true for incumbent insurers today more than ever because of the rise of insurtechs and the rapid shift in customer expectations.

It is not as if large incumbent insurers are unaware of this shift. The challenge facing them is overcoming the inertia and finding ways to transform their operations and business models beyond incremental improvements.

Incremental transformation is an oxymoron but reflects the reality

Global investment in insurtech startups totaled $10.5 billion in the first nine months of 2021, and traditional insurers are feeling the heat from the disruptive business models and innovative offerings from these digitally native startups.

Traditional insurers are impeded by technical debt. When they leverage digital technologies, those benefits are often incremental because of legacy burdens. Most transformational programs are laced with initiatives focused on internal operational efficiency, cost reduction or improvement of existing processes for a better customer experience. All of this progress proves incremental, at best.

See also: 2022 Resolutions to Foster Innovation

What would disruptive innovation look like?

Large incumbent insurers must transform their business models by challenging some of their principles. They need to begin charting a destination and create a vision for the organization. 

Some of these transitions can be disruptive, internally as well as externally, such as the following:

1. From agent-based models to total customer ownership

The traditional agent-based models worked well in an era where distributed ownership was the de facto model for offering a personal touch to customers spread all over. Digital technologies offer insurers the ability to offer highly personalized services without intermediaries by enabling total customer ownership.

2. From risk mitigation to risk prevention

The broader market trends toward casualty prevention and general safety are rapidly changing insurers’ underlying risk management paradigm. Automotive safety equipment and healthy living mechanisms are examples in auto insurance and health insurance, where insurers must strategically partner with manufacturers and service providers. Insurers must shift focus from traditional reactive underwriting to strategic risk management. 

3. From drawn-out underwriting to single-click policy issuance

Insurtechs, price comparison providers and aggregators are compelling traditional insurance product leaders to offer near-real-time underwriting. Customers expect single-click purchase of insurance products the same way they do in other aspects of their lives. Automated ingestion and processing of customer documents and supporting media can enable quick and accurate extraction and processing of relevant information. Real-time underwriting, when combined with analytics, can make this possible. 

4. From hindsight-based risk profiling to foresight-based risk assessment

With increasing data sources and the improved ability of insurers to gather intelligence, insurers are now faced with the challenge of processing larger sets of parameters for risk assessment. On the other hand, increased uncertainty means that such parameters are not the true indicator of impending future risks. Hence, historical trends are giving way to predictive insights as a means of risk assessment, both in terms of personal risk profiles as well as broader risk spreading.

5. From multi-stage claim assessment to digital real-time claim approval

Traditional assessments of claims involved multiple checks and balances and surveys. The availability of mobile devices for real-time capture of claim proofs along with locational accuracy, drones for surveys and automated image and video processing combined with artificial intelligence (AI) for claim assessment can truly transform the way claims are processed. 

Real-time underwriting, single-click product purchase, drastically reduced claim approval cycles, optimized costs and real-time availability of risk profiles and claims data can enable insurers to offer divergent insurance products that could not be conceived earlier.

See also: Creating Room for Innovation

A platform approach is pivotal

Most transformation opportunities didn’t seem real until digital technologies made them possible. However, just because these possibilities look feasible today doesn’t mean that they are easy to accomplish.

Success in any of these areas, let alone all of these together, requires a concerted effort that is consistent across the organization. Insurance providers need a cohesive and agile operational strategy that is backed by a platform that can support these transformational processes and systems across the enterprise. 

This is critical because, even if an insurer implements mobility for omnichannel engagement, the underlying process needs to be integrated and nimble. Similarly, speed and accuracy remain compromised without access to the appropriate context in the form of documents and media and real-time intelligent processing. The same is true for other digital technologies, such as AI and analytics. In short, integration is key. 

A platform-based approach addresses the end-to-end customer journey while integrating all the digital capabilities that are part of the disruptive innovation jigsaw.

Genomics Revolution in Life Insurance

Following his induction into the Insurance Hall of Fame at the Global Insurance Symposium held by the International Insurance Society last year, I sat down — via Zoom, of course, in these pandemic days — with Greig Woodring for what I thought would be some reflections on the history of the life insurance industry. After all, Woodring was the longtime CEO who built RGA into a giant with approximately $3.5 trillion of life reinsurance in force and assets of $89 billion. 

In fact, he led us into a fascinating discussion of the future of life insurance, based on some developments in genomics that could make life insurers partners in health and greatly reduce (and eventually even eliminate?) many cancers as a cause of death among their clients.

Here is a lightly edited transcript of our discussion:


You’re often eloquent about the sweeping changes in life insurance that you’ve witnessed in your distinguished career, but I’d like to zero in on the opportunity you’re pursuing now, to exploit the extraordinary progress in understanding the human genome. You—and I—think genomics will have a profound effect on life insurance, among many areas, and I’m hoping to explore the opportunities. 

Greig Woodring:

Our increasingly detailed understanding of genetics and how it affects our health will turn a lot of the life insurance selection process on its head. The change will accelerate when the cost of sequencing a person’s genome drops from $1,000—roughly where it is today—to $100, which will be so cheap that pretty much everybody can have a copy of their genetic information. And the drop in cost isn’t that far off.

Many have gotten some information from some of the consumer tests, the 23andMe-type tests, and there’s going to be a time in the near future where you get all the information about your genome from advanced genomic sequencing.

Life insurers can also use genetic information to improve the health of existing, in-force policyholders, for the benefit of all. The interests are perfectly aligned. Life insurers want their policyholders to live longer, just as they themselves do. 

Many researchers believe, and are intensively investing money and effort, in the pursuit to extend the maximum human lifespan beyond the 100-year or 115-year mark to maybe 120 to 150. But who wants to live to 150 unless they’re healthy? So, life insurers may be well-positioned to extend the “healthspan” of their policyholders. Life insurers should be concerned about the health of their policyholders more actively.

Life insurers will have to get up a genomics learning curve. They haven’t really begun that yet. And I think the understanding and usage of genetic information will separate companies that are successful in this next wave from the ones that fall behind a bit.


I can imagine an adverse selection problem. I mean, if I’m the one who pays $200 to have my genome sequenced and interpreted, I’m going to know much more about my likely lifespan than an insurer can, without access to that information. Does that seem to be a big problem, or do you see ways around the adverse selection issue?


I think that is a serious problem. Insurers will have to deal with that whether they want to or not. When consumers know their genetic information and can decide whether to buy life insurance, and how much, based on that information, the underwriting process needs to adapt. In the near future, clinical grade genomic information will be inexpensive and widely available.


Tell us a bit about Genomic Life, a company that you’ve been involved with for several years now and that I think illustrates the kind of opportunity that genomics will create, whether for insurers or for others.


Genomic Life is a service company, not a life insurer. A product that we’re offering first and have been for a couple of years now with good success is a cancer product. If someone gets cancer, we’ll sequence the cancer to help inform precision treatment, and we will provide a cancer support specialist and concierge navigation services that help them get through the disease and its emotional body blows. We’ll get our members into clinical trials at a much higher rate than they would if left to their own devices and steer them to the best cancer centers. 

It’s very difficult to navigate through the labyrinth of a disease like cancer in the environment that we have for healthcare delivery in the U.S. market. So, we help people get through that.


And, at least as I see it, that sequencing of the cancer’s genome is just the start. AI will kick in, in terms of the analysis of people’s genomes and what they say about, among other things, propensity for certain diseases, as well as in terms of possible treatments for diseases. A business owned by Google showed earlier this year that its AI could determine how proteins fold, more accurately than the chemical process that had been used up until now—and that cost hundreds of thousands of dollars and required more than a year just to determine the shape of a single protein. The final shape of a protein—and not just the string of amino acids that compose it—determines so much about how that protein acts. And once you can do this kind of analysis, about the shape, in a computer rather than in a lab, the pace of analysis kicks into an exponentially faster gear. 


I agree. That was a really big deal, even if it was little-noticed outside of the world that follows those sorts of things. If you think about the rapidity of the development of COVID vaccines, the same mRNA technology that was used for that could be used to develop cancer vaccines. 

That is extremely possible and a logical next step. There are people working on it right now. So, don’t be surprised if there’s a whole host of cancer vaccines coming in the next couple of decades.


Are all these developments in genomics a separate stream that branches off from what you’ve done in life insurance or do they then feed back into life insurance?


It all feeds back into life insurance. Think about a life insurance company with a million policies. Those million policies are going to get 50,000 cancer cases a year. Now imagine we can keep each of those 50,000 people alive for an extra two years. What is the value of that to the insurance company? And we think that is doable, today.

We’re just touching the surface of dealing with cancer today compared with when you have liquid biopsies [blood tests that can detect a broad array of cancers] and other genetic-based tests coming along that will lower the death rates. 

We’d like to get to the point of helping insurance companies largely eliminate cancer among their policyholders as cause of death. And right now, for most of them it’s a leading cause of death; underwriters don’t screen out cancers as well as they do, for example, cardiovascular deaths. 

You’ll be able to tell your policyholder base that, look, we’ll help you increase your health lifespan, at least partially, as best we can. This is a good message for a life insurance company.


Fascinating. That feels like a reasonable place to end things. But do you have any parting words? 


As you said, artificial intelligence has a big role to play. As you begin to use artificial intelligence in combination with genetic information, I think we’ll find doors to rooms we didn’t know existed. So, I’m really excited about the future of life insurance, and in a different way than it’s been in the past. Not only protecting you and your family from the adverse effects of premature death but helping maintain you in the best position to live a healthy, long life. 

If that’s what a life insurance company becomes, I think that everybody will be excited.

Educating Owners on New Risks

COVID-19 changed everything, including insurance. New risk opportunities emerged with each lockdown that included increased cyber threats among a dispersed workforce. With risk evolving at such a rapid rate, one can’t help but wonder: Does the average founder of a small to medium-sized enterprise (SME) understand their risks and business insurance? Do SME clients understand the importance of risk transference should a data breach occur? 

My team at Embroker conducted a survey of 500 SME owners, CEOs and tech startup founders and found a discrepancy between what founders know about risk and the actions they have taken to mitigate that risk. Embroker found that only 22% of owners and founders say they have read and understood all of their policies. 

The good news is half of the owners and founders rely on a broker to sign up for coverage, which may improve their understanding. Embroker research also shows that SME owners lack an understanding of insurance industry standards regarding risk mitigation and often look to brokers for better education. 

Trusting Brokers 

The report shows that 25% of owners and founders rely on the broker to fully research and price out their options. One in five admitted to not knowing how their insurance purchases are handled.

Almost one in three (29%) SME owners allow their insurance to auto-renew without making changes, while 74% of tech founders either engage with a broker or have someone internal to assess their needs and options upon renewal.

See also: A Commentary on Agents & Brokers

Cyber Risk

As business threats intensify and concern grows, both owners (46%) and tech founders (57%) fear they don’t have sufficient coverage in the event of a ransomware attack. But the concern about this risk remains low: 63% of SME owners believe they are unlikely to face a data breach or ransomware attack.

Tech founders, on the other hand, are more aware of cyber risks than other industry business owners. 58% of tech founders believe they are likely to face a data breach or ransomware attack. However, tech founders are still not securing coverage, with only 34% having cyber policies. Why is this? 

It’s likely tech founders don’t understand how transferring their risk in the event of a cyber attack can dramatically help their business, or they accept the popular assumption that obtaining cyber insurance puts you at greater risk of an attack. This is simply not true. Here’s the reality: It’s not if a company will face a cyber attack, but when.

We now know that COVID-19 pandemic created a dispersed workforce and thereby created more opportunities for hackers to spot weaknesses. Ransomware-as-a-service is becoming an increasingly common tool. According to ABC News, cybercrime is up 600% as a result of the COVID-19 pandemic. 

To learn more about the business insurance approach for SME owners, CEOs and tech founders, download the full report here.

Keeping Human Element in AI

Without a doubt, artificial intelligence (AI) is a valuable driver of innovation in today’s insurance industry. Unfortunately, the predominant attitude toward AI in our culture still hangs on a suspicion that people will lose their livelihoods as their jobs are taken over by autonomous machines. Many fear that we’ll lose some piece of our humanity as more and more important decisions are delegated to AI algorithms.

In fact, there is good reason to be cautious when it comes to AI, but that doesn’t mean we should shy away from using it. Indeed, we need to approach this technology with a keen attention to the importance of the human element.

Like so many other things, AI is what we make of it. It has the potential to improve our lives in meaningful ways. If we don’t act with clear and thorough deliberation, though, it also has the potential to do harm. Insurers must take care that their AI initiatives are deployed and governed in ways that support people. That means enhancing the quality of life for the employers and front-line workers whose livelihoods we safeguard. It also means empowering the claims managers and other professionals who bring that critically important human touch to our business.

AI Misconceptions

In the popular imagination, AI is imbued with almost magical powers — the ability to digest vast amounts of data, ponder the implications of that information and draw meaningful conclusions from it. Pop culture portrays AI as being fully autonomous, assuming decision-making powers and depersonalizing our lives and our relationships in the process.

Yet even tech giant Facebook is learning that a “set and forget” approach to AI generally doesn’t work well. As early as 2014, the company was using AI to categorize images, sift through content and identify material to be flagged as inappropriate. The company has been under fire from multiple directions for its sometimes overzealous policing of content as well as its apparent inability to flag truly objectionable material.

In the end, Facebook’s problem is a human one. Some critics argue that the company aims to maximize user engagement at the cost of all else and that its content must be regulated more carefully. Others argue that Facebook is too quick to block content that it doesn’t like. Both concerns speak to problems that require human solutions — not technical ones.

That leads to a fundamental question: “How can AI best serve human needs?”

AI Reality

Today, AI can very effectively support human decisions, primarily by shedding light on important matters that require attention. Most current AI applications consist of machine learning algorithms designed to perform clearly identifiable tasks based on a set of predefined business rules.

Those business rules are created and shaped by human beings. They must also be monitored and governed continuously, with a sharp eye toward the ethical implications of AI applications. Attention to the human element is essential.

The good news, though, is that human beings remain in charge. The future is in our hands. AI is a very powerful tool, with the capacity to dramatically improve people’s lives. We have the capacity to continue steering our AI initiatives in a direction that aligns with our moral and ethical priorities.

See also: How to Use AI in Claims Management

AI Supports the Human Element

Truly effective AI programs aren’t about replacing people. Like any other tool, AI can enhance the effectiveness and efficiency of the people who make our industry run smoothly.

In claims management, machine learning algorithms are most frequently deployed to aid in fraud detection, but AI is increasingly being applied in far more sophisticated ways, as well, such as matching injured workers to the medical providers most likely to help them recover quickly and completely. It’s helping claims managers to effectively handle heavy caseloads by watching for meaningful changes to each case, flagging noteworthy changes and bringing them to the attention of a human being who can assess them further and take action.

For heavily burdened claims managers, AI serves as a kind of intelligent assistant, relieving them of many of the tedious elements of monitoring cases while ensuring that nothing slips through the cracks.

Consider the case of an injured worker whose medical case has just taken a wrong turn. The details are buried in the physician’s notes, but the claims manager responsible for the case simply hasn’t had time to read the report yet. AI can spot that problem immediately and bring it to the claims manager’s attention. The vital human element is still there, but now it can be better-informed and more effective. The claims manager can act promptly, steering the case toward a better medical outcome.

AI can match injured workers up with the providers most likely to deliver positive results. That’s not simply a matter of ranking physicians based on their overall track record, though. AI can digest the details, including the type and severity of the injury, the patient’s medical history and other factors to deliver a nuanced recommendation as to which providers are most likely to help the employee recover quickly and completely.

The data fully supports this approach. When AI is applied to the task of matching insured workers with the best providers for each case, top-ranked recommendations result in under 28 days of missed work, whereas the lowest-ranked quintile shows an average of over 570 days of missed work.

Think about what that means to an injured worker and their loved ones. It’s the difference between short-term injury and chronic pain. For many, it’s the difference between dignity and depression.

This, in the end, is what AI is capable of. It’s true that we should proceed with caution. Like any other technology, AI has the capacity to deliver tremendous benefits, but it also has the potential to be misused. We all have a responsibility to see that AI is done well, that it has a humanizing influence, not a dehumanizing one. In the process, we can improve the lives of insured employees, claims managers and other stakeholders.

As first published in Claims Journal.

How Important Is the Human Touch Really?

While ITL serves as a platform for the varied insights and opinions of others, they tend to coalesce around certain themes: on the need to innovate, on the importance of moving faster than the industry historically has, etc. It’s not often that I see articles with almost opposite points of view, let alone have them arrive on top of each other, but that’s what occurred with two of the six articles I highlight below.

One argues that the human touch is overrated these days, that what clients really want is much more ability to self-service. The other says, among many other things, that “two in three consumers are resistant to the idea of purchasing insurance or filing claims on a website or app without speaking to a human being.”

Who’s right, and who’s wrong? Well, I have my own opinion on that.

Basically, I think that both articles make important points but that the right answer–as you’ve seen me say many times now about almost all things digital–needs to be a hybrid based on constant, small tests. Those tests will let you gradually find your way to the right approach, for now, and to let you keep adapting as your customers and competitive environment change.

Personally, I’m big on self-service. I have zero interest in talking to an agent of any company about anything if I don’t have to. So, I resonate with the statistics in “Human Touch: How Crucial Is It Really?”: that “90% of consumers expect a brand or an organization to offer a self-service consumer support portal”; that “today’s customers manage 85% of the relationship with an enterprise without interacting with a human”; and that “73% of all consumers say that valuing their time is the most important thing companies can do to provide them with good customer service.”

I have no reason to doubt any of those stats, and I suspect that many insurers, while moving in the self-service direction, still lag well behind their customers’ desires.

At the same time, as “Customers Wary of AI-Driven Insurance” argues, based on an extensive survey by Policygenius, “despite faster claims turnaround times, the potential for lower rates and other AI-enabled transformations, customers still value a human touch.” The survey found that “83% of consumers wouldn’t feel comfortable if their home, auto, or renters insurance claim was reviewed exclusively by artificial intelligence” and that, after a loss, only “around 43% of homeowners would agree to let a drone evaluate their property rather than a human.” Policygenius also reports that “72% of consumers wouldn’t be comfortable purchasing insurance online without ever speaking to a real person, and 64% wouldn’t feel comfortable filing a claim on a website or app without human interaction.”

If you pay attention to all the stats, you can find your way to a middle ground. You let customers handle as many of the routine matters on their own as possible–updating policies, determining when a payment is due, etc. But you make sure a human is always on call, at least during business hours, and you introduce a human into every process that might require advice or any sort of emotional support, whether that’s as fraught as filing a claim might be or as straightforward as assuring someone they’ve made the right choices as they’ve done research and chosen insurance coverages online.

But that’s just the broad outlines of a hybrid. There’s a lot of play in the details. Preferences will vary based on the age of the client, the income, the education level, the location, the… who knows what else?

The best way to see what customers want is to offer them options and then watch and listen to how they react. Do agents need to be monitoring all changes that clients make to their policies online, including to coverages, or just some? Is there some way to give clients freedom online but then ping agents about where they might way to follow up? What is the best way to help people do their own research online while introducing agents at just the right time and in just the right way to turn a search into a sale?

I encourage you to click on and read the first two articles below so you can make up your own mind. But my mantra on innovation for 25 years has been, Think Big, Start Small, Learn Fast. So, while I think the statistics in the articles are very helpful–I wouldn’t have published them otherwise–they’re just the starting point. The only way to learn what the right answer is for you is to test and learn, then test and learn some more and some more and some more.