Several years ago, property and casualty insurance executives were looking over their shoulders anxiously at a growing number of internet startups. Who were these scruffy people wearing black turtlenecks? Could they really “disintermediate” legacy providers that had been around for a century or more? Since then, we’ve all evolved. By now, most brands know they have inherent strengths that are hard to dislodge. The startups have matured, too, and they clearly have something to offer the market. We’re now working with companies in both camps, helping them navigate this new normal, where collaboration, acquisition and competition are all plausible options. Some insurers may think they’ve dodged a bullet. But insurtech’s threat is more stealthy, and no less powerful. Insurtech: the new, new thing? At this fall’s InsureTech Connect trade show, literally thousands of people descended on Las Vegas to show and examine the latest offerings, from core systems, predictive analytics tools and anything-as-a-service to pitches addressing distribution, pursuing unserved niche markets, offering comparative pricing and broker services and more. In our recent report on the state of insurtech, we cautioned insurers to look beyond the many truly interesting offerings now coming to market. As impressive as these tools are, we urged decision makers to stay focused on the capabilities that make their companies unique. See also: Has a New Insurtech Theme Emerged?   What do insurers really do? So, what are those capabilities? At holiday dinner tables, you may find yourself talking to a relative about what insurance is, and why it’s important. You may say something like, “We create products that help manage risk by sharing the possibility of individual loss with a larger pool of users.” This explanation held true for a long time, but, with the rise of insurtech, it may not be the best way to look at your business. That’s because many insurtech companies have emerged to manage the firehose of data that now shapes our world: the Internet of Things (IoT), wearable health devices, connected cars, artificial intelligence and more. Of course, there’s still a role for insurers when someone else captures and gets the insight from that data. But it’s a commodity role, driven by who is willing to write a policy to offset the risk at the lowest rate. There won’t be many winners, and the margins won’t be attractive. Some insurers see their business as settling claims and handing out checks. But when someone else is using telematics to assess driving habits, or social media to understand lifestyle risks, who will be able to monetize this data? Increasingly, underwriting depends on getting deep into the data-driven weeds. If you’re not there, recognize that someone else will be. The rise of outside money There’s another factor shaping insurance today: the amount of private equity (PE) and venture capital (VC) money flooding into the industry. An industry as highly capitalized as insurance was bound to have external investors come knocking eventually. Now, they have. To be blunt, many insurance systems are too costly and too slow. PE and VC firms have seen this, and they’ve said to themselves, “I don’t have to be perfect, and I know I can be more efficient than this. Even if I’m only a little bit better than the legacy players, I can make a very healthy profit.” It’s a form of arbitrage, and competition could soon get a lot tougher. With the acceleration of insurtech and related technologies such as cloud and artificial intelligence, PE and VC firms have found a way in that doesn’t require them to show a century of stability. They can do very well developing an insurtech play for very specific aspects of the P&C value chain. Many traditional companies are finding themselves in a commoditized business, without the structure of a commodity manufacturer. Finding your way to play Some of the most exciting developments in technology are now reshaping the insurance industry. That spells new opportunities and new risks. With the rise of PE and VC funding, we now see competition emerging from companies with significant resources—and they’re privately held so they can be more patient investors. See also: Advice for Aspiring Leaders in Insurtech   Legacy insurance companies still have enormous advantages, and many opportunities to win. But most won’t be able to do it alone, and there are many examples of insurers that wasted time (and money) on the wrong insurtech acquisition or partnership. As the cycles of innovation and capital movement accelerate, you’ll need to be more focused than ever on the capabilities that make your company great. Insurtech is a game-changer.  Make sure you’re playing the right game.

The range of risks facing company executives or directors and officers (D&Os) – as well as resulting insurance claims scenarios – has increased significantly in recent years. With corporate management under the spotlight like never before, Allianz Global Corporate & Specialty has identified, in its latest risk report, Directors and Officers Insurance Insights 2020, five mega trends that will have significant risk implications for senior management in 2020 and beyond. 1. More litigation is coming from “bad news” events Allianz continues to see more D&O claims emanating from “bad news” not necessarily related to financial results, including product problems, man-made disasters, environmental disasters, corruption and cyber-attacks – “event-driven litigation” cases that often result in significant securities or derivative claims from shareholders after a share price fall or regulatory investigation related to the “bad news” event. Plaintiffs seek to relate the “event” to prior company or board statements of reassurance to shareholders and regulators of no known issues. Of the top 100 US securities fraud settlements ever, 59% are event-driven. One of the most prevalent types of these events is cyber incidents. Allianz has seen a number of securities class actions, derivative actions and regulatory investigations and fines, including from the E.U.’s General Data Protection Regulation (GDPR), in the last year, and expects an acceleration in 2020. Companies and boards increasingly will be held responsible for data breaches and network security issues that cause loss of personal information or significant impairment to the company’s performance and reputation. Companies suffering major cyber or security breaches increasingly are targeted by shareholders in derivative litigations alleging failure to institute timely protective measures for the company and its customers. The Marriott case – where the hotel chain announced that one of its reservation systems had been compromised, with hundreds of millions of customer records left exposed - is a recent example of a cyber breach resulting in D&O claims – one $12.5 billion lawsuit among several filings alleges that a “digital infestation” of the company, unnoticed by management, caused customer personal data to be compromised for over four years. 2. ESG and climate change litigation on the rise Environmental, social and governance (ESG) failings can cause brand values to plummet. And investors, regulators, governments and customers increasingly expect companies and boards to focus on ESG issues, such as climate change, for example. Climate change litigation cases have been brought in at least 28 countries to date (three-quarters in the U.S.). In the U.S., there are an increasing number of cases alleging that companies have failed to adjust business practices in line with changing climate conditions. Human exploitation in the supply chain is another disrupter and illustrates how ethical topics can cause D&O claims. Such topics can also be a major focus for activist investors whose campaigns continue to increase year-on-year. Appropriate company culture can be a strong defense risk-mechanism. Many studies show board diversity helps reduce and foresee risk. Regulators are keen to investigate and punish individual officers rather than the entity, forcing directors into increased personal scrutiny to provide assurance that they did due diligence to prevent such cases from occurring. See also: How to Deliver Tough Message on D&O   3. Growth of securities class actions globally Securities class actions, most prevalent in the U.S., Canada and Australia, are growing globally as legal environments evolve and in response to growing receptivity of governments to collective redress and class actions. Significantly, the E.U. has proposed enacting a collective redress model to allow for class actions, while states, such as Germany, the Netherlands and the U.K., have established collective redress procedures. The pace of U.S. filing activity in 2019 has been only marginally slower than record highs of 2017 and 2018, when there were over 400 filings, almost double the average number of the preceding two decades. Shareholder activism has increased. Approximately 82% of public company merger transactions valued over $100 million gave rise to litigation by shareholders of the target company threatening that the target company’s board will have breached its duties by underpricing the company, should the merger succeed. 4. Bankruptcies and political challenges With most experts predicting a slowdown in economic growth, Allianz expects to see increased insolvencies, which may potentially translate into D&O claims. Business insolvencies rose in 2018 by more than 10% year-on-year, owing to a surge of over 60% in China, according to Euler Hermes. In 2019, business failures are set to rise for the third consecutive year by more than 6% year-on-year, with two out of three countries poised to post higher numbers of insolvencies than in 2018. Political challenges, including significant elections, Brexit and trade wars, could create the need for risk planning for boards, including revisiting currency strategy, merger and acquisition (M&A) planning and supply chain and sourcing decisions based on tariffs. Poor decision- making may also result in claims from stakeholders. 5. Litigation funding is now a global investment class These mega trends are further fueled by litigation funding now becoming a global investment class, attracting investors hurt by years of low interest rates searching for higher returns. Litigation finance reduces many of the entrance cost barriers for individuals wanting to seek compensation, although there is much debate around the remuneration model of this business. Recently, many of the largest litigation funders have set up in Europe. Although the U.S. accounts for roughly 40% of the market, followed by Australia and the U.K., other areas are opening up, such as recent authorizations for litigation funding for arbitration cases in Singapore and Hong Kong. Next hotspots are predicted to be India and parts of the Middle East. Estimates are that the litigation funding industry has grown to around $10 billion globally, although some put the figure much higher, in the $50 billion to $100 billion range, based on billings of the largest law firms. The state of the market Although around $15 billion of D&O insurance premiums are collected annually, the sector’s profitability is challenged due to increased competition, growth in the number of lawsuits and rising claims frequency and severity. Loss ratios have been variously estimated to be in excess of 100% in numerous markets, including the U.K., U.S. and Germany in recent years due to drivers such as event-driven litigation, collective redress developments, regulatory investigations, pollution, higher defense costs and a general cultural shift, even in civil law countries, to bring more D&O claims both against individuals and the company in relation to securities. The increased claims activity, combined with many years of new capital and soft pricing in the D&O market has resulted in some reductions in capacity. In addition, there has also been an increase in the tail of claims. Hence, there is a double impact of prior-year claims being more severe than anticipated and a higher frequency of notifications in recent years. As for claims severity, marketplace data suggests that the aggregate amount of alleged investor losses underlying U.S. securities class action claims filed last year was a multiple of any year preceding it. See also: Why Private Firms Should Buy D&O   Despite rising claim frequency and severity, the industry has labored under a persistent and deepening soft market for well over a decade before seeing some recent hardening. Publicly disclosed data suggests D&O market pricing turned modestly positive in 2018 for the first time since 2003. However, D&O rates per million of limit covered were up by around 17% in Q2 2019, compared with the same period in 2018, with the overall price change for primary policies renewing with the same limit and deductible up almost 7%. From an insurance-purchasing perspective, Allianz sees customers unable to purchase the same limits at expiration also looking to purchase additional Side A-only limits and also to use captives or alternative risk transfer (ART) solutions for the entity portion of D&O Insurance (Side C). Higher retentions, co-insurance and captive-use indicate a clear trend of customers considering retaining more risk in current conditions.

It’s been more than four years since the term "insurtech" was coined. Back then, a surge in interest in deploying new technology, data and analytics to improve all parts of the insurance buying process held the potential for radical change. Disruption in financial services saw the emergence of challenger banks. Today, costs have been reduced, and customer experience has improved. If even banking could be fun, surely insurance would be next? The message has got through – few in the insurance industry would claim that better data, analytics and technology are not a critical part of their future success. Blueprint One by Lloyd’s of London identifies how it will deliver on the Future of Lloyd’s manifesto. It provides an excellent vision for any insurance organization about how the world should look a few years hence. But don’t mistake a clear view for a short distance. Just because we know what the future looks like doesn’t mean it’s going to be easy to get there; nor does it mean it will happen fast. Insurance is a grudge purchase. Other than the risk-obsessed or the highly analytical, few people, or companies, want to spend money on it. The future of insurance is not going to be about making buying insurance fun; it will be about making insurance disappear. Most of us are terrible at assessing the true risks around us. We overestimate the true risk of what has happened most recently, the things that have affected us personally and what we can’t control. We underestimate the impact of events that happen infrequently, that have happened to other people and that we think we control. People are often more scared of flying than driving to the airport, yet there is a far greater likelihood of having a fatal accident in a car journey than in a flight of the same duration. Some of the innovations in insurance in recent years have failed because they assumed people wanted more options about how to insure their possessions or insure against bad things happening. The reality is that we don’t want to have to make more decisions about insurance. It’s painful thinking about potential losses, and we see no immediate benefit from spending money on insurance. We tend to ignore it if we can. See also: 5 Emerging Trends for Insurance in 2020   Most of us buy insurance because it’s a required condition of something else, such as car insurance to drive, household insurance to get a mortgage or liability insurance to run a business. We’ll accept insurance if we get it for little or no cost (health insurance as an employment benefit, for example). It’s often painful to buy insurance – and even more so to make a claim. We can spend hours providing information online or by phone to get cover that reflects our needs. Or we can buy on an app on our phone and risk purchasing something that doesn’t match our needs. Either way, we rarely properly understand what we are actually covered for and suspect (often correctly) that we are duplicating our cover between different insurance policies in some areas, and have glaring holes in others. Traditionally, it was the role of the broker to ensure that we had adequate cover across all areas of our lives and businesses. That worked fine when most of our risk came from physical assets (houses), or lives, where a loss was generally beyond doubt (your house burns down or you die) and the risk could be assessed by actuaries and understood by the policy holder. Today, much of our value lies in the intangibles of our digital lives. It’s much harder to know what our exposure to cyber-risk is, or reputation, or liability is. At the same time, the old broking advisory business model is proving expensive and unable to deliver the depth of advice to keep up. Individuals and small businesses are being encouraged to go directly to the insurer, and corporations are looking beyond their brokers for advice. Unless we have to buy the insurance, people (and companies) often ignore it or defer the decision. Human nature isn’t going to change any time soon, so insurance needs to. The core fundamentals of insurance aren’t going away, but they will look very different. At some point, the concept of buying insurance as a separate protection will vanish. Insurance will become embedded into our purchases and corporate spending. Regulation will ensure the protection for loss still exists, but the onus will be on the provider of the services not the buyer. The easiest way to do something is not to do it all. We will not have to grapple with understanding our various insurance policies, because they won’t exist. If we lose, burn, break, flood, crash or hurt things, they will be put right, sometimes before we even realized we had a problem. See also: Future of Claims Intake for Insurance?   It’s not going to happen soon, but, in the meantime, look out for the insurers, innovators and technologists that are making insurance easier and clearer. A few might succeed in making it fun, but the best will be making it invisible. They are the future of insurance. You can find the article originally published here.

It was the ARM Partners Conference in New Orleans on April 18, 2012. I was to speak on change. The attendees, many with bloodshot eyes, were slowly filling the room. The program was the first of the morning. Slow and bloodshot are part of the culture of early a.m. in The Big Easy. I placed a trash can in front of the group and a bottle of baby aspirins on the podium. I explained that “my intention today is to create chest pains, because chest pains change behavior. If the chest pains get too serious, take a baby aspirin and place it under your tongue. If I upset your already queasy stomach, you can throw up in the garbage can.” Nervous laughter followed. An early slide included two quotes. The first: “Fat, dumb, and happy, commercial banks are being quickly replaced as financial intermediaries.” (Time magazine, June 28, 1993, Bernard Baurnohl). Agencies, not just bankers, needed that warning. The second quote was from Peter Drucker: “Whom the gods wish to destroy, they send 40 years of success.” That one was because recurring revenue from renewals makes many agents too comfortable. As John F. Kennedy said, "There are risks and costs to action. But they are far less than the long-range risks of comfortable inaction.” See also: Are You a Manager or a Leader?   How would your agency look if your marketing and sales were audited to see how well you were taking advantage of your opportunities? Is your organization about performance, sales, marketing, customer intimacy OR the daily transactions and the comfort of your staff and yourself? Auditors are tough: One with the Centers for Disease Control and Prevention in Atlanta said, “When the war is over, the auditor steps onto the battlefield and bayonets the survivors.” Is your agency and your team bruised and bloodied from battles of yesterday or up and running forward into the future? Will the marketplace, the ultimate arbiter of success, bayonet you or reward you? Are you the past or the future? Max DePree says, “The first role of the leader is to define reality.” The following questions may help you begin to define your starting point for tomorrow:

1. Do you and your team share understanding of and commit to the vision, values, mission and objectives established for your future? Will each of you and all of you be accountable for your performance and results? Are these your X commandments or X suggestions? Are these right for the world as it is and as it will be?
2. Is the marketplace you serve or hope to serve in decline, level or in ascendancy? If your answer is in decline or “flat lining,” can you find new products to offer your existing clients? Can you offer your existing products to new clients or, even better, can you offer new products (services) to new clients?
3. Is your team compatible with the market niches you serve? If you are blessed with some really experienced and wise baby boomers, will they be right for the Gen X and Gen Y that is your tomorrow? Will your English-speaking producers be right for a Laotian population? Will your clients shop producers based on their knowledge or their cultural/gender compatibility?
4. How will you sell in a non-verbal world? Is your delivery process (sales and service) of choice the preference of your clients and prospective clients? Are they comfortable with what and how you do business? Are you comfortable with what and how they want the relationship to be? CAN YOU ADAPT TO THEIR FUTURE?
5. What products, important today, might not be available tomorrow for you to sell? Is the National Flood Insurance Program sustainable, for instance, or will its vulnerability to adverse selection ultimately cause it to collapse? Will auto liability coverage be needed with self-driving cars? Will Gen Ys prefer private ownership of cars or Uber or public transportation? Will they have the appetite for home ownership that we had? Will your community survive? Will coastal properties be readily available, or will global warming have moved them all off of the coast?
6. What new opportunities might be available to you that are not in your "briefcase" today?
7. Will the advances in technology allow you to do more with your clients and prospects more efficiently/effectively? In a virtual world, might 7.5% commission be adequate where today you are blessed with 12%? Who will dictate commission levels in the future – you or your clients? Will carriers determine your commissions on what you need or what the market is willing to pay? Could you sell effectively with full disclosure of commission or quotes net of commission?
8. What will the world of retail - malls and Main Street -- be like tomorrow? Will all the action be on the banks of the Amazon?
9. Will the government finally move to a single payer healthcare system? Will your local doctors now satisfy their needs through their network versus as individual business owners? Will they be entrepreneurs or employees? Will they be in the business of business and the business of medicine, or will they specialize in only medicine?
10. In the future must you be “too big to fail,” or will you be too small to succeed?

I don’t know the answers. I don’t even know the questions that are appropriate for tomorrow. Your future doesn't depend on me. It depends on you. What do you know? What should you know? What will you do? Can you be profitable regardless of what the market is willing to pay? See also: 5 Transformational Changes for Clients   About 20 years ago, I was speaking to an agency conference and talked with one of the attendees. He was over 75, very traditional, successful, conservative and very comfortable in his ways. I asked if his exit from his agency by death or retirement would increase or decrease the value of his agency. His response was immediate, “Boy, you done gone from preaching to meddling.” I now offer you the same question – are you and the agency you own or work with ready, willing and able to move from yesterday and today into tomorrow? REALLY??? It’s your future.

This article by Lemonade co-founder and CEO Daniel Schreiber tackles a profound issue for insurance and offers an innovative solution. The article suggests a smart way to watch for bias hidden in algorithms and to correct for it. In the process, Daniel provides an opening toward a holy grail: being able to price risk accurately for each individual.

The article is well worth your time. We're delighted to be able to share it with you and hope you'll share it, too. The change will require support not just from incumbents and insurtechs but also from regulators, whose structures, as Daniel notes, are reasonably friendly in Europe but would require more adaptation in the U.S.

I won't describe in any detail what Daniel calls his "uniform loss ratio" test, which makes sure that AI-based pricing for individuals produces defensible results for every group when losses are measured against pricing at the group level. But I want to build on his proposed test and explore the implications for how we'll all need to adapt to a world of much more individualized pricing of risk.

First, consider the technical requirements that must be met. Specifically, the data requirements will necessitate a continuous re-examination of privacy issues. The industry is already facing legislation designed to prevent an insurer's access to specific, individual data. A few in the public policy sector have taken this to an extreme by introducing legislation that would deny consumers even the option to voluntarily share data with their insurer for their own benefit. 

Second, the more data that is aggregated by any organization, the more it becomes a target for bad actors. While all insurers ferociously protect their customers' data, the convergence of the required new computational capabilities and vast array of data raises the bar on cyber security significantly. 

Third, basing premiums on an individual's risk profile will intensify the spotlight on operational expenses. As insurers zero in on an individual's risk, that individual will have more transparency about the process and will tend to sign on with whatever insurer can cover his or her risk at lowest cost.

Fourth, how will customers react? The move to individualized pricing creates huge opportunities for innovation, but consumers need to participate in the development. Would we not want consumers to have a choice between traditional, segmented, pricing and the new, individual pricing?

The benefits of individualized pricing are clear. If we can be sure to avoid bias, we can take advantage of the full array of capabilities of artificial intelligence. And the "uniform loss ratio" test can get rid of the "ghosts in the machine": biases that are unintentional but that are currently unrecognized and unavoidable given the limitations of our data and computational capabilities. We can then democratize access to services and products and accelerate the move away from ratings and recovery and toward preventing risks.

The journey from here to there:

• Will require a substantial collection of innovations,
• Will increase the clarity and urgency of certain issues.
• Rightly will drive a stake through the heart of discrimination,
• Represents an abundancy of opportunity,
• Is, let’s face it, inevitable.

Might as well get moving, right?

Regards,

Guy Fraker
Chief Innovation Officer

This article by Lemonade co-founder and CEO Daniel Schreiber tackles a profound issue for insurance and offers an innovative solution. The article suggests a smart way to watch for bias hidden in algorithms and to correct for it. In the process, Daniel provides an opening toward a holy grail: being able to price risk accurately for each individual.

The article is well worth your time. We're delighted to be able to share it with you and hope you'll share it, too. The change will require support not just from incumbents and insurtechs but also from regulators, whose structures, as Daniel notes, are reasonably friendly in Europe but would require more adaptation in the U.S.

I won't describe in any detail what Daniel calls his "uniform loss ratio" test, which makes sure that AI-based pricing for individuals produces defensible results for every group when losses are measured against pricing at the group level. But I want to build on his proposed test and explore the implications for how we'll all need to adapt to a world of much more individualized pricing of risk.

First, consider the technical requirements that must be met. Specifically, the data requirements will necessitate a continuous re-examination of privacy issues. The industry is already facing legislation designed to prevent an insurer's access to specific, individual data. A few in the public policy sector have taken this to an extreme by introducing legislation that would deny consumers even the option to voluntarily share data with their insurer for their own benefit. 

Second, the more data that is aggregated by any organization, the more it becomes a target for bad actors. While all insurers ferociously protect their customers' data, the convergence of the required new computational capabilities and vast array of data raises the bar on cyber security significantly. 

Third, basing premiums on an individual's risk profile will intensify the spotlight on operational expenses. As insurers zero in on an individual's risk, that individual will have more transparency about the process and will tend to sign on with whatever insurer can cover his or her risk at lowest cost.

Fourth, how will customers react? The move to individualized pricing creates huge opportunities for innovation, but consumers need to participate in the development. Would we not want consumers to have a choice between traditional, segmented, pricing and the new, individual pricing?

The benefits of individualized pricing are clear. If we can be sure to avoid bias, we can take advantage of the full array of capabilities of artificial intelligence. And the "uniform loss ratio" test can get rid of the "ghosts in the machine": biases that are unintentional but that are currently unrecognized and unavoidable given the limitations of our data and computational capabilities. We can then democratize access to services and products and accelerate the move away from ratings and recovery and toward preventing risks.

The journey from here to there:

• Will require a substantial collection of innovations,
• Will increase the clarity and urgency of certain issues.
• Rightly will drive a stake through the heart of discrimination,
• Represents an abundancy of opportunity,
• Is, let’s face it, inevitable.

Might as well get moving, right?

Regards,

Guy Fraker
Chief Innovation Officer

Insurance is the business of assessing risks and pricing policies to match. As no two people are entirely alike, that means treating different people differently. But how to segment people without discriminating unfairly? Thankfully, no insurer will ever use membership in a "protected class" (race, gender, religion...) as a pricing factor. It's illegal, unethical and unprofitable. But, while that sounds like the end of the matter, it’s not. Take your garden-variety credit score. Credit scores are derived from objective data that don’t include race and are highly predictive of insurance losses. What’s not to like? Indeed, most regulators allow the use of credit-based insurance scores, and in the U.S. these can affect your premiums by up to 288%. But it turns out there is something not to like: Credit scores are also highly predictive of skin color, acting in effect as a proxy for race. For this reason, California, Massachusetts and Maryland don’t allow insurance pricing based on credit scores. Reasonable people may disagree on whether credit scores discriminate fairly or unfairly—and we can have that debate because we can all get our heads around the question at hand. Credit scores are a three-digit number, derived from a static formula that weighs five self-explanatory factors. But in the era of big data and artificial intelligence, all that could change. AI crushes humans at chess, for example, because it uses algorithms that no human could create, and none fully understand. The AI encodes its own fabulously intricate instructions, using billions of bits of data to train its machine learning engine. Every time it plays (and it plays millions of times a day), the machine learns, and the algorithm morphs. What happens when those capabilities are harnessed for assessing risk and pricing insurance? Many fear that such "black box" systems will make matters worse, producing the kind of proxies for race that credit scores do but without giving us the ability to scrutinize and regulate them. If five factors mimic race unwittingly, some say, imagine how much worse it will be in the era of big data! But, while it's easy to be alarmist, machine learning and big data are more likely to solve the credit score problem than to compound it. You see, problems that arise while using five factors aren’t multiplied by millions of bits of data—the problems are divided by them. To understand why, let's think about the process of using data to segment—or "discriminate"—as evolving in three phases.

Phase 1:

In Phase 1 all people are treated as though they are identical. Everyone represents the same risk and is therefore charged the same premium (per unit of coverage). This was commonplace in insurance until the 18th century. Phase 1 avoids discriminating based on race, ethnicity, gender, religion or anything else for that matter, but that doesn't make it fair, practical or even legal. One problem with Phase 1 is that people who are more thoughtful and careful are made to subsidize those who are more thoughtless and careless. Externalizing the costs of risky behavior doesn’t make for good policy, and isn't fair to those who are stuck with the bill. Besides, people who are better-than-average risks will seek lower prices elsewhere - leaving the insurer with average premiums but riskier-than-average customers (a problem known as "adverse selection"). That doesn't work. Finally, best intentions notwithstanding, Phase 1 fits the legal textbook definition of "unfair discrimination." The law mandates that, subject to "practical limitations," a price is "unfairly discriminatory" if it "fails to reflect with reasonable accuracy the differences in expected losses." In other words, within the confines of what's practical, insurers must charge each person a rate that’s proportionate to the person's risk. Which brings us to Phase 2.

Phase 2:

Phase 2 sees the population divided into subgroups according to their risk profile. This process is data-driven and impartial, yet, as the data are relatively basic, the groupings are relatively crude. Phase 2—broadly speaking—reflects the state of the industry today, and it's far from ideal. Sorting with limited data generates relatively few, large groups—and two big problems. The first is that the groups may serve as proxies that affect protected classes. Take gender. Imagine, if you will, that women are—on average—better risks than men (say the average risk score for a woman is 40, on a 1-100 scale, and is 60 for men). We'd still expect many women to be sub-average risks, and many men to be better than average. So while crude groupings may be statistically sound, Phase 2 might penalize low-risk men by tarring all men with the same brush. The second problem is that—even if the groups don’t represent protected classes—responsible members of the group are still made to pay more (per unit of risk) than their less responsible compatriots. That’s what happens when you impose a uniform rate on a nonuniform group. As we saw, this is the textbook definition of unfair discrimination, which we tolerate as a necessary evil, born of practical limitations' But the practical limitations of yesteryear are crumbling, and there's a four-letter word for a "necessary evil" that is no longer necessary... Which brings us to Phase 3.

Phase 3:

Phase 3 continues where Phase 2 ends: breaking monolithic groups into subgroups. Phase 3 does this on a massive scale, using orders of magnitude more data, which machine learning crunches to produce very complex multivariate risk scores. The upshot is that today's coarse groupings are relentlessly shrunk, until—ultimately—each person is a group of one. A grouping that in Phase 2 might be a proxy for men, and scored as a 60, is now seen as a series of individuals, some with a risk score of 90, others of 30 and so forth. This series still averages a score of 60—but, while that average may be applied to all men in Phase 2, it's applied to none of them in Phase 3. In Phase 3, large groups crumble under the weight of the data and the crushing power of the machine. Insurance remains the business of pooling premiums to pay claims, but now each person contributes to the pool in direct proportion to the risk the person represents—rather than the risk represented by a large group of somewhat similar people. By charging every person the same, per unit of risk, we sidestep the inequity, illegality and moral hazard of charging the careful to pay for the careless, and of grouping people in ways that serve as a proxy for race, gender or religion. It's like we said: Problems that arise while using five factors aren’t multiplied by millions of bits of data—the problems are divided by them. Insurance Can Tame AI It's encouraging to know that Phase 3 has the potential to make insurance fairer, but how can we audit the algorithm to ensure it actually lives up to this promise? There's been some progress toward "explainability" in machine learning, but, without true transparency into that black box, how are we to assess the impartiality of its outputs? By their outcomes. But we must tread gingerly and check our intuitions at the door. It's tempting to say that an algorithm that charges women more than men, or black people more than white people, or Jews more than gentiles is discriminating unfairly. That's the obvious conclusion, the traditional one, and—in Phase 3—it's likely to be the wrong one. Let's say that I am Jewish (I am) and that part of my tradition involves lighting a bunch of candles throughout the year (it does). In our home, we light candles every Friday night and every holiday eve, and we'll burn through about 200 candles over the eight nights of Hanukkah. It would not be surprising if I, and others like me, represented a higher risk of fire than the national average. So, if the AI charges Jews, on average, more than non-Jews for fire insurance, is that unfairly discriminatory? It depends. It would definitely be a problem if being Jewish, per se, resulted in higher premiums whether or not you’re the candle-lighting kind of Jew. Not all Jews are avid candle lighters, and an algorithm that treats all Jews like the "average Jew," would be despicable. That, though, is a Phase 2 problem. A Phase 3 algorithm that identifies people’s proclivity for candle lighting, and charges them more for the risk that this penchant actually represents, is entirely fair. The fact that such a fondness for candles is unevenly distributed in the population, and more highly concentrated among Jews, means that, on average, Jews will pay more. It does not mean that people are charged more for being Jewish. It's hard to overstate the importance of this distinction. All cows have four legs, but not all things with four legs are cows. The upshot is that the mere fact that an algorithm charges Jews—or women, or black people—more on average does not render it unfairly discriminatory. Phase 3 doesn't do averages. In common with Dr. Martin Luther King, we dream of living in a world where we are judged by the content of our character. We want to be assessed as individuals, not by reference to our racial, gender or religious markers. If the AI is treating us all this way, as humans, then it is being fair. If I'm charged more for my candle-lighting habit, that's as it should be, even if the behavior I’m being charged for is disproportionately common among Jews. The AI is responding to my fondness for candles (which is a real risk factor), not to my tribal affiliation (which is not). So if differential pricing isn't proof of unfair pricing, what is? What outcome is the telltale sign of unfair discrimination in Phase 3? Differential loss ratios. The "pure loss ratio" is the ratio of the dollars paid out in claims by the insurance company, to the dollars it collects in premiums. If an insurance company charges all customers a rate proportionate to the risk they pose, this ratio should be constant across their customer base. We'd expect to see fluctuations among individuals, sure, but once we aggregate people into sizable groupings—say by gender, ethnicity or religion—the law of large numbers should kick in, and we should see a consistent loss ratio across such cohorts. If that's the case, that would suggest that even if certain groups—on average—are paying more, these higher rates are fair, because they represent commensurately higher claim payouts. A system is fair—by law—if each of us is paying in direct proportion to the risk we represent. This is what the proposed Uniform Loss Ratio (ULR) test, tests. It puts insurance in the enviable position of being able to keep AI honest with a simple, objective and easily administered test. It is possible, of course, for an insurance company to charge a fair premium but then have a bias when it comes to paying claims. The beauty of the ULR test is that such a bias would be readily exposed. Simply put, if certain groups have a lower loss ratio than the population at large, that would signal that they are being treated unfairly. Their rates are too high, relative to the payout they are receiving. ULR helps us overcome another major concern with AI. Even though machines do not have inherent biases, they can inherit biases. Imagine that the machine finds that people who are arrested are also more likely to be robbed. I have no idea whether this is the case, but it wouldn't be a shocking discovery. Prior run-ins with the police would, in this hypothetical, become a legitimate factor in assessing property-insurance premiums. So far, so objective. The problem arises if some of the arresting officers are themselves biased, leading—for example—to an elevated rate of black people being arrested for no good reason. If that were the case, the rating algorithm would inherit the humans' racial bias: A person wouldn't pay more insurance premiums for being black, per se, but the person would pay more for being arrested—and the likelihood of that happening would be heightened for black people. While my example is hypothetical, the problem is very real. Worried about AI-inherited biases, many people are understandably sounding the retreat. The better response, though, is to sound the advance. You see, machines can overcome the biases that contaminate their training data if they can continuously calibrate their algorithms against unbiased data. In insurance, ULR provides such a true north. Applying the ULR test, the AI would quickly determine that having been arrested isn’t equally predictive of claims across the population. As data accumulate, the "been arrested" group would subdivide, because the AI would detect that for certain people being arrested is less predictive of future claims than it is for others. The algorithm would self-correct, adjusting the weighting of this datum to compensate for human bias. (When a system is accused of bias, the go-to defense runs something like: "But we don't even collect information on gender, race, religion or sexual preference." Such indignation is doubly misplaced. For one, as we've seen, systems can be prejudiced without direct knowledge of these factors. For another, the best way for ULR-calibrated-systems to neutralize bias is to actually know these factors.) Bottom line: Problems that arise while using five factors aren't multiplied by millions of bits of data—the problems are divided by them. The Machines Are Coming. Look Busy. Phase 3 doesn't exist yet, but it's a future we should embrace and prepare for. That requires insurance companies to redesign their customer journey to be entirely digital and reconstitute their systems and processes on an AI substrate. In many jurisdictions, how insurance pricing is regulated also must be rethought. Adopting the ULR test would be a big step forward. In Europe, the regulatory framework could become Phase-3-ready with minor tweaks. In the U.S., filing rates in a simple and static multiplication chart for human review doesn't scale as we move from Phase 2 to 3. At a minimum, regulators should allow these lookup-tables to include a column for a black box "risk factor." The ULR test would ensure these never cause more harm than good, while this additional pricing factor would enable emerging technologies to benefit insurers and insureds alike. Nice to Meet You When we meet someone for the first time, we tend to lump them with others with whom they share surface similarities. It's human nature, and it can be unfair. Once we learn more about that individual, superficial judgments should give way to a merits-based assessment. It's a welcome progression, and it's powered by intelligence and data. What intelligence and data have done for humanity throughout our history, artificial intelligence and big data can start to do for the insurance industry. This is not only increasingly possible as a matter of technology, it is also desirable as a matter of policy. Furthermore, as the change will represent a huge competitive advantage, it is also largely inevitable. Those who fail to embrace the precision underwriting and pricing of Phase 3 will ultimately be adversely selected out of business. Insurance is the business of assessing risks, and pricing policies to match. As no two people are entirely alike, that means treating different people differently. For the first time in history, we’re on the cusp of being able to do precisely that.

We hear almost daily how insurtech is disrupting the once-staid insurance industry. The main ingredients are big data, artificial intelligence, social media, chatbots, the Internet of Things and wearables. The industry is responding to changing markets, technology, legislation and new insurance regulation. I believe insurtech is more collaborative than disruptive. There are many ways insurance technology can streamline and improve current processes with digital transformation. Cognitive computing, a technology that is designed to mimic human intelligence, will have an immense impact. The 2016 IBM Institute for Business Value survey revealed that 90% of outperforming insurers say they believe cognitive technologies will have a big effect on their revenue models. The ability of cognitive technologies, including artificial intelligence, to handle structured and unstructured data in meaningful ways will create entirely new business processes and operations. Already, chatbots like Alegeus’s “Emma,” a virtual assistant that can answer questions about FSAs, HSAs and HRAs, and USAA’s “Nina” are at work helping policyholders. These technologies aim to promote not hamper progress, but strategies for assimilating these new “employees” into operations will be essential to their success. Managing the flood of data is another major challenge. Using all sorts of data in new, creative ways underlies insurtech. Big data is enormous and growing in bulk every day. Wearables, for instance, are providing health insurers with valuable data. Insurers will need to adopt best practices to use data for quoting individual and group policies, setting premiums, reducing fraud and targeting key markets. See also: Has a New Insurtech Theme Emerged?   Innovative ways to use data are already transforming the way carriers are doing business. One example is how blocks of group insurance business are rated. Normally, census data for each employee group must be imported by the insurer to rate and quote, but that’s changing. Now, groups of clients can be blocked together based on shared business factors and then rated and quoted by the experience of the group for more accurate and flexible rating. Cognitive computing can also make big data manageable. Ensuring IT goals link back to business strategy will help keep projects focused. But simply getting started is probably the most important thing. With cognitive computing, systems require time to build their capacity to handle scenarios and situations. In essence, systems will have to evolve through learning to a level of intelligence that will support more complex business functions. Establishing effective data exchange standards also remains a big challenge. Data exchange standards should encompass data aggregation, format and translation and frequency of delivery. Without standards, chaos can develop, and costs can ratchet up. Although there has been traction in the property and casualty industry with ACORD standards, data-exchange standards for group insurance have not become universal. See also: Insurtech’s Approach to the Gig Economy   The future is bright for insurers that place value on innovating with digital technologies and define best practices around their use. It’s no longer a matter of when insurance carriers will begin to use cognitive computing, big data and data standards, but how.

As insurance executives look out for the industry’s next wave, they will see a paradox of great risk and opportunity. The most serious threats — societal megatrends, disruptive technology advancements and intensifying competition from both new and traditional players — also hold the greatest potential for growth and transformation.

As the strategic evolution of the industry accelerates, the most effective response for insurers is to harness the power of change and thoughtfully design their futures. They must develop their vision for the future and adjust their strategic and tactical plans to realize that vision.

Certainly, these recommendations apply to three of the top issues the industry faces — climate change, the rise of new ecosystems and operating models and more inclusive insurance. These are just a few of the trends and scenarios we explore in our recently released report titled, NextWave Insurance: personal lines and small commercial.

Climate change: Climate change is arguably the biggest challenge facing humanity today. For insurers, it also presents an array of new uncertainties that make pricing risk harder than ever. The potential impact of climate change on the insurance sector is staggeringly large. Just consider these numbers:

• $219 billion: combined global insurance losses from natural disasters, 2017–18 (Swiss Re)
• 90%: proportion of natural disaster costs that can be attributed to weather-related events in an average year (Munich Re)
• Five times: total economic losses caused by hurricanes in 2017, relative to the average of the previous 16 years (Aon Benfield)

As storms grow more severe, insurers have a clear opportunity to offer increased protection to families, businesses and communities. Only 30% of catastrophic losses were covered by insurance between 2009 and 2018, according to Aon Benfield. It also estimates that there is a $180 billion global protection gap for weather-related risks.

Of course, insurers must be able to accurately model and price the risk of climate change if they are to collect more premium dollars. They must also understand the potentially detrimental impact of pricing customers out of the market and increasing the underserved community.

As societies around the world come to terms with the implications of global climate change, it’s clear that the insurance industry has a leading role to play in managing risk and offering protection. The earlier that firms grapple with and understand these complex climate-related risks, the more likely they are to derive value from them. Instead of waiting for perfect information, firms should take a flexible approach to this fast-moving topic and embed climate-related considerations into their decision-making.

The rise of ecosystems: Today’s insurance marketplace is hypercompetitive, with extremely tight margins, slow (if any) growth and high operating costs. The industry’s current economics are unsustainable, which means insurers need to rethink their business models.

See also: The Insurance Lead Ecosystem  

Ecosystems, which entail multiple companies partnering to offer specialized, but complementary, services in mutually beneficial ways, are one way for them to enhance the value of their offerings. Ecosystems can take many forms — strategic partnerships, alliances, mergers and acquisitions and joint ventures. The cloud, artificial intelligence and new data sources are key to enabling the development of ecosystems and other new business models.

Early adopters and forward-looking insurers can capture market share by defining their role in the ecosystem relative to other types of entities (e.g., sharing platforms, social media, insurtechs, data providers, customer associations and business services). By connecting with insurtechs, leaders can rapidly add innovative technologies and enhance business processes and customer experiences. Ecosystems and other new operating models will spark innovation and change multiple parts of the business.

Direct, digital and embedded sales will become dominant channels for growth, and ecosystems can help position insurers to capture their fair share of revenue. Subscription models will make insurance more deeply woven into consumers’ everyday lives, clarifying the value insurers deliver.

Ecosystems are one example of how insurers will change both what they deliver and how they deliver it. And the industry appears ready to adopt these models; a full 76% of insurance executives view partnerships and ecosystems as determinants of a future competitive advantage, according to Swiss Re. Small and mid-tier carriers that lack focus and differentiation may find it hard to make the required investments in people and technology, while achieving their financial targets.

More inclusive insurance: Insurers are well-positioned to help protect the many underinsured consumers and businesses around the world. They must find ways to engage younger consumers — so-called “generation rent” — sooner. As these consumers wait longer to purchase vehicles (which they may never do), buy homes, get married and have children, their first interactions with insurers happen later in life.

See also: Opportunities and Risks in the IoT  

Insurers must innovate with technology to engage and support the underinsured and other underserved markets. It’s worth noting how insurers in emerging markets exhibited great creativity in using mobile phones to provide microinsurance, asset-based coverages and embedded insurance purchases in their efforts to connect to the underinsured. These approaches are likely to succeed with the underserved and underinsured segments in mature markets, too. As carriers use greater amounts of information and advanced analytics, they need to be sensitive to pricing customers out of the market.

Seizing opportunity while navigating risk The fundamental question to ask is: Will growth opportunities outweigh the threats in the next wave of insurance? Insurers’ actions and investments in the next five to 10 years will determine if they maximize the upside of these opportunities or struggle with the downside.

The views expressed by the presenters are their own and not necessarily those of Ernst & Young LLP or other members of the global EY organization.

Cardiorespiratory fitness (CRF) is a measure of the body’s ability to supply oxygen to muscles, including the heart, during sustained levels of exercise. Whether you believe the hype that just sitting around poses a significant health risk, the truth is that most people could do with exercising more. An inverse association between CRF and mortality is well-established. A recent study of the long-term mortality of physically active adults found the benefit of increased CRF is independent of age, sex, race/ethnicity and comorbidities. Exercise provides numerous health benefits, including reduction in coronary artery disease, hypertension, diabetes, stroke and cancer. The same study confirms that the greatest chance for survival is associated with the highest aerobic fitness, debunking the notion that exercise benefits plateau quickly or even result in harm. So there really is no excuse for running a bath instead of a mile, or indeed for avoiding any exercise you fancy that increases resting heart rate. This is good news. But as everyone’s level of cardio-fitness is different, the correct dose of exercise needed to confer any real benefit is less obvious. That gap in accessible knowledge is why it’s also good news that there has been such progress with Personalized Activity Intelligence (PAI), a health score that measures cardiorespiratory fitness (CRF). PAI helps add years of healthy life through personalized activity engagement and has been scientifically proven to reduce the risk of cardiovascular disease and early death. PAI provides individual guidance on the most beneficial exercise dose by measuring heart beat data and translating it to a PAI Score. See also: New Efficiencies in Life Insurance   PAI takes account of resting and maximum heart rates adjusted for exercise intensity and collected over a seven-day rolling period to encourage consistent exercise behavior. Any activity that increases the heart rate above a threshold and into the CRF training zone may generate points, meaning people of all fitness levels can score points from activities they enjoy; whether that’s kayaking down rapids, mowing the lawn or running after the grandkids. Physical activity can be measured simplistically but without much insight into the physical workload achieved. PAI, however, measures heart rate and uses an algorithm that calibrates to an individual’s heart effort and is helpful in creating personalized programs for sustained physical activity. PAI has shown the positive impact that sustained physical activity has on heart health and represents a more effective and realistic approach than setting daily step or exercise targets. The guidance indicates when the intensity of exercise does not contribute to increased levels of CRF or when fitter people with higher heart rate reserve (the difference between resting heart rate and maximum heart rate, which is used to calculate the optimal cardiorespiratory fitness level in aerobic exercise) need to challenge themselves more. Life and critical illness products do an amazing job protecting policyholders from financial loss but until now have provided little practical help in safeguarding customers’ health. We believe PAI has the potential to motivate behavioral change, helping policyholders to become more physically active and stick to it, while reducing their risk of disease and premature death. As insurance seeks to shift its emphasis from protection to prevention, this winning formula is possibly some of the best news yet. See also: Intersection of Tech and Holistic Health   Hear more about the science behind PAI from inventor Ulrik Wisløff, professor at NTNU and head of the Cardiac Exercise Research Group, in this short interview: To find out more, contact Ross Campbell.