Lemonade is currently the most talked-about disruptor. That’s why we’re pleased that, for the first time in Europe, Lemonade will present at DIA Munich what the pioneering concept is all about in a keynote presentation. As a special DIA Munich appetizer, we spoke to Lemonade CEO and co-founder Daniel Schreiber recently, exactly one year after the company launched. DIA: Daniel, congratulations on Lemonade's first anniversary. It must have been a roller coaster ride. Thanks for being willing to share some of the experiences and learnings. Did the first year meet your expectations? Daniel: "Yes, it has been quite a ride. But it is great to see that we're striking the right chord. We already sold ten thousands of policies. Our portfolio doubles every 10 weeks." DIA: If you had to name just one thing, what would you say is the key success factor so far? Daniel: "Our renters insurance is 80% cheaper than what competitors offer and takes less than 90 seconds to purchase.” DIA: 80% cheaper is almost unbelievable … Daniel: “Many industry insiders think so, too. [They think that] at least 40% of what insurance carriers receive in premiums is paid out in claims. So if Lemonade is 80% cheaper it must lose money on every policy. That is not true. Renters insurance covers personal property, not real estate. The expected loss is therefore significantly lower and so should the corresponding premium be. Unfortunately, the enormous overheads incumbents have make low-premium products impossible. Their minimum premium reflects their high costs rather than your low claims.” See also: Lemonade’s New Push: Zero Everything   DIA: We can imagine that such a price difference attracts a specific segment … Daniel: “Yes, indeed. We offer a good price, especially at entry levels. No less than 87% of our customers are first-time buyers. Lemonade is the preferred insurance brand among first-time insurance buyers. In the state of New York, where we first launched Lemonade, we now have a market share of 27% among first-time buyers.” DIA: Was this the target segment you planned to focus on initially? Daniel: "Not really, at least not to this extent. This was definitely not planned or expected. It appears our proposition is attracting people who did not think of such an insurance before; because it was too expensive, too much hassle, or because they had little trust in the added value. So it turns out we actually opened up an underserved, untapped market. This was really a surprise for us, as well. It just shows that with really new propositions there is only so much that you can plan." DIA: This suggests the Lemonade concept is about solving frictions that customers experience when dealing with a traditional insurance incumbent. Aren't you selling yourself short here? Daniel: "True. It is not just about solving frictions; being faster, better or cheaper. That wouldn't be sufficient in the long run. When we started conceiving Lemonade, we immediately realized there is no way you can beat insurers at their own game. We needed to think beyond that. We decided to foster trust, not suspicion. Our business model is built on two very distinctive pillars: behavioral economics and artificial intelligence.” DIA: The pillar that is often highlighted is behavioral economics, one of the reasons we like Lemonade so much. Insurers could benefit much more from psychology and social sciences. Daniel: "The vast knowledge and experience of our Chief Behavioral Officer Dan Ariely (professor of psychology and behavioral economics at Duke University) is instrumental in this. We apply behavioral economics to neutralize the adversarial relationship, the conflict of interest, between customers and their insurance provider. We take 20%, and the rest (80%) goes to paying claims, and this includes our reinsurance. If less than the 80% is used to pay out claims, for instance 75%, the 5% unclaimed money is donated to charities chosen by customers. The maximum amount that can be given back is 40%. Lemonade gains nothing by refusing a claim. This way we are reinventing insurance from a necessary evil to a social good." DIA: Can you explain how behavioral economics reflects in Lemonade's daily customer experience? Daniel: "Not just our business model but also the whole product flow is informed by behavioral economics. For example, we ask people to sign on the top of the form, not at the bottom. Behavioral research shows that asking people to pledge honesty first results in forms that are actually more accurate." DIA: How does this affect the combined ratio? Daniel: "Multiple ways. For example, we also apply behavioral economics to reduce fraud. In the onboarding process, customers are asked which charity they want the money that is not used for claims to go to, let's say the Red Cross. Now, when at some point in time a customer files a claim, we first remind the customer of the charity he or she selected before diving into the claim. We do that on purpose. To many people, insurance fraud is considered a victimless crime; you're not really hurting someone, at least that is the perception. Research shows that 24% say it’s okay to pad an insurance claim. We're changing that by immediately creating the presence of a victim. Making it crystal clear that a claim harms a charity someone cares about inhibits misuse." DIA: Do you already have proof points that using behavioral economics this way works at a larger scale? Daniel: "Obviously we're a young company, so the amount of claims that we receive are still limited. But we already have early indications that this really works. In the last two months, we actually had six customers who claimed and got paid, but later on returned the money. Someone, for instance, thought his laptop was stolen, claimed and got paid. A few weeks later, it turned out he had left the laptop with his mother-in-law. He then decided to return the money, probably because he didn't want to harm the charity he selected. I would really love to know how many customers of traditional insurers are returning their money." DIA: Insurers need to manage the feelings side of financial services much better than they do today. Quite a few tend to forget that when they are going digital. Others are building hybrid solutions of, for instance, chatbots and human experts. How do you secure the human side in a pure play such as Lemonade? Daniel: “Behavioral economics is one pillar of our business model; artificial intelligence is the other. Thanks to AI, we don’t have to rely on brokers and paperwork. Underwriting and claims handling are taken care of by AI, as well. This makes it even more important to secure that we are recognized as living, breathing people who really care. My co-founder Shai Wininger has a rare talent to marry technology with customer understanding. Our bot has a name. It talks in an approachable manner. It doesn't say, ‘I don't understand.' We know its limits and anticipate the direction in which the conversation is going. Next-level questions are seamlessly moved to our, human, support staff.” See also: Lemonade: World’s First Live Policy   DIA: We quite often see that traditional insurance carriers have a strong immune system when it comes to embracing insurtechs. Apparently, different cultures are difficult to match. Sometimes we even see organ rejection. We noticed that the Lemonade team not only incudes tech veterans like yourself but also former executives from AIG and ACE. How do you make that work? Daniel: “When we started thinking about a new concept in insurance, we just had a rudimental understanding of insurance. We had the advantage of being ignorant. We had no preconceived notion. This helped us to question the basic principles of the industry, such as the conflict of interest. "Coming from the outside helped us to rethink, reconceptualize in a fundamental way, from scratch, what Lemonade should be about. "Now, it is only so far you can take that. As soon as you move to execution, you really need to have deeply entrenched insurance knowledge on board. Think of the regulatory maze we have to go through. Then it comes to finding the right people, which was not that easy. We soon realized that we were looking for ‘insiders’ who were ‘outsiders’ at the same time. In our recruitment ad, we actually said it was a requirement to be in the throes of a midlife crisis; not feeling happy in the corner office anymore. They had to buy into our vision.” DIA: We noticed that your fast growth in an market segment that is so difficult to reach by incumbents has led companies such as GEICO and Liberty Mutual to use "lemonade" in their marketing and promotion activities … Daniel: “Ha ha, yes, we’ve noticed that as well, of course. GEICO even introduced a ‘lemonade’ TV commercial at the same time as we launched the company. Liberty Mutual, in fact, introduced a new brand, Lulo, and paraphrased everything, from logo to pricing. "We take it as a compliment that such renowned brands are looking at us, and try to learn and use our ideas. But the examples also show that it is not that easy. Lemonade is more than a logo. You really need to understand the two pillars of our model: behavioral economics and artificial intelligence and how that reflects in the way we operate. And you need to understand that we are really a different kind of company. Obviously, we have duties to our customers, employees and investors. But we’re also a B-Corp, which makes us legally committed to social impact. Our customer base is therefore more like a community of people around a cause – which in turn results in more trust and less fraud. It is about aligning customers and insurer, and giving up underwriting profits. We’re rebranding the insurance sector.”

Insurers are under increasing pressure to accelerate innovation to keep pace with new disruptive technologies and changing customer behavior. Cloud computing provides a crucial vehicle for much-needed digital innovation. It underpins all the major digital advances insurers need to adopt to satisfy future customer needs and meet long-term business objectives. Innovations as diverse as digital channels, self-driving cars, wearables and advanced analytics systems all depend on cloud technology. See also: Growing Import of ‘Edge Computing’   However, many insurance executives are holding back from embracing the cloud. Around 85% of the executives we canvassed in our 2016 Technology Vision for Insurance survey, for example, agreed that the cloud would foster innovation in their businesses that was not previously possible. Yet, only 49% were investing in comprehensive digital technology programs as part of their business strategies – moves essential to capitalize on the potential of the cloud. Often insurers are stalling a shift to the cloud because of common misconceptions about this powerful technology environment. Some of the reasons they give for their reluctance are:

• Lifting and shifting applications to the cloud does not work.
• Sunk costs are unrecoverable.
• Digital transformation can happen without cloud.

These beliefs are, for the most part, fallacies. Let’s look at the first fallacy: that lift-and-shift doesn’t work. In fact, we’ve already helped some insurers realize 80% savings using the lift-and-shift approach to cloud application migration. In the highly competitive banking sector, Capital One demonstrated the cost-cutting potential of switching to the cloud by migrating most of its critical workload to Amazon Web Services (AWS). The U.S. banking group expects this shift to help it more than halve the number of data centers it requires. In addition to cost savings, moving to a more agile, cloud-based environment provides insurers with the flexibility and speed-to-market that traditional infrastructure cannot match. That’s important, given that more than half the insurance executives we canvassed claim current technology processes are impeding their business objectives. Incremental change to traditional technologies will not solve that problem. A utility-based cloud model is the best way for insurers to improve their flexibility. Furthermore, our research shows that more than two-thirds of insurance executives believe that replacing their legacy technology with something new would be too costly. Yet more than 10% of carriers use no legacy technology at all. While the savings achieved by replacing legacy with cloud-native applications are typically hard to match, we see equivalent gains in some lift-and-shift scenarios, such as migrating development and test environments to cloud workloads. See also: ‘Core in the Cloud’ Reaches Tipping Point   In my next post, I’ll address the common fallacy that sunk costs cannot be recovered if companies move to the cloud. In the meantime, have a look at this link. I think you’ll find it useful. Eighty percent reduction in insurance carrier costs? Cloud as rainmaker. This article was written by Daniel Presutti.

Putting the injured worker first is key to the “advocacy-based claims model,” which puts the worker at the center of all activity. Until now, most employers have focused on corporate outcomes, with goals such as cutting costs and reducing days lost. But experts now say focusing on the worker can improve all outcomes. The stories of three injured workers provide an opportunity to see the importance of focusing on the injured worker. Take Melanie. Melanie was working as a lifeguard for the summer at a recreational center. She slipped on some wet pavement that had been targeted for cleanup but not yet addressed. She fell on one knee, sustaining a shattered patella and a deep laceration. Melanie needed immediate surgery for the laceration. She initially was put into a brace to stabilize her patella but was told by a different provider that she didn’t need the brace. She recalls feeling like there was a lot of uncertainty around how to treat her shattered patella. One provider told her he could wire it together with surgery; another said surgery would be a mistake. Ultimately, after a delay of almost a year, during which she was unable to work, she was referred to physical therapy. Melanie found the physical therapy helpful in enabling her finally to go back to work. However, she was left with a great deal of concern about the future of her knee and the possibility of late-onset complications. She was also upset about the long delay before she could start physical therapy. Because of this uncertainty, and to ensure that her knee would be taken care of regardless of what happened to it in the future, Melanie retained an attorney to help her obtain lifetime medical benefits for her knee. Her claim is still open. See also: Perspectives From Injured Workers Amy’s story has a bit of a different take. Amy was working as an administrative assistant for an apartment complex. A large number of boxes were delivered to the office and were stacked up against her desk, such that she couldn’t leave her desk area without climbing over the boxes. She asked her manager to move the boxes several times, but they were not moved. At one point, while climbing over the boxes, she fell and injured a knee. Amy reported the injury to her supervisor, who wanted her to go immediately to a hospital emergency room. Amy did not want to wait in an emergency room and successfully argued in favor of seeing an orthopedist the next day. The orthopedist obtained an MRI of her knee that showed both old, long-standing damage and newer areas of injury consistent with the fall Amy had just sustained. However, the day after her injury, Amy’s employer fired her. The payer in Amy’s case tried to deny the claim based on the older damage in Amy’s knee. Amy did not deny that she had damage from years of being a dancer, but she felt it was the newer damage that was limiting her mobility, as she had previously been able to exercise and now had too much pain in the damaged knee. She simply wanted the payer to pay for a few sessions of physical therapy, but the claim denial coupled with being fired left her with no resources to pay for physical therapy. Amy found the claims adjuster hostile and inflexible. She felt betrayed by both her former employer and the payer for her claim. She retained an attorney to fight the claim denial. Eventually, the claim was accepted, and Amy was given several sessions of physical therapy. By the time this came to pass, Amy was suffering pain in the other knee because of having to favor the injured knee. What could have been resolved within a limited time had morphed into a time-lost claim with the need for extended physical therapy. Last, there is Arthur. Arthur was employed as a consultant for a nationally known consulting firm. One day, he was carrying some boxes filled with reports when he dropped a pen and tried to pick it up without dropping the boxes. As he twisted his body to try to pick up the pen, he felt a “pop” in his back and fell to the floor in pain. After a minute or two, he was able to get up without assistance, but the pain in his back remained. Arthur was knowledgeable about the workers’ comp system and decided to file a claim. His pain was persistent and intrusive, but Arthur was still able to work, so his was a “medical-only” claim. Arthur told the claims adjuster that he wanted to see his own physician, with whom he already had a relationship. He alluded to wanting “to avoid retaining an attorney if at all possible.” After an MRI, Arthur’s physician told him that he now had a protruding disk that he was going to have to deal with for the rest of his life. Arthur’s physician gave him the evidence-based statistics about the success rate of surgery for a protruding disk, which were not good, and recommended that he avoid surgery and deal with his problem with stretching and physical therapy. Arthur told the claims adjuster that he preferred to have physical therapy over surgery. The claims adjuster not only approved the physical therapy but also, unsolicited, ordered a special desk chair for Arthur to use in his home office. Arthur felt well taken care of, and after six months of following the regimen his physical therapist had designed, Arthur’s back pain resolved, and his claim was closed. He had not needed an attorney and was extremely satisfied with how his claim had been handled. What can these three journeys tell us about how to put the injured worker first? Here are five best practices:

1. Ensure the injured worker is educated about the claims process and what to expect. One of the most common reasons why injured workers retain an attorney is because they are worried about whether their claim will be accepted and their bills paid. Reducing uncertainty and fear on the part of the injured worker improves his or her engagement in the treatment process and reduces the attorney involvement rate, which improves quality of care and reduces cost.
2. Use technology to facilitate the injured worker’s interactions with other stakeholders, from the initial reporting of the injury (think mobile app, direct self-reporting of injuries) to selection of the right physician and finally to the collection of feedback from the injured worker on treatment. Technology is a powerful tool to provide high-quality, personalized, yet cost-effective service. However, it needs to be backed up by strong operational processes.
3. Encourage injured workers to plan on returning to work right from the beginning. This means helping them select the right physicians proven to have good outcomes and encouraging them to be partners with their treating physicians in choosing wisely among treatment options. For example, there are several physicians who are waging an admirable fight against the opioid epidemic and pushing for more holistic pain-management techniques. We need to send more injured workers to them instead of physicians who have been proven to encourage opioid use.
4. Trust the injured worker to want to return to work as soon as possible. Most injured workers honestly want to recover, not to game the system. We need to go with that assumption upfront. A good marriage of Math+Trust can help reduce attorney involvement in claims from 13% to 4%.
5. Stay in close contact with the injured worker; keep the lines of communication open. Use this opportunity to determine if there are any treatment delays that can be mitigated or any questionable treatments that are being recommended. Expectations on communication speed have increased in this constantly connected world. We need to be as close to them as possible throughout their journey to recovery.

Gently guiding the injured worker to the best possible course of treatment will optimize outcomes, improve injured worker satisfaction and minimize costs. See also: 3 Reasons to Talk With Injured Workers   The primary purpose of the workers’ compensation system is to get injured workers back on track rapidly. As an industry, it’s time we realigned ourselves toward that goal. Using the “injured worker comes first” principle as a true north improves claim outcomes, lowers costs and improves workplace productivity. No longer do you need to make false trade-offs between cost and quality, cost and speed, etc. Just focus on getting the worker back on his or her feet fast. All operational metrics will follow. As first published in Claims Journal.

Listen to the pundits and self-described experts, and you get the impression that artificial intelligence is taking over the world. Our cars will drive themselves, our buildings will optimize their energy, accidents will be avoided and we will be afforded every manner of convenience. Everything around us will be smart – looking out for our best interests, automating everything, providing new services and generally making life wonderful. Many emerging technologies will have to come together to realize this vision, but perhaps artificial intelligence (in all its various forms) is the lynchpin. This all sounds fantastic – and creates some great opportunities for the insurance industry to help policyholders reduce risks and improve their health and well-being. But dig a little deeper and you’ll discover a paradox – we will be expected to turn over the controls to AI-based machines while recognizing the fact that the AI in use, at least today, can be unreliable. Everyone has their own favorite examples of tech gone awry, and these examples are not meant to tar all AI-based systems with the same brush. But let me offer a few examples from everyday life to illustrate that. while many actually work quite well, there are enough that stumble to cause more careful consideration to how AI should be used for critical applications. See also: Underwriting Lessons From the PGA   AI-Based Meeting Schedulers Interacting with bots via email to quickly schedule meetings can lead to frustration. What seems like a simple request that any person would understand is often misinterpreted by the AI-based schedulers. I’ve seen many instances where emails were sent back and forth multiple times, each with increasing layers of confusion, just to get the right people on the right call at the right time. Voice Assistants Voice communications are poised to become the major way that humans interact with computing devices. Tremendous progress has been made in the accuracy of speech recognition and natural language processing. While the progress has been terrific and the accuracy rates are now approaching that of human understanding of speech, there are still enough errors that we should be cautious. To illustrate this point, I provide the following humorous examples. As humorist Dave Barry is fond of saying, “I am not making this up.” What I said in a post about autonomous vehicles: “… as more personal vehicles are embedded with AI…” How Siri translated it: “…as more personal vehicles are in bed with a guy…” Another prime example is the word "insurtech." I would not expect this to be translated correctly at first, but after correcting Siri hundreds of times, I still get "and shirt tech," or "ensure text" or my personal favorite: “I’m sure Texas.” I do find that my Amazon Echo device, Alexa, is generally good at interpreting a request, although one of the most common responses I get is: “Hmmm, I don’t know that.” With my car navigation system, I have given up trying to voice dial my wife, Deanna, and certain other individuals because the names are never recognized, and I sometimes trigger a call to someone in Asia! These are light examples of how a small error in interpretation can significantly alter the original meaning. In the examples I’ve provided, the errors were harmless and easily fixed. But what does this mean for the vision of the connected, intelligent, autonomous world? And what does it mean for insurers? See also: Seriously? Artificial Intelligence?   I believe the power and potential of AI is tremendous and will yield astounding benefits for the world. We will be able to dramatically reduce vehicle accidents and reduce or avoid machine breakdowns and property damage. We will be able to assist the elderly and disabled to live independently, improve personal health and extend lifespans. All of this is possible and will be enabled by AI, working in concert with other emerging technologies like the IoT, robotics, wearables and others. But we do need to be circumspect regarding this AI-fueled future. Asking the status of a flight or requesting a song is one thing … but controlling high-value machinery, healthcare devices or moving vehicles is quite another. Insurers should monitor the progress of AI, pilot and experiment with various types of AI technologies and consider the possible positive and negative implications of a broader usage of AI. The real questions are about timing and whether businesses and individuals will have the restraint and governance to wisely use AI-based solutions for the mission-critical and life-critical uses that are being contemplated.

Recently, I was invited to present on the impact of big data and advanced analytics on the insurance industry at the NCSL Legislative Summit. This talk couldn’t have been timelier, as the insurance sector now tops the list of most disrupted sectors. Some of the culprits and causes for this top spot are related to the speed of technological change, changing customer behavior, increased investments in the insurtech sector and new market entrants, such as homeowners and renters insurance startup Lemonade. A significant driver of this disruption is technological change – especially in big data and advanced analytics. See also: Why to Refocus on Data and Analytics   Here are 10 key trends that are affecting big data and advanced analytics – most of which have a hand in disrupting the insurance industry:

1. Size and scope – Big data is getting bigger and faster. With connected cars, homes and buildings, and machines, the amount of data is increasing exponentially. Investments in IoT and Industrial IoT, 5G and other related areas will only increase the speed and amount of data. With this increased volume and velocity, we will not be able to generate meaningful insights from all of this data without advanced analytics and artificial intelligence.
2. Big data technology – Big data technology is moving from Hadoop to streaming architectures to hybrid “translytical” databases. While concepts like “data lakes” and NoSQL databases mature, new technologies like Apache Spark, Tez, Storm, BigTop and REEF, among others, are creating a constant flow of new tools, which adds to a sense of “big data in flux.”
3. Democratization – The democratization of data, business intelligence and data science is accelerating. Essentially, this means that anybody in a given organization with the right permissions can use any dataset, slice and dice the data, run analysis and create reports with very little help from IT or data scientists. This creates expectations for timely delivery, and business analysts can no longer hide behind IT timelines and potential delays.
4. Open source movement – The open source revolution in data, code and citizen data scientist is accelerating access to data and generation of insights. Open source tools are maturing and finding their way into commercial vendor solutions, and the pace of open source tool creation is continuing unabated; the Apache Software Foundation lists more than 350 current open source initiatives. This steady stream requires data engineers and data scientists to constantly evaluate tools and discover new ways of data engineering and data science.
5. Ubiquitous intelligence – Advanced analytics – especially various types of artificial intelligence areas (reference to my AI report post) – is evolving and becoming ubiquitous intelligence. AI can now interact with us through natural language, speak to us, hear us, see the world and even feel objects. As a result, it will start seamlessly weaving itself into many of our day-to-day activities, such as using a search engine or sorting our email, recommending things to buy based on our preferences and needs, seeing the world and guiding us through our interaction with other people and things without our even being aware of its doing so. This will further heighten our sense of disruption and constant change.
6. Deep learning – Deep learning, a subset of the machine learning family (which itself is just one area of AI), has been improving in speed, scale, accuracy, sophistication and the scope of problems it addresses. Unlike previous techniques, which were specific to the different type of data (e.g., text, audio, image), deep learning techniques have been applied across all different types of data. This has contributed to reduced development time and greater sharing and broadened the scope of innovation and disruption.
7. MLaaS – Machine learning, cloud computing and open source movement are converging to create Machine Learning as a Service (MLaaS). This not only decreases the overall variable costs of using AI but also provides large volumes of data that the machine learning systems can further exploit to improve their accuracy, resulting in a virtuous cycle.
8. Funding – Big data funding peaked in 2015. However, funding for artificial intelligence, especially machine learning and deep learning, has continued to attract increasingly significant investments. In the first half of this year, more than $3.6 billion has been invested in AI and machine learning. This increased funding has attracted great talent to explore difficult areas of AI that will be disruptors of the future economy.
9. Center of Excellence: As organizations continue to obtain good ROI from their initial pilots and proof-of-concepts in analytics, automation and AI efforts, they are increasingly looking toward setting up centers of excellence where they can train, nurture and grow the talent. The exact role of the center changes based on the overall organizational culture and how the rest of their business operates – centralized, federated or decentralized.
10. Competitive landscape – The big data landscape continues to grow, and the AI landscape is expanding rapidly. Deep learning companies are growing the fastest across multiple sectors. Competition among startups – as well as incumbents that want to stay ahead of potential disruption – is creating a vibrant ecosystem of partnerships and mergers and acquisitions that further the disruptive cycle.

See also: Analytics and Survival in the Data Age   Are there other trends you would add to the list? Share them here!

What is your most pressing employee health issue today? It’s not cholesterol, weight, sitting or probably anything else you are prioritizing. Instead, by far the major health menace facing your employee population is the opioid epidemic — which, according to Harvard Medical School psychiatrist John Kelly, has reached “DEFCON 5.” DEFCON 5 is right. There is roughly one opioids prescription written for every adult in the U.S., and the total addiction rate is estimated at 4.6%, which makes it higher than alcoholism and roughly comparable (in the employed population) to diabetes. Here are five things you need to know:

1. Opioid abuse has jumped 500% in the last seven years.
2. The price per milligram of morphine-equivalent paid by employees has declined about 75% in the last 15 years. This is due to more generous coverage (by you!), more use of the formulary and, most distressingly, more pills per prescription. There is virtually no product whose use doesn’t increase as the price falls. And there are very few products whose price falls that much.
3. The $78 billion all-in cost in the U.S. of opioid use, abuse and treatment works out to about $756 per employee per year. To put that in perspective, that’s about 10 times what you spend on heart attacks and diabetes events (not that those aren't important, too!).
4. Workers' compensation claims costs are 10 times higher when long-acting opioids are involved.
5. Your ER visit claims coded to opioid issues have probably increased threefold since 2003.

(Yes, we know, that is only five facts. and we promised six. Keep reading...) How do you solve an opioid problem within your organization? You can’t look to your wellness vendor to solve this problem. If biometric screens included drug-testing, the employees who need to submit to them wouldn’t. (The legality of the testing would be very questionable anyway.) Asking a health risk assessment question: “Are you addicted to painkillers or heroin?” would generate — at best — the same level of candor wellness vendors observe when they ask about drinking and smoking. You can’t address an addiction that an addict won’t admit to having in the first place. However, a health literacy vendor – ideally, my firm, Quizzify – can raise awareness of the hazards of opioids in your employee population. Because health literacy quizzes don’t require personal health information, there is no opportunity to lie, no one is being singled out and no one needs to worry that the results aren’t confidential. It’s simply, purely education. The answers are pure facts. (And in our case have passed review by doctors at Harvard Medical School.) See also: The True Face of Opioid Addiction   For employees not already using pain meds: Firstly, employees who are not currently using prescription painkillers need to be made aware of the risks of starting. If there is one health literacy risk worthy of attention — meaning one risk where curing a knowledge deficit (as opposed to trying to change behavior, as with smoking cessation or eating habits) matters — it’s in opioid addiction prevention. A few facts:

• It can take as little as three days of use before the first signs of addiction occur. To put this into perspective, even something as minor as prophylactic wisdom teeth removal (not generally recommended by Quizzify anyway) can generate three days of painkiller medication.
• If you use a 10-day supply as directed, you have a 20% risk of becoming a long-term user.
• Dose matters. A lot. A high dose for a short duration is 40 times as likely to cause an opioid use disorder as a low dose.
• Employees’ kids are taking prescription pain meds in numbers far exceeding those of previous generations. This is because they believe them to be safer than street drugs and are easier to get hold of (often from the parents’ medicine cabinets).

For employees already using pain meds: As mentioned, the percentage of employees using pain meds, 4.6% on average, is roughly the same as the percentage with diabetes. The cost of treating those on pain meds – and their productivity losses (not to mention the possibility to pilferage or other crimes to support the habit) – is much higher than diabetes. Further, employees are unlikely to seek help on their own. Use of medications designed to treat opioid addiction has grown only about a fifth as fast as opioid use itself. And many employees either don’t know where to turn or are concerned that their EAP conversations are not confidential. Fear of job loss or having a criminal record also impede the likelihood of seeking help. Your health literacy vendor should be able to create the education for you to overcome these natural impediments. Quizzify's opioid abuse education includes:

• Specific contact information for the EAP.
• “What if I think a coworker is opioid-dependent?”
• “Are there resources for family members?”
• “Can I get or renew pain meds from the on-site clinic?”
• “Is opioid treatment a covered benefit?”
• “Will human resources find out I am getting opioid treatment?”
• "What are signs that my children are abusing painkillers?"

What can you do to help? Your budget allocation for health and wellness should be in proportion to the priorities for health and wellness. As of now, you are likely spending less on educating employees on opioids (not to mention on other health literacy imperatives) than on, for example, weighing employees. Likewise, employees are probably spending more time figuring out how to cheat on their weigh-ins than on understanding the hazards of opioid use. It’s time to reconfigure these priorities. Teach employees how to avoid, manage and treat opioid addiction before it is too late. See also: Opioids: Invading the Workplace   And by “too late” we mean #6 of the facts you need to know: Far exceeding diabetes and heart attacks, overdoses are the leading cause of death for employees under 50. We invite you to take Quizzify's Opioids Awareness Quiz and share it with the top executives, HR administrators and wellness champions within your organization. Your awareness that something needs to be done now will increase. Quizzify offers educational quizzes about opioids for employees. That might be a good place to start.

As InsureTech Connect gets underway this week, here is our latest look at Six Innovators to Watch. (Previous honorees can be found here.) We hope that you find this month's six, and the many others showcased at InsureTech Connect, thought-provoking and perhaps even inspirational as we try to drive innovation in insurance and risk management.

Agent Ave

Agent Ave, based in St. Paul, MN, wants to help independent insurance agents manage the data associated with all the insurance companies they represent. From underwriting appetite, to application forms to company contacts, each insurer has different rules and regulations for an agent to manage—and, for an independent agent, managing all this information for multiple carriers can be overwhelming. The Agent Ave platform allows the agency to organize, coordinate and communicate this information via a single accessible tool, making agents more efficient, productive and able to serve their customers more quickly. Learn more about Agent Ave at https://www.itlinnovatorsedge.com/companies/agent-ave

Clear-Cut Medical

Clear-Cut Medical is a medtech company based in Israel that has developed a portable MRI technology that promises significant savings of time and cost on cancer surgeries. The company’s ClearSight system is designed to provide a surgeon with effective margin assessment—making sure all cancer was caught by ensuring a margin of healthy tissue around excised tumors—in real time within the operating room, helping reduce the occurrence of second surgeries to remove cancerous tissue that was not caught. The savings and improved outcomes from avoiding another surgery can be significant, to the benefit of the payer, which typically is the insurance industry. The ClearSight system is available in Europe and is undergoing clinical trials in the U.S., with a goal of FDA approval in 2018. Learn more about Clear-Cut Medical at https://www.itlinnovatorsedge.com/companies/clear-cut-medical-ltd

DataCubes

DataCubes has a goal of using big data technology to enable insurers to underwrite a small commercial insurance risk with just the name and address of a company. The Chicago-area company’s data science uses that information to access relevant data to provide a more comprehensive risk profile of a business, thus allowing the insurer to underwrite and price coverage with precision, making coverage application faster while reducing manual entry of diverse data and ultimately lowering costs. DataCubes technology can be deployed as a customer-facing form—such as an agency portal or direct-to-consumer web site—or can be integrated with a carrier’s or MGA’s systems. Learn more about DataCubes at https://www.itlinnovatorsedge.com/companies/datacubes-inc 

Digital Fineprint

Digital Fineprint turns social media data into intelligence for insurance underwriting and customer engagement. One of its first applications was using social sign-ons to complete online forms for insurance coverage applications. The company has expanded to use data science to analyze social media information to provide insights to a consumer seeking to evaluate and better understand insurance needs, as well as generating a risk profile for insurers to underwrite a variety of coverages. London-based Digital Fineprint is working with several insurers around the world, including life/health and property/casualty companies. Learn more about Digital Fineprint at https://www.itlinnovatorsedge.com/companies/digital-fineprint-331 

MotionsCloud

MotionsCloud wants to reduce the time it takes to settle insurance claims, addressing a pain point for both insurers and policyholders. Using a mobile application and artificial intelligence, MotionsCloud allows a policyholder to capture information about a claim, deliver information to insurers and even use a smartphone to provide a live video inspection and chat. In the background, the A.I. algorithms are analyzing the claim against historical claims and repair data and giving a human adjuster the information needed to quickly sign off on a claim—and even make payment via the app. MotionsCloud, based in Germany with an office in Des Moines, Iowa, hopes that, by reducing the time insurers spend evaluating and processing routine claims, they can boost customer satisfaction, lower costs and deploy their resources on more complex claims. Learn more about MotionsCloud at https://www.itlinnovatorsedge.com/companies/motionscloud 

Safeguard Guaranty

Safeguard has developed an insurance product, the Marriage Assurance policy, that is designed to not only provide a long-term benefit for policyholders who stay married but also provide a payout in case of divorce, death, or terminal illness. The Durham, N.C.-based company identified a lack of insurance coverage solutions against the financial losses that result from a divorce, especially among women. The Marriage Assurance product is designed to be bought by individual parties in a marriage to cover either themselves or their spouse, by a parent or grandparent or even by a business partnership concerned about protecting assets in case of a partner’s divorce. Safeguard is planning to either underwrite the coverage itself or partner with an established insurer to bring it to market. Learn more about Safeguard Guaranty at https://www.itlinnovatorsedge.com/companies/safeguard-guaranty-corporation

I hope I don't come across as ignoring the fact that InsureTech Connect is being held in Las Vegas, where some evil idiot killed dozens and wounded hundreds more Sunday night while they minded their own business, enjoying an open-air concert. I just don't know what to say. I'd love to think that, in the spirit of innovation in risk managment (not to mention humanity), we could at least have a conversation about how to preserve Second Amendment rights while reducing the number of mass shootings in the U.S. (They average almost one a day.) But past massacres haven't led to a serious conversation about how to reduce gun violence, so I despair that this one somehow will, either. 

Cheers,

Paul Carroll,
Editor-in-Chief

One of the amazing things about where we are in the arc of data changing our lives is that analytic models are pervasive. They are changing our professional lives, for sure, but I was also reminded recently that models can be used in all areas of our lives. Why? Because, golf! As I watched the professional golf Tour Championship, I thought about how analytic models recently helped me to cash in on predictive golf data. For the British Open golf tournament in July, the golf club where I play ran a Pick 5 pool. They divide the field into the Top 5 players and A, B, C and D groups of players. You pick one player from each group, and the handful of people who pick the best-performing groups of five players win some credits in the pro shop. I could have simply made my picks based on research, gut feel for the players and a little knowledge of the game. Instead, in a surprise to nobody, I opted to pick using a big data approach. CBS Sports created a simulation of all the golfers in the field playing the course for the event 10,000 times. They used the current statistics for each player, mapped how those statistics would help or hurt the player on the specific course for the event and then ranked the projected scores for the golfers. I made my picks based on their results. I won the pool for the British Open using this approach. The golfers that the CBS Sports model had as the lowest scorers for each of the groups created the best pick of about 150 picks from my club mates. Where is the win in insurance data? My experience has a corollary meaning for insurance. There is money to be made (and saved) in insurance data modeling by understanding where underwriting is heading with the power of analytics. While we look at what is changing in underwriting, we’ll also look at its impact on insurance profitability, examining three areas in particular:

• Improving the pool of risk
• Deeper analysis and new data sources that will drive product innovation
• Artificial Intelligence (AI) and predictive analytics

Improving the pool of risk Let’s start with the basics and define the pool. Our pool contains insureds (the breadth of the pool) and their data (the depth of the pool). It would be nice, as underwriters, to pick only pools of winners, but criteria that strict would give us pools that are too small to generate premiums, and underwriters would frequently “lose” because their best picks would disappoint them. This is the first lesson from the golf simulation’s success: I didn’t use it to pick the winner of the tournament. I used the model to pick a portfolio of golfers who should have performed better than the others in their group. I actually didn’t have the winner of the tournament in my group. As with putting together a baseball team, picking stocks for a mutual fund or filling any occupation where the performance of a group matters, that we need to build a healthy pool of risk is a “no-brainer.” Actually doing it, however, is more difficult than simply looking at a few key factors. It requires expert data analysis (some of it automated). It requires excellent visibility (into the pool of risk). And, it requires continual monitoring and tweaking (possibly with some assistance from AI and cognitive computing). See also: The Next Step in Underwriting   The basic idea, in summary, is that we need a complete knowledge of the full pool and a better visibility into the life of the individual applicant. Underwriters are trying to create a balanced portfolio. They don’t need to pick a perfect risk, but they need to know who is positioned to outperform their peers.  By figuring out how to identify those above-expectation performers, they are able to skew their portfolio risk lower and out-perform the odds and the market. Deeper analysis, new data sources and “smarter” pools will prepare insurers for product innovation. The second lesson from the golf simulation was this: Every piece of data that is available should be made available in the decision process. In Majesco’s recent report: Winning in a New Age of Insurance: Insurance Moneyball, we look at how outdated analytic techniques can hide strategic opportunities. The risk to insurers is that up-and-comers will evaluate and price risk with more sources of data and more relevant data. Traditional underwriting characteristics will give you “A”, “B” and “C” risks (as well as those you’ll reject), but you won’t see within the peer group to see where there’s value in writing business. Traditional underwriting also assumes that factors don’t change on the applicant once they have entered the pool. And it treats everyone in the pool equally (same premiums, same terms) with the same expected outcomes. But what if pools were built with the ability to tap into more granular data and to adapt forecasts based on current conditions and possible trends? Like looking at a golfer’s ability to play on a wet course, what if we could see how a number of new factors including both personal and global data will affect outcomes?  For example, what if commercial insurers could see how small changes in investor sentiment early in a cycle drive expensive, D&O-covered, class action lawsuits three years (two renewals) later? Look at life insurance. When your company initially accepted Ron as an applicant, it placed him into the A pool. At the time, you only collected MIB data, credit data and some personal data. Since then, you’ve started giving small discounts to the same pool when given access to wearable data and social media data, and you have started collecting Rx reports. In running some simulations, you are realizing that a combination of factors can give you a much better picture of possible outcomes with the new data sources, such as Amazon purchase data or wearable data. What if you set out to improve predictive analytics within the pool by re-analyzing the pool under newer criteria? Perhaps you offer to give wearables at a discount to insureds or free health check-ups to at-risk members of the pool. It could be any kind of data, but the key is continuous pool analysis. Preparation’s bonus: Product agility and on-demand underwriting Every bit of work that goes into analyzing new data sources has a doubly valuable incentive: preparation for next-generation product development. Once we have our data sources in place and our analytics models prepared, we can grasp the real value in the source, creating some redundancy and fluidity to the process. So, if a data source goes away or is temporarily unavailable or it becomes tainted (imagine more Experian breaches), it could be removed without consequence. This new thinking will help insurers prepare for on-demand products that will need, not just on-demand underwriting, but on-demand rating and pricing. As we noted in our thought leadership report, Future Trends 2017: The Shift Gains Momentum, we showed how the sharing economy is giving rise to new product needs and new business models that are using real-time, on-demand data to create innovative products that don’t fit under the constraints of current underwriting practices. P&C insurers, for example, are experimenting with products that can be turned on and off for different coverages … like auto insurance for shared drivers like Uber or Lyft. And this is just the start of the on-demand world. Insurance is available when and where it is needed and priced based on location, duration and circumstances of need. If an insurer has removed the rigidity of its data collection and added real depth to data alternatives, it will be able to approach these markets with greater ease. At Majesco, we help insurers employ data and analytic strategies that will provide agility in the use of data streams. Real-time underwriting will become instant/continuous underwriting. Analytics will be used more to prevent claims than to predict them. Which brings us to the role of artificial intelligence in underwriting. See also: Data Opportunities in Underwriting   AI and predictive analytics Simulations have been in use for decades, but, with artificial intelligence and cognitive computing, simulations and learning systems will become underwriting’s greatest asset. Underwriters who have seen hundreds and thousands of applications can pick out outlying factors that have an impact on claims experience. This is good, and certainly it should continue, but perhaps a better form for picking the winners would be for applications to run through simulations first. Let cognitive computing have the opportunity to pick out the outlying factors and allow predictive analytics to weigh applications and opportunities for protection. (For more information on how AI will affect insurance, be sure to read Majesco’s Future Trends 2017: The Shift Gains Momentum). Machine learning will improve actuarial models, bringing even more consistency to underwriting and greater automation potential to higher and higher policy values. And it will also allow for “creativity” and rapid testing of new products. Can we adapt a factor and re-run the simulation? Can we dial up or dial down the importance of a factor? Majesco is currently working with IBM to integrate AI/cognitive into the next generation of underwriting and data analysis. Perfection is unattainable. But if we aim for the best process we can produce, we can certainly use new sources of data and new methods of analysis to improve our game and take home a higher share of the winnings. How do I know this? Well, the golf club ran a pool for the PGA Championship the month after the British Open. I didn’t win that pool. Out of more than 200 entries — I came in second. Cha-ching!

You just never know. Wednesday, Jan. 16, 1991. I was on a flight to the West Coast when Desert Storm started. The pilot came on and told us about President Bush’s speech. He asked us to pray for our solders in harm’s way and for our country. Tuesday, Sept. 11, 2001. I was at a conference in Disney World when a trickle of news reports quickly turned into the media tsunami that forever changed the trajectory of our culture. We gathered in the hotel ballroom to address questions as a group. Over the next couple of days, I had customers and friends melt in my arms, overcome with grief. We comforted one another as we struggled to try and make sense of the terrorist attacks, making arrangements to get people home, renting cars, vans and buses. Friday, July 20, 2012. I was driving to a speaking engagement when I received a panicked call about the shooting in Aurora, CO, where our son and his wife live. They were safe, but he had to report to the scene immediately because some airmen in his charge were in the theater. See also: Time to Mandate Flood Insurance?   Sunday, Sept. 10, 2017. Hurricane Irma cut a wide swath of damage and flooding through central Florida, where we live. Our normally quiet small town is still abuzz with electrical and phone crews feverishly working to restore normal operations, making permanent repairs. Many homes in our area are a patch quilt of blue tarps. FEMA contractors are still removing debris as a convoy of trucks and equipment rumble through neighborhoods. Monday, Oct. 2, 2017. Today, I’m in Las Vegas only to be awakened to the horrific news that we know all too well. I’ve received numerous messages over the entire spectrum of electronic communications, asking about our safety. In these and other events, we will want to learn as much as possible. We want to know the who and struggle with the why. Much will be uncovered over the next hours and days. There are so many open questions waiting to be answered. There is so much that we don’t know. But there is one thing that I know for certain, and I say this in all seriousness and respect. Insurance will play a vital role in the coming days, weeks and months, helping to rebuild lives, families and businesses devastated by this heartbreaking and senseless tragedy. Working in insurance since 1972, I've been humbled over and over again to be part of an industry that helps people. While my career has been on the technology side of the business, there is a quiet assurance, knowing that what we do will help restore lives. At the tender age of 19, I had my first “data processing” interview. It was for a junior terminal programmer trainee position at a large insurance company that no longer exists, paying an exorbitant $7,500 a year. After the interview, I walked to the bus stop and wondered about working for an insurance company. I replayed all the jabs and jokes that we know all too well in my mind that surround the insurance industry. Was I somehow going to be tainted by being a part of a profession that had a reputation equal to that of gas station attendants (true statistic)? See also: Harvey: First Big Test for Insurtech  There have been opportunities to leave the insurance industry over the years. But I kept coming back to the reality that there are precious few professions that can have such a direct, positive effect on the lives of so many as insurance. Yes, we have our problems and detractors. Yes, we can sometimes be our own worst enemy when it comes to public perception. Yes, we could do a better job at communicating to and servicing our customers and the public as a whole. But I count it a personal honor and privilege to serve in the insurance industry. I hope you do also.