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How Is Marine the Heart of Insurtech?

As with many parts of insurtech, the underlying driver is the move from pure risk transfer to risk mitigation, and from prevention to prediction.

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Who would have thought marine insurance would be at the center of the insurtech revolution? The relationship between insurtech and marine insurance is not an obvious one for many people.

Marine is one of the oldest and most traditional classes of business, the origins of Lloyds of London, when from 1686 members of the shipping industry congregated in the coffee house of Edward Lloyd to arrange early forms of marine insurance.

However, two recent announcements firmly place marine in the center of the technology revolution affecting insurance.

First, Maersk announced they are building a blockchain-based marine insurance platform with EY, Guardtime, Microsoft and several insurance partners. Second, a U.K.-based technology company, called Concirrus, announced the launch of the first AI-powered marine insurance analytics platform.

At Eos, this was not surprising.

See also: Insurance Needs a New Vocabulary  

In the first half of 2017, as part of our thesis-driven investment approach, we highlighted commercial insurance as a key area of focus and within that our first product vertical to focus on was marine insurance. What led us to this conclusion?

Commercial marine insurance is a $30 billion premium market, it’s complex and fragmented, and through our analysis we identified a significant potential shift in profit pools over the next few years. Importantly, the emergence of IoT and other devices has created a wealth of data within the industry. Marine also sits at the heart of global supply chain logistics.

During our deep dive into the sector and having spoken with more than 40 market participants across various parts of the value chain, it became apparent that marine insurers (and shippers) have never had so much data (internal and external) available to them, and many don’t have the tools or skill set to take advantage of it.

Growing competition, underwriting capacity and downward pressure on pricing has given little room to maneuver, but we were intrigued and kept digging.

The ability to gather and analyze these new information sources is helpful, but more important will be driving actionable insights through well-informed decision making based on high-quality, real-time data and analytics to improve risk selection, pricing and claim management while helping the insured better manage risk. As with many parts of insurtech, the underlying driver is the move from pure risk transfer to risk mitigation, and from prevention to prediction.

The creation of marine analytics solution platforms provide tailored insights to users, which is an important first step. Currently, software and tech providers to the marine industry are fragmented, with no dominant vendors and no joined up, end-to-end solutions.

As the market matures, the ability to harness analytics capability at the front end with improved efficiency at the back end through blockchain or other initiatives creates an even more compelling story and is an area we will be watching with interest.


Sam Evans

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Sam Evans

Sam Evans is founder and general partner of Eos Venture Partners. Evans founded Eos in 2016. Prior to that, he was head of KPMG’s Global Deal Advisory Business for Insurance. He has lived in Sydney, Hong Kong, Zurich and London, working with the world’s largest insurers and reinsurers.

5 Best Practices on Injured Workers

Using the “injured worker comes first” principle as a true north improves claim outcomes, costs and workplace productivity.

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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.

Laura Gardner

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Laura Gardner

Laura B. Gardner is chief scientist and vice president, products, CLARA analytics. She is an expert in analyzing U.S. health and workers’ compensation data with a focus on predictive modeling, outcomes assessment, design of triage and provider evaluation software applications, program evaluation and health policy research.

The AI Paradox: A Troubling Implication

We will be expected to turn over the controls to AI-based machines while recognizing that the AI in use, at least today, can be unreliable.

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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.

Mark Breading

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Mark Breading

Mark Breading is a partner at Strategy Meets Action, a Resource Pro company that helps insurers develop and validate their IT strategies and plans, better understand how their investments measure up in today's highly competitive environment and gain clarity on solution options and vendor selection.

10 Trends on Big Data, Advanced Analytics

Big data is getting bigger and faster. We will not be able to generate meaningful insights without advanced analytics and artificial intelligence.

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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!

Anand Rao

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Anand Rao

Anand Rao is a principal in PwC’s advisory practice. He leads the insurance analytics practice, is the innovation lead for the U.S. firm’s analytics group and is the co-lead for the Global Project Blue, Future of Insurance research. Before joining PwC, Rao was with Mitchell Madison Group in London.

6 Shocking Facts on Opioid Abuse

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. 

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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.

Six Innovators to Watch - September 2017

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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


Paul Carroll

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Paul Carroll

Paul Carroll is the editor-in-chief of Insurance Thought Leadership.

He is also co-author of A Brief History of a Perfect Future: Inventing the Future We Can Proudly Leave Our Kids by 2050 and Billion Dollar Lessons: What You Can Learn From the Most Inexcusable Business Failures of the Last 25 Years and the author of a best-seller on IBM, published in 1993.

Carroll spent 17 years at the Wall Street Journal as an editor and reporter; he was nominated twice for the Pulitzer Prize. He later was a finalist for a National Magazine Award.

Underwriting Lessons From the PGA

Outdated analytic techniques can hide strategic opportunities, and up-and-comers will use more sources and more relevant data.

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!

John Johansen

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John Johansen

John Johansen is a senior vice president at Majesco. He leads the company's data strategy and business intelligence consulting practice areas. Johansen consults to the insurance industry on the effective use of advanced analytics, data warehousing, business intelligence and strategic application architectures.

A Reflection on the Las Vegas Slaughter

The tragedy is senseless and irreparable, but on days like this I'm proud I chose a profession that will help restore so many shattered lives.

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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.

Chet Gladkowski

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Chet Gladkowski

Chet Gladkowski is an adviser for GoKnown.com which delivers next-generation distributed ledger technology with E2EE and flash-trading speeds to all internet-enabled devices, including smartphones, vehicles and IoT.

Insurance CROs: Shifting to Offense

A survey finds insurers' chief risk officers engaged on high-priority strategic and business-driven issues.

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EY’s seventh annual survey of chief risk officers in the insurance industry confirms that companies are starting to move on from the post-crisis era of defensive risk management. While some CROs speak of works in progress or continuing improvements to their company’s risk management efforts, more CROs report they are comfortable with functioning frameworks that provide “defense” for the company. There is continued maturation and increasing sophistication of the role. Some CROs are spending more of their time engaged on high-priority strategic and business-driven issues, such as disruption, innovation and emerging threats, including cybersecurity. See also: The State of Risk Oversight in 2017   CROs are starting to move to offense. They see their roles less in terms of organizational compliance with enterprise risk management (ERM) policies. Nor are they reacting to regulatory requirements. For almost all companies surveyed, Own Risk Solvency Assessments (ORSA) are “job done.” Even CROs at companies that faced challenges related to federal regulation or Solvency II report that such issues are largely behind them. Many of this year’s discussions involved consideration of “what comes next?” As the CRO agenda evolves, significant transitions are underway (see figure 1):
  • From relative stability to disruption
  • From clear and well-understood threats to emerging and unknown risks
  • From serving as a control function to partnering with the business
  • From focusing on the risks of action to promoting innovation and avoiding the risk of inaction
See also: Key Misunderstanding on Risk Management   Where CROs mostly played defense in focusing on compliance and regulatory activities after the crisis, many have started to move on to a more active, business-driven posture, with greater emphasis on adding value through the efficient delivery of ERM. You can find the full EY report here.

20 Likely Changes in Ethics on Claims

Insurance is changing in ways that have profound implications for claims. Questions only occasionally raised before will now become common.

Insurance is changing in ways that have profound implications for claims. Some claims practices will become redundant. Questions only occasionally raised before will now become common. New skills will have to be learned. It’s all very exciting, but also a little daunting. Clearly, the way we think about claims will change, but, at the same time, certain constants will remain: settling claims honestly and fairly, for example. So what are the changes that have implications for the ethics of insurance claims? I want to look at 20 changes that I think will be significant in terms of the ethical challenges facing claims people. The "Ask It Never" Policy As insurers turn from asking questions of the policyholder about the risk to be insured and instead obtain that information through big data, the time of "no questions at all" will approach. What will happen to claims then? If no questions are asked, then non-disclosure becomes obsolete, as does the whole idea of material facts. What will be left for the claims team to review or decide upon? The Personalized Policy A personalized policy will, by its very nature, mean that a claim made upon it will result in an increase in premium. As the public comes to increasingly sense this, how will it influence the way in which claimants approach their claim? Should claims people warn potential claimants that their claim will result in an increased premium? Some claimants will self-fund small, valid claims, although those spending patterns will then be picked up by insurers, which could move the premium anyway. Claims may well become more confrontational, as policyholders sold on the idea of personalization find the consequences unpalatable. What can claims people do to maintain trust in such circumstances? See also: Most Controversial Claims Innovation   Optimizing Claims Decisions The trend toward claims settlements being optimized according to what a claimant may be prepared to accept in settlement fundamentally changes key concepts in insurance. What would be a fair claims settlement in such circumstances? And how would "fair" be determined, and by whom? Claims optimization pushes the claims specialist to the margins, although not out of the process altogether, for optimized settlements will raise questions. Someone may be hard up, but not stupid: They will want to know the basis upon which the settlement they’ve been offered has been calculated, and claims people will have to do the explaining. Correlation and Causation Insurers are using big data to make decisions about individual claims and claimants. Yet big data analysis relies on identifying significant correlated patterns of loss, while individual claims rely on identifying the causation of a loss. That difference is important, for correlation and causation are not the same. You can’t replace a "one to one" technique like causation with a "one to many" technique like correlation. It would be akin to saying that because your claim is like all those others (which were turned down), then we’re going to turn down your claim, too. Hardly a recipe for fairness. So as the tools of artificial intelligence are increasingly applied to claims processes, the extent to which the decisions being made remain fair will have to be closely monitored, both in terms of inputs and outcomes. How will this be done? Reasonable Expectations As data streams all around us (both policyholder and insurer), our ability to understand more about what is happening around us increases. This raises the question of the extent to which a claimant could have reasonable been expected to have been aware of something. If big data knows something, should individual policyholders be expected to know it too? How will insurers start to judge whether a claimant took sufficient notice of something that subsequently influenced the claim? The Sensor Balance As homes, offices and factories become covered in sensors, telling you all sorts of information about the property that you were only vaguely aware of before, so then will increase the number of decisions you’ll be called upon to make. There could be some maintenance required to your roof or drains, and unless it’s done soon, then your insurance could be affected. Or perhaps some machinery has been running for longer than usual, in order to meet some new orders, but the sensors are telling you to shut it down for servicing. That knowledge is being recorded and stored, along with the decisions you take in relation to it. All ready then for your insurer to tap into, should there be a claim. Insurers will now have the information to apply those traditional policy clauses relating to maintenance with a new vigor. How will this play out? The 3 Second Repudiation The 3 second claims settlement made news for Lemonade, but so will a 3 second claims repudiation. After all, giving people what they want as quickly as possible is a quite different experience to giving people what they don’t want as quickly as possible. How will such repudiation situations be managed, and how might claimants react to an almost instant dismissal of their claim? A Smart Contract Just for You Big data, smart contracts and personalized policies that ask no questions of the policyholder all point to a level of individualization that will baffle the typical claimant. A loss covered last time might not be covered next time. A neighbor’s loss may be covered in a quite different way to yours. How do you explain such situations to a claimant who’s knowledge of ‘insurtech’ is zero? If everything is so variable, then might communication turn out to be the claims person’s key skill? The Automation of Fairness As claims processes become increasingly automated, insurers will have to take care not to lose sight of their obligations in terms of the fairness of the decisions being made. Some insurers struggle with this even in today’s relatively straightforward workflow processes, so how they will cope with something like artificial intelligence is a concern. Experience points to this being harder as systems become more complex. A lot will depend on the extent to which those in oversight roles bring challenge and critical thinking to the implementation of such projects. Transparency As claims processes become increasingly automated, should the claimant have the right to be told about this? There’s talk of news written by artificial intelligence ‘bots’ soon having to be flagged as ‘artificial news’. Might the same soon apply to individual decisions on things like claims? If so, then from a European perspective, a claimant’s ‘right to know’ might soon become a more complicated request to fulfill. Upholding Supplier Standards The consensus is that a typical claim function’s supply chain network will continue to grow for some time. And bringing in all of these exciting and new capabilities is fine, so long as everyone is singing the same tune. Insurers have to abide by the ethics of insurance claims, such as covered in rules for fairness, honesty and integrity. So how can a claims director convince her board of fellow directors that their firm’s ethical obligations are being met every bit as confidently as in more analogue times? Has her due diligence taken account of not just the intelligence and energy of those providers of artificial intelligence solutions, but their integrity as well? It’s a challenge best met earlier on. Instantaneous Claims That breed of policies described as ‘mobile, micro and moment’ are all about instant cover for just what you want, when you want it, for as long as you want it, and arranged with a few clicks on your phone. Turn those conveniences around and you have the potential for the instantaneous claim, perhaps only moments after inception – “I bought cover for a bike, got on it, went outside and crashed it”. Such claims have usually been looked upon with suspicion by claims people, on the basis that such a quick loss could not be fortuitous. Yet if you provide cover in this way, why shouldn’t some claims happen in much the same way? This is a change of mindset needed throughout an organization, not just in underwriting. Managing Complexity As more cogs, and more complicated cogs, are added to the overall claims process, the greater the challenge it becomes for them to deliver on the promises that were made at the planning stage. This is an existing problem for claims people in the UK, who have acknowledged that the multiplying layers of many claims systems aren’t delivering the expected results. The answer will not come from artificial intelligence working it out for itself: all AI needs to be trained on historical data. So claims people need to understand complexity and how to manage it. Challenging the Decision Research by one leading insurer in the UK market found that policyholders are less likely to trust an automated decision than one involving a human. So as claims become more automated, insurers could face an increasing number of challenges from individual claimants, asking how the decision on their claim was reached. How will they explain an output from an increasingly ‘black box’ process? They may be tempted to rely on generalized responses, but that isn’t going to work when the claimant appeals to an adjudication service like the UK’s Financial Ombudsman Service (FOS). Organisations like FOS should be working now on how they can get inside that automation and assess the fairness of the outcomes it has been designed to produce. Will they perhaps look to accredit the overall automation, or rely on case by case use of techniques like fairness data mining. Another factor that insurers need to take into account will be claimants turning to the EU’s General Data Protection Regulation and enforcing their right to access the data upon which the decision on their claim was made. Insurers will need to prepare for this, both in terms of the volume of such requests and the complexity of responding to them. Again, the ability of claims people to communicate complex things will become a key skill. Provenance of Data As insurers bring more and more data into their claims processes, especially unstructured data drawn from sources like social media, they will need to be prepared to demonstrate the provenance of that data. In other words, they need to be able answer questions like “where did you get that piece of data from that seems to have been a big influence on my claims decision?” Or “that piece of data is wrong so you need to change your decision.” If you utilize data outside of the context in which it was first disclosed, then the error rate shoots up. Just because a piece of data resides within a system doesn’t establish it as a fact. Significance in Algorithms Pulling all sorts of data together is one thing, but the value that claims people draw from all that data comes from the algorithms that weigh up its significance. At what levels the various measures of significance are set will be hugely important for the outcomes that claimants experience. These introduce options that require judgements and such judgements need to overtly account of ethical values like fairness and respect. See also: How AI Will Transform Insurance Claims   Segmentation of Claimants As claims processes become more automated, so claims people are presented with the opportunity to segment the experience of the various claimant types they engage with. Many insurers currently use software to assess claimants at the ‘first notification of loss’ stage and vary the type of experience they receive. At the moment, this is being used to address claims fraud, but it is unlikely to end there. Artificial intelligence coupled with audio and text analysis will allow insurers to segment non-fraud claimants for all sorts of purposes. The challenge for claims people is just how acceptable some of those purposes might be. For example, what if claimants are segmented according to the amount they are prepared to accept as a claims settlement? All of these new technology platforms introduce options, but just because you have the option to do something doesn’t mean that it’s a good thing. Warnings Ahead New ways of communicating with policyholders offer up the possibility of advance warnings being given of storms, floods and the like. That brings many benefits to both insurer and policyholder, but it also raises the prospect of those warnings having conditions attached. Rather than advice, they could include requirements linked to continuation of certain elements of cover. If the policyholder doesn’t (for whatever reason) respond to those communications, this then introduces possible conflict zones for subsequent claims. The Convenience of Clicking The ease with which cover can be incepted using mobile devices is a great convenience to policyholders at the outset of a policy, but it could turn into a great inconvenience when making a claim. Research shows that we invariably do not read the terms and conditions presented to us when buying a mobile based product or service: it’s just too easy to click accept, especially when the fine print looks even finer on a small screen. So claims people need to be prepared for many more people than at present not knowing about the cover they’ve signed up to, beyond what is indicated by a few well designed icons on a screen. The Language of Claims A subtle change of language has emerged in claims circles in recent years. The service element of what’s on offer is being stressed more than the insurance element. While it’s great to see insurers now paying attention to risk management in their personal lines portfolios, this shouldn’t be at the cost of what is at the heart of an insurance product, which is risk transfer. The danger is that this slow and subtle change will not be picked up by customers until they find out when trying to claim that what they’ve bought is largely a service and not insurance. To conclude. It’s a great time to be in insurance, and I would say even more so in respect of claims, for that is where all the promises inherent in the insurance purchase are fulfilled. Those who recognize the ethics of insurance claims and rise to the challenges outlined above will be those who are trusted in the digital market.

Duncan Minty

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Duncan Minty

Duncan Minty is an independent ethics consultant with a particular interest in the insurance sector. Minty is a chartered insurance practitioner and the author of ethics courses and guidance papers for the Chartered Insurance Institute.