A handful of tech firms ensure the smooth operation of millions of businesses world-wide. A great many companies may not even realize that they rely on these critical web-service suppliers, but when the technology goes down, business may grind to a halt. To compound the problem, some of these background web companies depend on the services of yet others, which creates a complex web of potential contagion. For cyber insurers and their reinsurers, the contingent business interruption (CBI) aggregation risk is enormous.

The companies are the cloud service providers (CSPs) and content delivery networks (CDNs) that make the internet work. They support billions of dollars of commerce and services every day. AWS (Amazon Web Services) is the leading CSP, with about 40% market share and more than one million customers. More than 95% of the Fortune 500 rely on Azure, Microsoft's CSP, at least to a certain extent. Cloudflare is used by about 150,000 clients. Google Cloud Services sits behind Shopify, the e-commerce platform relied upon by about 800,000 merchants in the U.S. alone.

These, along with other CSPs and other service providers, form the backbone of the technology and infrastructure that allows the internet - and therefore the modern economy - to work. When they're operational, they ensure that e-commerce, from online banking to pizza delivery orders, functions effortlessly and almost instantly. Unfortunately, they go down with alarming frequency. Major vendors that suffered at least one outage this year include AWS, Fiserv, Shopify, Azure, Cloudflare, Google Cloud, IBM and Verizon. One service interruption happened because a cable was inadvertently cut. Another occurred when the air conditioning shut down.

See also: Essential Steps for Cyber Insurance

Traditional business interruption (BI) insurance covers losses arising when something the insured does or suffers causes a systems problem that brings normal business to halt. Contingent BI, also known as Dependent BI, is a subclass that protects insureds when something goes wrong at a third-party service provider and their shutdown causes the insured's business to stop in its tracks.

It's a complicated risk to assess at the best of times, but it's made fiendishly more difficult when the third parties are CSPs and CDNs. Worse, because an outage at one of the big players can affect hundreds or thousands of insured firms, the potential aggregation - especially for reinsurers - is gigantic.

Cyber insurers' reactions to the threat naturally vary. Some have lengthened the time the outage must last before coverage kicks in, effectively increasing the self-insured retention. Others have imposed low sub-limits that cap the indemnity payable to a fixed maximum that may be much lower than the insured's actual loss. The third option is to exclude CBI cover for CSPs and CDNs. The fourth and most extreme reaction is to remove DBI coverage altogether.

The widespread reluctance to cover cloud outages and distribution network interruptions is understandable. It is very difficult to gain a clear vision of all clients' true exposure to specific services. It is even more difficult to garner a granular view of the nature of the exposure; the insured can sometimes name their service provider but often don't know the specific service provided or the regional sub-service that delivers it.

Historical data about the services consumed is typically very limited or absent, which leaves insurers unable to model individual risks, let alone the threat of aggregation. And, because the risk lies with third parties, it is impossible to differentiate among insureds based on their systems architecture, infrastructure or controls. As a result, accumulations of exposures, particularly around market-dominating service providers, cannot easily be managed effectively.

There are several strategies to tame these challenges. Foremost is understanding: Downtime policies should cover specific, named services. Secondly, each risk should be underwritten individually. This is a necessity, because not all risks are insurable. Some service providers' reliability is not up to par, and sometimes an insurer must manage its own accumulation. Insureds presenting a higher accumulation risk may face higher premiums or longer downtimes before coverage is triggered. These measures allow downtime insurers to limit accumulation risk. That goes both ways, though, because customers using service suppliers outside those that present the largest accumulations pay less and benefit from shorter self-insured interruptions.

These accumulation management measures are important, but the heart of downtime insurance should be cloud monitoring. Downtime insurers should watch the CSPs and CDNs constantly, in real time, to see their performance and detect dependencies and know about clients' interruptions as soon as they happen. That monitoring allows downtime insurers to offer Cyber CBI insurance products on a parametric basis. When the cloud or network used by a specific customer goes down long enough to trigger a claim, the downtime insurer should tell them. It's simple and efficient and helps the world get back to business as usual with as little disruption as possible.

The theme from last week's InsureTech Connect in Las Vegas crystallized for me when an insurtech executive, sipping on a glass of wine, told me he thought bartenders would make the best insurance agents. 

Think about it for a second. Even leaving aside that people tend to let their guards down after they've had a couple of drinks, bartenders know a lot about the wants and needs of their regular customers. Bartenders also have lots of leeway to offer advice. It tends to be along the lines of "Dump the guy" or "Don't dump the guy," or "Quit the job" or "Don't quit the job," but why couldn't the advice go further? 

Imagine a bartender saying, "You know, a lot of people these days don't carry any personal life insurance or don't carry enough to support their families if they get run over by a truck. Life insurance for somebody your age is a lot cheaper than you think...."

While the idea of bartender-agents is fanciful, the notion brought home to me what I saw as the theme of ITC: embedded insurance. 

That theme showed up throughout the conference, where almost every executive I met with talked about how their company was building application programming interfaces (APIs) to connect seamlessly with partners. Many executives cited examples of embedding insurance offerings in the sales of other products -- offering insurance for an engagement ring while the couple is still at the jeweler's, offering specialized auto insurance that can be purchased with just four taps on a phone when a driver signs on with a ride-sharing company in the U.K., etc. An executive from Credit Karma told me about how the company is using its relationships with its more than 100 million members in the U.S. to engage them about insurance.

While I was at the conference, Next Insurance and Intuit underscored the embedded theme by announcing that small businesses and accountants would be able to buy numerous insurance products without ever leaving the QuickBooks ecosystem, in which they spend so much time thinking about financial considerations. This follows Next's announcement with Amazon a year ago, where Amazon is offering product liability insurance to the massive number of small businesses that sell products through it. (I continue to think that relationship could spread and have a major effect on insurance, as I wrote here.)

The recent examples build on others that have drawn attention in the past couple of years, such as the ability to sell renter's insurance as part of the process of renting the apartment, the increased availability of warranties when someone buys a phone or other expensive device and, of course, the continued success of Exhibit A for embedded insurance: travel insurance. 

It seems to me that one of the shibboleths of the insurance industry is being turned on its head. We've all been told that insurance is sold, not bought. Increasingly, though, insurance will be bought, not sold -- at least if companies can position themselves in the middle of the purchase of something that triggers a thought about the need for insurance.

Cheers,

Paul

P.S. If you'd like to read more about embedded insurance, I'd recommend these articles that we've published over the past year or so:

Embedded Insurance: The Hot New Topic

Embedded Insurance Reaches Tipping Point

The Recipe for Embedded Insurance

Embedded Insurance -- Both Old and New

There’s not a company on the planet that isn’t generating and collecting data in some way. Today alone, the world will generate trillions upon trillions of bits of information. But there’s a problem with gathering all that data: It’s way too much for companies and their chief data officers (CDOs) to handle — and way too much for people to comprehend.

Much of what’s collected is called dark data, information that’s collected but never used. In our experience, dark data makes up most of the information companies collect. Unstructured data — videos, images, emails and other points that can’t be inserted into a spreadsheet — falls into this category, but businesses also collect a lot of information that for one reason or another never gets analyzed.

What’s exciting is that while a lot of valuable data is considered dark, when brought to light it can reveal real insights into the wants and needs of your organization, including around your customers and employees, the quality of your assets and manufacturing and the risks your brand faces on social media.

More dark data ahead

CDOs need to get a handle on their dark data now, as their companies are gathering increasing amounts of information every day. Businesses are putting data-collecting sensors on pretty much everything — industrial equipment, agricultural fields, office building walls and people’s wrists. They’re using drones to measure climate impacts and snap pictures of assets, while artificial intelligence (AI) technology is finding data points in minutes rather than the days, weeks or years it used to take to uncover.

While companies don’t need to analyze every number or statistic that comes their way, they also shouldn’t continue keeping all this data in the dark.

A lot of this is potentially valuable information that can be used to increase productivity and boost growth. And not knowing what’s there can pose significant risks.

Say a utility has sensors on its equipment to detect gas leaks, and a safety incident occurs. Now suppose that there was information hidden in the dark data those sensors collected that could have been used to predict and prevent the incident, but the utility never looked at that data. Having the data available but unused could bring lawsuits or a public relations nightmare.

Or take a bank dealing with a fraud case. An analysis of its dark data might have revealed red flags that would have prevented the breach. Worse, regulators — who now have a much better understanding of the data companies collect — might be unimpressed with a bank defending itself with, “How could we have known that was coming?” Regulators expect businesses to use all the tools they have to prevent fraud and generally crack down if they see something that a company missed.

How can companies make their data more visible? Here are some ideas:

Get a handle on all your information

• Know what’s being gathered and where it’s being stored.
• Then decide whether it’s useful to the business or not.

Understand your risks

• Start by identifying the risks in your business that require you to take action to address. Risks could include becoming more compliant with certain industry regulations, fixing holes in your cybersecurity defenses or better understanding when equipment might break down.
• Once you know the risks and what actions you must take to fix them, start identifying what kind of dark data will help. 

Use AI tools to parse data

• Use artificial intelligence tools that can identify patterns in photos, detect irregularities in sensor data and uncover other hard-to-find insights. A lot of data, whether it’s images, weather patterns or vibrations, can’t be interpreted by the average person. The volume of information will be overwhelming, and humans won’t be able to understand all of the anomalies in the information.

Get excited about data

• Understand how data can elevate business performance. Business leaders should take a keen interest in all kinds of data, not just what they can see in front of them. 
• Create data lakes where structured and unstructured information gets stored. 

The more data that your company can analyze, the better decisions you’ll make. While it’s important to not get overloaded with information — keep going back to your business objectives and the risks you’re accounting for to determine what to observe — you should still be aware of what you’re collecting.

As companies become even more data-focused, and as new tools emerge to help people analyze their unstructured information, it’s only a matter of time before your dark data gets out in the open. The companies that use their data to their advantage can be the ones that get ahead.

This article first appeared on PwC's website here.

Flood is the most frequently occurring effect of climate change globally, and its severity is only forecast to get worse. According to the UN Intergovernmental Panel on Climate Change (IPCC), “climate change could increase the annual cost of flooding in the U.K. almost 15-fold by the 2080s under high-emission scenarios.”

More than 50 severe flood events around the world caused combined economic losses of $82 billion in 2021, while insured losses stood at slightly more than $20 billion, according to Swiss Re Institute’s sigma report. Clearly, insurers and those who live in areas liable to flooding must take the threat of climate change seriously. Not only are the losses eye-watering, but flood also has an impact across the entire environmental, social and governance (ESG) spectrum. 

With ESG dominating boardroom agendas due to pressure from shareholders, regulators, customers, social activists and even firms’ own employees, it’s worth looking at the impact of flood through the ESG lens. 

E (Environmental) impact

In the U.K. storms of 2020, the average commercial property flood claim was £70,000. In the vast majority of cases, claims are processed using new for old, which is anti-sustainable. 

That’s not all. Many businesses also face another environmental challenge if flooded. Manufacturers, retailers, utilities, distribution centers, agricultural suppliers and others use chemicals and produce high volumes of waste, which can contaminate the flood water. Flood water can carry hazardous pollutants considerable distances, causing serious environmental damage over a wide area. 

Accurate flood warnings and real-time flood alerts can reduce this potential environmental damage. Businesses and households have time to turn off gas and electricity and move crucial items out of danger, reducing the need to replace them. 

The ability to move waste and chemicals out of harm’s way alone would reduce the potential environmental impact of the flood hugely. Businesses with flood resilience measures in place can also protect their buildings, heavy machinery and other immovable items. 

See also: 10 Insurtech Trends at the Crossroads

S (Social) impact

Around 40% of businesses never reopen after a flood. The social impact of this is huge. When businesses close, unemployment increases. In small towns, losing 40% of local shops and cafes will reduce the number of shoppers. And, of course, this has the knock-on effect of reducing the income of local councils due to fewer businesses paying rates, fewer visitors paying for parking and so on. 

The more an area is prone to risk, the less likely it is to get back on its feet. After all, what local business person would take the risk of opening a new café in a location where the last one closed due to flooding? The less income the council makes, the less money it has available to invest in attempts to revive the area. 

What’s potentially even worse is that experiencing extreme weather damage can increase the chances of suffering stress or depression by 50%, according to the Environment Agency. 

The good news is that businesses that invest in flood resilience measures and use flood warnings to prepare flood plans can see a significant impact. 

Mary Dhonau OBE is a flood risk consultant and former CEO of the National Flood Forum. She specializes in raising local flood awareness by planning for flooding and advising on property flood resilience measures. She interviewed various business in Hebden Bridge that had suffered multiple floods in the past five years. She found that businesses there had put resilience measures in place and used flood warnings and flood plans. 

As a result, after their most recent flood the local businesses had reduced their losses by over 90% and were able to reopen in three days rather than four months – as had been the case for most of them prior to putting flood measures in place. This is an example of where an entire local community has been able to minimize the social impact of flooding by being better-prepared. 

G (Governance) impact

Insurers and banks are now subject to stress tests that assess their vulnerability to flooding, while boards and investors require that governance around climate change risk is taken seriously. 

Flooding can affect large organizations in various ways, including by creating major stock shortages and raising prices, having to pay fines due to the environmental impact of the flood or even hospitals having to close, endangering lives. 

This is where flood warnings can again have a crucial impact for a large organization’s ability to create effective flood plans as part of their good governance. 

Take the example of BT, the U.K. telecom company. Flooding to BT properties could result in major outages of telephone and internet services that people rely on. Alleviating this threat is a focus area for the company:

“More accurate flood warnings can help us to combat climate change and support our ambitious sustainability targets,” said Jim Dempsey, BT’s director of service.

Zurich Insurance Group is working with BT and Previsico on piloting more accurate flood warnings and has created a unit, Zurich Resilience Solutions, dedicated to “managing complex risks by applying our extensive risk engineer expertise,” according to Gabrielle Durisch, head of sustainability for commercial insurance and group underwriting at Zurich.

See also: Lemonade: No Sign of Disruption Yet

Viewing flood through the ESG lens 

These major insurers are treating flood resilience as part of their governance responsibilities, realizing that ESG is a helpful framework for better understanding the potential impact of flood. Flooding has a clear environmental, social and governance impact, and it’s beneficial to see it in those terms. 

Doing so helps insurers and their clients to look at flood resilience in a more holistic way, thereby gaining a better understanding of the importance of flood warnings, flood plans and flood resilience to better protect lives and livelihoods and reduce losses for insurers.

Technology is indispensable to the insurance industry. It can make once-antiquated systems and processes usable for the modern consumer while also introducing efficiencies throughout the insurance lifecycle for carriers. Customers can get quotes by simply clicking a button. Carriers can manage coverage through apps on their mobile devices. Insurers looking for an edge over competitors should embrace the emerging tech trends offered in the industry to simplify tasks for customers and within their organizations. 

However, executing a digital transformation can be intimidating. The goal is to equip employees and customers with the tools to smooth the application process. A digital adoption platform (DAP) will work hand-in-hand with organizations to make the insurance process easier for everyone, from customers to agents and underwriters. However, if those users struggle to learn how the insurtech works, the benefits of deploying the tool and using it correctly will be lost.

The insurance application process is one of the most significant trouble spots we hear about from customers. It is often lengthy and confusing for customers, agents and underwriters alike. Using technology to simplify the process at each step with solutions that help explain the process and catch and correct errors before they happen is an ideal way to increase productivity and improve applications' success rate. This process can also cut costs, helping companies stay ahead of the competition by meeting customer expectations.

See also: Insurtech: Still No Sign of Disruption

Benefits for Customers

Customer expectations have changed drastically in recent years. They value personalization and seek simplicity, and they also expect to be able to manage their insurance needs 24/7 without leaving their homes. Insurtech allows insurers to meet those demands of customers. Enabling customers to serve themselves has resulted in many benefits, including lowering customer acquisition costs, increasing customer retention and generating more referrals. 

Online customer portals and mobile apps allow customers to independently handle most of their needs. However, there are still many customers who will need help through the application process. AI chatbots are becoming the primary way to guide customers through tricky processes. It's estimated that, by 2025, 95% of all customer interactions will be powered by chatbots. A chatbot, programmed correctly, can replace a live agent, guiding customers and executing the policy and claims process on its own. It's like having an expert over their shoulder helping them throughout the application.  

Similarly, a DAP can help deliver customers' personalization and simplicity. A DAP will help customers with the creation of a live walkthrough. These walkthroughs are step-by-step guides that help customers through trouble spots with "nudges." For instance, if a customer is struggling with entering the information needed for the application, a pop-up will not only ask if they need help but guide them in the right direction to get back on track. A DAP will also minimize the errors during the application process through AI automated data input. Crucially, it does this within the flow of work, offering contextual guidance without disrupting the user's attention to the task at hand.  

Eliminating Margin of Error for Agents and Underwriters

Deploying AI and machine learning will transform legacy insurance processes for agents and underwriters to make those processes more efficient and improve accuracy. AI-enabled insurtech can improve and even automate claims processing, all but eliminating the margin of error. Because AI-driven technologies reduce the incidence of human error among users, they eliminate costly mistakes that can harm your company's profits and reputation. A recent McKinsey study predicted that AI-powered automation will replace more than half of claims activities by 2030. AI enables insurers to process massive amounts of consumer data, creating personalized customer experiences.

Deploying a DAP on top of those technologies makes agents' and underwriters' jobs even more manageable by helping with guided workflows. They remind agents what tasks to perform at each step of the policy process, allowing them to avoid missing a step. They cut processing time along with the number of customer agent interactions, granting more time for agents and underwriters to work on other tasks. 

Underwriters have a lot to handle. They must optimize their workflows across company manuals, industry regulations and customer data while maintaining their interaction with agents. The work starts as soon as an application is submitted and doesn't end until a policy is issued. Underwriting software will improve the process by increasing efficiency. A DAP will act as a force multiplier by reducing errors and performing repetitive tasks through AI rather than taking up an underwriter's valuable time.

Companies can enjoy the benefits of AI-powered insurtech by fully embracing its potential. This technology will streamline the application process by giving customers the power to manage their own needs while driving better efficiency on the carrier end by supplying troves of customer data. It will simplify the process and reduce the time for agents and underwriters with automation that will catch costly mistakes before they happen. Finding the right DAP breaks the daunting task of digital transformation into manageable pieces while helping everyone use the tech relevant to their job in the most efficient way.

As a key component of electric vehicles (EVs) or electronic devices, the transport of highly inflammable lithium-ion (Li-ion) batteries is increasingly affecting shipping safety, as demonstrated by a number of fires on vessels such as roll-on roll-off (ro-ro) car carriers and container ships. Given the many difficulties involved with suppressing battery fires, particularly at sea, focusing on loss-prevention measures is crucial, whether batteries are transported within EVs or as standalone cargo, according to a new report from marine insurer Allianz Global Corporate & Specialty (AGCS) .

Hazards and causes

The report Lithium-ion batteries: Fire risks and loss-prevention measures in shipping highlights four main hazards: fire (Li-ion batteries contain electrolyte, an ignitable liquid); explosion (resulting from the release of ignitable vapor/gases in a confined space); thermal runaway (a rapid, self-heating fire that can cause an explosion); and the toxic gases that these hazards can produce. The most common causes of these hazards are substandard manufacturing of battery cells/devices; overcharging of the battery cells; over-temperature by short circuiting, and damaged battery cells or devices, which, among other causes, can result from poor packing and handling or cargo shift in rough seas if not adequately secured.

See also: Will Electric Vehicles Be Safer?

Loss-prevention measures for EVs on car carriers and in containers

Recommendations to mitigate the fire risk that can result from Li-ion batteries during the transportation of EVs on car carriers and within freight containers include ensuring that staff are trained to follow correct packing and handling procedures and that seafarers have had Li-ion battery firefighting training; checking the battery’s state of charge (SOC) is at the optimal level for transportation, where possible; ensuring that EVs with low ground clearance are labeled, as they can present loading/discharging challenges; and checking all EVs are properly secured to prevent any shifting during transportation. In transit, anything that can aid early detection is critical, including watchkeeping/fire rounds and using thermal scanners, gas detectors, heat/smoke detectors and CCTV cameras.

Ensuring safe storage of Li-ion batteries in warehouses is particularly important, as large-format batteries, such as those used in EVs, ignite more quickly in a warehouse fire than do smaller batteries used in smartphones and laptops. Recommendations include training staff in appropriate packing and handling procedures; establishing an emergency response plan to tackle damaged/overheating batteries and a hazard control plan to manage receiving, storage, dispatch and supervision of packaged Li-ion batteries; preventing the exposure of batteries to high temperatures and ensuring separation from other combustible materials; as well as prompt removal of damaged or defective Li-ion batteries.

If the maritime industry is to improve the transportation of lithium-ion batteries, all parties involved in the supply chain must understand the hazards involved, the most common causes and the problems associated with transporting. Regulations and guidance are specific in addressing these batteries to help prevent most incidents, but can only be effective if they are communicated and enforced. Only through a concerted effort by stakeholders in the supply chain can we hope to reduce the rate of incidents.

Other relevant findings from the report include:

• AGCS analysis of over 240,000 marine insurance industry claims over the past five years (with a value of €9.2 billion), shows that fire/explosion (from all causes) is the most expensive cause of loss, accounting for 18% of the value of all claims.
• The number of fires (from all causes) on board large vessels has increased significantly in recent years. Across all vessel types, fire/explosion was the second top cause of the 54 total losses reported in 2021 (eight), second only to foundered (12). Over the past decade fire/explosion ranks as the third top cause of loss overall, accounting for 120 out of 892 reported total losses, behind foundered (465) and wrecked/stranded (164). 
• Ro-ro and car carriers can be more exposed to fire and stability issues than other vessels. To facilitate carriage of automobiles, the internal spaces are not divided into separate sections as in other cargo ships. The lack of internal bulkheads can hurt fire safety, and a small fire on one vehicle or battery can grow out of control very quickly. Vehicles are not easily accessible once loading has been completed. The large volume of air inside the open cargo decks provides a ready supply of oxygen in case of fire.

Star Trek was ahead of its time in many ways, and not just because the sci-fi franchise portrayed a future several centuries after the series first aired in the 1960s. The cult classic TV show/films imagined what life might be like in the 23rd century, when humankind could be hurtling on spaceships through unknown universes. Much of the show was fantasy, of course, but the series has been heralded for foreshadowing the future. Dozens of articles have appeared on the topic, even one in the venerable Scientific American. Many tech writers have credited the show with anticipating and even inspiring the advent of myriad modern technologies, including iPads, flat-screen TVs, Bluetooth headsets, sliding doors and chatbots like Siri that can answer our questions and complete tasks.

But perhaps no technology imagined by Star Trek has enticed more international interest than the medical tricorder. The device, which resembled a clunky transistor radio on the original show, included a small, detachable scanner that Dr. McCoy, Spock or other Enterprise crew member could use to instantly diagnose health. The magical medical tricorder then pushed the patient’s clinical information to a master databank, allowing intergalactic doctors to learn more about all life to help further hone the technology. As I’m about to explain, we aren’t as far off from this scenario as you might think!

Since the tricorder’s first appearance, the device has inspired endless intrigue, spurring both techies and Trekkies to try to recreate the tool for real-life use. The technology needed to devise a modern-day tricorder, however, has long lagged behind the enthusiasm to do so. In 2014, telecom giant Qualcomm hoped to speed the science along, launching a global competition called the Qualcomm Tricorder XPRIZE and offering $10 million to anyone who could create a tricorder that diagnosed 13 medical conditions and monitored five vital signs, all independent of a physician. Qualcomm’s intent was to give people control over their healthcare—a theme of patient empowerment that you’ll see associated with many AI-enabled medical machines.

Eight international teams were selected to show off their prototype tricorders and compete for the $7 million grand prize. Although none were completely successful in meeting the competition’s demands, several came close, and the XPRIZE Foundation awarded more than $3 million to the top-scoring two teams and an additional $100,000 “Bold Epic Innovator” award to a third, donating more than $5 million of the remaining original purse to efforts in consumer testing and adaption of tricorders for hospital use in developing nations. In the four years since the conclusion of the Tricorder XPRIZE competition, the teams have met with varied success in moving their technologies from prototype to consumer-ready.

The “Bold Epic Innovator” team from Canada is arguably one of the most successful. Cloud DX had its beginnings in the aftermath of a devastating 2010 earthquake in Haiti. Physician Sonny Kohli was volunteering and realized quickly the need for a small, portable device that could help doctors diagnose patients. Just a few years later, back in Ontario, Kohli joined forces with others who would later become the Cloud DX team. Cloud DX’s tricorder, named Vitaliti, continuously monitored multiple vital signs, including blood pressure, heart rate, blood oxygen saturation and temperature.

Cloud DX today has made significant strides in tackling the problem of monitoring and diagnosing patients, inside and outside of the hospital. Its Connected Health Kit can do a lot of what its tricorder could do: monitor blood pressure, temperature, weight, glucose and blood oxygen levels. For doctors looking to keep an eye on patients who are discharged from the hospital, or in settings where hospital care is difficult to come by, the Connected Health Kit addresses many concerns. Devices like this one are an integral part of the future of healthcare. Tools like tricorders may seem like science fiction, and it’s true that we’re not (yet) able to wave a tiny device over the length of someone’s body and one second later know absolutely everything about their health. But Cloud DX and companies like it are showing us that science fiction is well on its way to becoming reality.

See also: How Digital Health, Insurtech Are Adapting

Empowering the Patient: How the Smartphone Is Transforming Medicine

Mobile health isn’t new. The practice of using personal mobile devices like smartphones with wearable sensors like watches to track and even diagnose medical conditions has been around for more than a decade. The Withings company launched its connected body scale in June 2009 and its blood pressure monitor (connected to the iPhone) in 2011, for example. And Apple made its foray into tracking personal fitness and health when it teamed up with Nike in 2006 with the Nike+iPod Sports Kit.

What is new, however, is the breadth of today’s technology. In the past several years, AI has advanced so rapidly that smartphone apps and their connected sensors can now accomplish feats previously inconceivable just several years ago. Using only a smartphone, you can now prevent health emergencies, diagnose clinical disorders and even treat conditions without prescription drugs.

AI isn’t the only technology driving the breakneck explosion of mobile medicine either. AI-enabled software is only as good as the data it relies on to make medical predictions. Today, software companies have more data than they’ve ever had before, thanks to millions of users worldwide who’ve been tracking their heart rate, steps, sleep and other biometrics, knowingly or not, for years. This ever-expanding databank allows software manufacturers to hone the accuracy of their existing apps while creating software and sensors that can monitor, diagnose and treat people in other amazing, new ways.

Another factor fueling the transformation of smartphone medicine is hardware, which has become more sophisticated in recent years. This hardware upgrade has given our phones the ability to process and store more data in a smaller space, making it as powerful as some supercomputers used to be. Today’s smartphone even outshines the supercomputer found on the spaceship Orion, launched by NASA in 2014 to prepare for man’s first crewed mission to Mars.

As our smartphones get smarter—and our out-of-pocket healthcare costs continue to rise—the world of medical apps has exploded. Today, there are more than 350,000 healthcare apps, and the mobile-health market is expected to approach $290 billion in revenue by 2025. It’s a fascinating contradiction: While the costs of technology continue to drop (does anyone remember how expensive the first personal computers were?), healthcare costs keep rising. It’s not really surprising that there’s a lot of interest, especially from big tech and the business world, in using the power of technology to tackle one of healthcare’s biggest challenges—cost. I believe that’s one of the reasons we’ve seen so many tech companies enter the healthcare and life sciences industries; their outsider point of view is not unlike the one I had looking in at the telecom industry and imagining how GPS could be used in a whole new way. The industry is revolutionizing not only how we look at medicine but also the power we hold in our hands to take care of our own health.

Think about it for a moment. If you could own an app that could diagnose you with the same accuracy as your primary care provider, you’d have the virtual equivalent of an on-call physician with you at all times who could help streamline your care in real life. Earache? Let the AI-enabled app, maybe combined with access to a telehealth provider, distinguish between something that needs an office visit in the next day or two, a simple prescription with advice to follow up in a week or a recommendation to head to the emergency room or urgent care right away. Without the cost or chaos of an unnecessary office or urgent visit, you’d be able to consult this virtual physician regularly without waiting to get seriously sick to realize something was wrong with you—or if you should just take an over-the-counter pain reliever and rest for the day.

Similarly, if your phone and a few connected sensors could monitor your blood pressure, cholesterol and other basic biomarkers around the clock, you’d know within seconds if something was irregular rather than waiting to reach the same conclusion after developing symptoms.

How many of us head to the dermatologist every year for a head-to-toe exam to look for signs of skin cancer? What if your phone could also scan your skin for signs of cancer or other ailments without the yearly trip, and in the comfort of your own home? And then transmit the scan to the dermatologist’s office, where it could be looked over? If things look good, you might get a letter in your electronic health record saying you’re good for another six months or a year. If the dermatologist sees something concerning, you might get a phone call instead, asking you to schedule an appointment for a follow-up in office. The setup could also be ideal for parents who are worried about a rash on their child. You’d have the ability to know what was wrong, probably in less time and at a lower cost than it takes to get an accurate diagnosis today.

In short, smartphones are democratizing medicine in ways we’ve never seen before—an idea first touted by the eminent cardiologist Dr. Eric Topol in his 2014 book The Patient Will See You Now. Since then, more of us own smartphones. Nearly four billion worldwide, including 81% of US adults, possess this portable supercomputer. Now, anyone who has a smartphone or smartwatch can potentially access quality healthcare, no matter how old they are or where they live, whether in a big city with access to excellent hospitals and specialists or in a rural area without many medical facilities or qualified physicians. We’ll still need trained doctors, of course, and there’s some level of infrastructure needed to get healthcare systems ready to receive data from our phones and digital devices, but the smartphone has become medicine’s great equalizer, making it easier for everyone to obtain top medical attention, regardless of their nationality, ethnicity, age, income level, insurance coverage or other factors that have traditionally limited quality healthcare.

This article is an excerpt from "The Future You."

Imagine this email exchange with a claims executive:   

Pre-COVID: “We’re a people-driven firm, Tom, and our people don’t want Big Brother. What our people want, our people get, as we need to keep them, not drive them away. Humans are cheaper and easier to manage than bots. And bots don’t buy auto and home policies.”

Last week: “Hey, Tom, with remote work seemingly a permanent thing, skyrocketing wages and turnover, plummeting service levels and the Great Retirement looming on the horizon, I think we need 10 Big Brothers ASAP.” 

Our working world is fundamentally changed from three years ago. Skilled workforce is aging and retiring earlier, younger workers are opting out of mundane jobs in search of meaningful work, wages are indeed skyrocketing, machines are getting more proactive, customers are more digitally demanding (and impatient) and then there’s general inflation, an annualized 8.3% as I type this. 

And people like working remotely; they like it a lot. 

All of these trends, except inflation, we hope, seem long-term. Customer-native firms (e.g., State Farm and virtually all carriers of any size in the U.S.) start with customers and add technology; digital native firms (e.g., Lemonade and virtually all insurtechs) start with technology and (hope to) add customers. As a rule, customer-natives view post-COVID trends as a threat, operational or existential in scope, and digital natives see opportunity.

Most readers have been through a formal program of change management, typically led by a management consultancy, to implement growth strategies, embrace new IT systems or reorganize to reduce expenses. The program likely followed the industry-standard Kotter 8-Step approach: 

1. Establish a sense of urgency
2. Form a powerful guiding coalition 
3. Create the vision
4. Communicate the vision
5. Empower people to act on the vision
6. Plan for and create short-term wins
7. Consolidate improvements for more change
8. Institutionalize new approaches       

See also: Insurers Turn to Automation

Let’s focus on 1) urgency, because without it nothing much else happens. “At the very beginning of any effort to make changes of any magnitude,” writes John Kotter, the dean of organizational change management, “if a sense of urgency is not high enough and complacency is not low enough everything else becomes so much more difficult, difficulties that add up to, historically speaking, a 70% chance of failure.” 

Customer-natives tend toward complacency and digital natives tend toward urgency, as garnering customers is an existential challenge--there’s a cash burn rate and a clock ticking somewhere counting down to death and not a cushy retirement. Urgency is definitely a strategic cultural advantage post-COVID.  

Re-reading the two emails, there’s a tone of complacency in the first and a palpable urgency in the second. Urgency is a starting point; it’s fuel for change. The bad news is automation doesn’t happen ASAP. Like any meaningful change, automation is a process, not an event, with three steps: 

1. Discovery 
2. Instrumentation and Optimization
3. Implementation and Control 

In the follow-up meeting with the client, we didn’t talk about automation or the three steps. I asked two questions, finding answers to which consumed the hour:   

1.  Who owns the problem? In many customer-native firms, this question typically triggers a call from operations to the IT department to, in this case, buy robotic process automation (RPA) licenses and spin up some bots. Exporting the problem to IT may have worked in the past, but it won’t work with automation.   

Let’s assume a highly automated operation at some point in the future. With round-the-clock attention required and service-level agreements (SLAs) measured in minutes, would you want the humans managing the automations to roll up to you (operations) or IT (the CIO)? A desire to own the solution implies or demands a willingness to own the problem.  

IT can and should play a valuable role in the automation journey, but it’s a support role, like finance or HR plays.

2.  Who is going to lead the process? Generally speaking, management’s role is to optimize the existing system. Though automation, when properly done, is about process optimization, it also represents a cultural paradigm shift. 

One thing you can be sure of, automation will trigger urgency from workers who view it--rightly or wrongly--as a threat to their working existence. Line workers will have no problem summoning the urgency to reject it. Who is going to own leadership resolve? Who is going to stand as change agent number one, spreading the automation mission to agents two, three, four and beyond? 

Leading the automation effort isn’t a path to popularity, at least in the short term. Leadership requires energy, vision, resolve and a thick skin. While it’s true you’re about automating jobs humans decreasingly want, such logic tends to melt in the emotional heat. 

Assuming leadership, here again the IT department can play a valuable support role building demo bots sharable with other line leaders, the COO, CEO and board. Working bots draw an emotional rise from leaders familiar with them in concept alone. RPA is a truly fantastic tool for spreading urgency toward a larger automation initiative. And it’s cheap.    

The term “Big Brother” is indicative of a widespread perception of workplace automation. Though automation entails the mapping and tracking of human activities, the goal is not surveillance per se but operational excellence in resource-constrained environments serving customers growing more digital by the day. The leader’s impulse may be to strike the term “Big Brother” from the firm vernacular, but the real job is to change the mind that produces the term. 

Ultimately, there are three games in automation: human v. machine, complacency v. urgency and leadership v. management. Position yourself to play and win all three.

If you compare a 1922 Ford Model T with a 2022 Tesla Model S, it’s easy to focus on the differences.  However, since the inception of the automobile, there are some basic features that have endured—there are still four wheels, seats and a steering wheel, and the goal is still moving people safely from one destination to another. The innovations made over the last 100 years have made this core structure safer, faster and more efficient.

Insurance—at its core—is all about data processing. Methods for collection and analysis have become more sophisticated over the years, but data still drives everything in the industry. And now we have the ability to look at data differently, leveraging innovations to identify risk, extrapolate insights and see the bigger picture big data offers. Digital capabilities have moved risk research beyond paper forms and phone calls and allow complex real-time comparisons to power more informed and strategic decisions.

Insurance carriers have long been early adopters of new technologies to aid in data procurement and computation. Much like automobile production, the insurance industry is continually streamlining its collective production line, moving from punch card tabulators in the mid-20th century to be one of the first and most prolific adopters of computerization.

This pursuit gave rise to the insurtech industry, which specializes in innovations to better identify risks and opportunities for insurers and insureds. Artificial intelligence (AI) is the next logical step in the evolution of insurance data collection and analytics. In just the last few years, AI and machine learning algorithms have provided commercial insurance carriers a much faster, more thorough and more accurate depiction of business risk. AI helps expand, deepen and interpret the range of available data sources, making commercial risk more searchable, accessible, and rapidly actionable. 

In commercial insurance, it’s often the first quote returned that binds the business. The digital age has trained consumers to expect immediate responses to their inquiries. According to the Salesforce 2021 State of the Connected Customer Report, 88% of customers expect companies to accelerate their digital initiatives to keep pace.  

See also: How AI Can Solve Prior Authorization

A Tailored Fit for Bespoke Strategies

More and more leading insurance carriers are trying on AI for real-time data and advanced analytics to reimagine risk evaluation, enhance efficiency, optimize rate-setting, improve customer experience and customize offerings. 

AI and machine learning programs have shown to fit perfectly within the insurance business model. The proliferation of insurtech options offers a clear path forward to optimize all aspects of the insurance lifecycle with analytics and predictive data technology, benefitting insurers and insureds alike. As an example, with just a business name and address, the Planck platform provides real-time business data, customized insights, submission validation, submission prioritization, risk-scoring, risk management and automation options for straight-through processing.    

The ACORD 2022 Insurance Digital Maturity Study, based on an analysis of the 200 largest insurers in the world, indicates an unambiguous connection between digitization and insurer performance. In a recent interview, ACORD President and CEO Bill Pieroni said, “Digitization is the key enabler for the vast majority of strategic imperatives and allows insurers to address them in a timely manner.” And, according to Deloitte’s 2022 Insurance Industry Outlook, which includes 424 insurance respondents from around the globe, 74% expect to increase spending on AI. 

In 2015, I contributed to an article for the Harvard Business Review, “Know What Your Customers Want Before They Do,” where I warned that “the technologies and strategies for crafting next best offers are evolving, but businesses that wait to exploit them will see their customers defect to competitors that take the lead.” What AI truly offers commercial insurance carriers is a distinct competitive advantage to better understand and service businesses. It is possible to know the risks before your customers realize them—or before something happens.

Over the past few decades, our increasingly digital economy has dramatically expanded the amount of both available data and data sources. Billions of internet users around the world contribute daily to this expanding online library—including photos, blogs, customer reviews and social media posts. Anyone can access this data, but the future of commercial insurance is being guided by those equipped to read the insights.  

See also: Insurtech Success Stories: Still Waiting for Godot

Crowd-Sourced Data and The Parable of the Ox

I first discovered The Parable of the Ox in “The Wisdom of Crowds” by James Surowiecki. I’ve seen several industry-specific iterations and parodies since, but the main story beats are always the same. A contest challenges hundreds of people to guess the weight of an ox. While some guesses came in too high and others too low, the average of all the guesses submitted was almost exactly right. By connecting individual data points, a reliable response method emerged. This process was taken a step further by creating models of what the submitted guesses might be and using those data points to predict the correct response.  

AI and machine learning models create the same revelatory inroad, but on a much larger scale. The Planck platform, for instance, finds all relevant business data in real time and refines the information into valuable insights. Machine learning amplifies the process by modeling and creating additional risk insights and building a gold standard. 

Using big data to leverage crowd wisdom at this level would be impossible through manual research. Digital assistance creates these opportunities for bespoke solutions. Underwriters are still making the decisions, but with an enormous amount of help to support their process.

For example, one carrier employed a basic randomized formula to select organizations from their written policies for a post-bind audit. This audit would identify opportunities to cancel or propose changes to coverage, limits or rates. Because this was a manual process—taking about two hours per policy—they were only able to audit a small percentage of their book.

Using an AI-based scoring algorithm, Planck was able to look at the entire book of business to model and identify the businesses most likely to require underwriter follow-up. This exploration created significant process efficiencies, identified new revenue opportunities and generated value for the carrier and their insureds.  

Data superpowers can extend further into the customer experience by offering preemptive guidance to mitigate risk. According to a 2021 report from Beazley, insurance customers are looking for more from their insurance partners to meet their changing business needs. If a new restaurant wants to remain open until 2:00 AM, you could use AI to collect surrounding business data, police reports, police station proximity and other data points to offer insight into potential risk of assault or criminal activity. These insights could be applied to other areas of the policy lifecycle, as well, such as market research, prospecting and renewals.

As early adopters of digitization, the insurance industry clearly recognizes the value of capably mining and refining big data. However, that early adoption can often be a significant impediment in the form of legacy systems. The first step in the process is finding a vendor that aligns with your business and taking them for a test drive. See how the provided solution can be applied to your approach to customer acquisitions, submissions, underwriting and renewals.   

Quality insurtech solutions aren’t meant to replace existing systems—or underwriters, for that matter. Rather, they augment process and capability to handle modern data flow. The goal remains the same, but faster, more accurate and more efficient. And digital laggards are likely to be left in the dust by insurers with strategies driven by AI. 

Digitization and data. Data and digitization. No matter what sector you are in, you almost can't use one word without the other. Insurance is no different. Gone are the days when insurance was an offline financial product disconnected from the ecosystems and risks the “paper” was meant to cover. 

The ecosystem where all of this is coming together is the supply chain and transportation risk management space. Supply chains, as we all know, have been in turmoil for the better part of the past three years as crisis after crisis has rippled across suppliers, shippers, logistics service providers and transportation companies. The situation may seem dire, but there is reason for optimism. Why? Digitization and data. 

The supply chain and transportation ecosystem is rapidly digitizing. According to a 2021 McKinsey report titled “How COVID-19 is reshaping supply chains,” over 93% of companies state that they intend to make their supply chains far more flexible, agile and resilient. The same report makes the case that “success of an organization's performance is strongly linked to its use of modern digital tools, especially advanced analytics. Compared with organizations that reported problems, successful companies were 2.5 times more likely to report they have adopted advanced-analytics capabilities."

In summary, the disruptions we’ve experienced have accelerated the need to digitize and create more resilient supply chains and transportation networks. Transportation management systems, fleet management systems, warehouse management systems, routing solutions, real-time transportation visibility platforms, IoT devices and telematics are all being deployed at an increasingly rapid clip. Better yet, they are being stitched together into end-to-end solutions to create value across digital siloes.

See also: Tomorrow’s Insurance Is Connected

What does this have to do with insurance? A lot. As the supply chain and transportation ecosystem digitizes, the data captured and, most importantly the insight created, is bringing real-time operational risk management closer than ever to financial risk transfer.

Loss control teams at insurance brokers and carriers desperately need new ways to de-risk day-to-day operations of high-value shippers that have had troubling losses. Enter real-time digital risk management platforms (DRMP).

Savvy loss control professionals are finding out that these platforms can and do serve as an early-warning system, complete with a set of best practices that can be translated into intelligence used to spot non-compliance issues and known leading indicators of losses. What’s more, loss prevention staff can be more prepared than ever to intervene on behalf of insureds at a moment’s notice, especially when issues escalate to high-priority concerns.

Sounds simple, right? Ha. Maybe not that simple, but entirely doable. For the hundreds of billions of dollars of freight that are now being actively monitored, it’s not unusual to see loss frequencies in the low single digits and loss ratios in the mid-teens. 

The connection between real-time data and insight, loss control and underwriting is the next horizon for commercial property and casualty insurtech. In our world, that means cargo and commercial auto insurance will be programmatically fused to digital platforms that predict risk and prescribe interventions in real time. As an industry, we will connect the dots between theft and pilferage loss causes, product damage, asset conditions, operator health and safety and ever-changing operating conditions to dynamically price risk in real time and mitigate profitability killers for insurers and insureds alike.

Taken to their logical conclusion, these business models will beget innovative parametric insurance programs, “pay-how-you-orchestrate” offerings and enterprise risk transfer strategies more like digital advertising platforms and securities markets than anything resembling today’s insurance markets.