At SMA, we have been tracking what we and others have called emerging technologies for the better part of the last decade. However, the question arises, “When is an emerging technology no longer emerging?” Technologies such as drones or mobile payments are often classified as emerging tech, but these technologies are, in fact, relatively mature. And their adoption is becoming widespread. Artificial intelligence is also touted by many as an emerging technology, which is quite odd given that the term AI was coined in 1956. But maybe classifying technologies as “emerging” misses the main point anyway, which is how technology transforms industries. Thus, rather than approach the topic from a pure technology perspective, we believe it is more important to take an insurance business perspective. This is why we at SMA now discuss a category we call transformational technologies. From this point of view, when the technology was created or how far along it is in the development cycle becomes irrelevant, although those are still interesting facts. Instead, the focus on transformational technologies places emphasis on which technologies are now having (and will have) the most impact on the insurance industry. What, then, are examples of transformational technologies? There are a large number of technologies that fit the bill, so perhaps it is more useful to sort them into categories. First, a major point before identifying the four key categories of transformational technologies – data is at the center of transformation and is fueling every transformational technology. Whether the data is proprietary or generally available, structured or unstructured, or gathered from traditional or new sources, it is essential to every single transformational technology. See also: Emerging Tech Is Poised for Growth   The four main groups of transformational technologies include:

1. The connected world: sensors, devices, platforms and solutions that are related to buildings, vehicles, people and other physical things in the world
2. Access, transfer and security tech: technologies such as 5G, edge, blockchain and biometrics that are vital for information in a connected world
3. Insights and actions: the analytics and AI technologies that derive meaning and drive actions
4. New UI technologies: tech that now includes voice, chatbots, augmented reality and more

It’s important to understand how these technologies in combination with foundational technologies address specific insurance business problems and opportunities. The following examples show the power of applying a business use case lens to identify potential solutions that leverage transformational technologies. Claims fraud: Technology to address this age-old problem has been evolving for decades. There are now solutions that combine machine learning with existing claims administration systems, damage estimation systems and data – to take fraud detection and management to new levels of effectiveness. Property underwriting: Aerial imagery captures digital data from drones, satellites and fixed-wing aircraft, which is then analyzed by AI/image recognition/machine learning algorithms to present insights to underwriters on property characteristics and risks. These technology systems are integrated with the foundational systems and data that underwriters use today. See also: Insurtech’s Lowest Common Denominator   Change is sweeping through the insurance industry, which is likely to continue full steam ahead for the next decade. The transformational technologies that are the catalysts for much of this change will continue to evolve and be applied to more and more use cases across the insurance enterprise. And whether we call them emerging, transformational or something else will make no difference: These technologies are ushering in a new way of doing and experiencing everything.

Cybersecurity threats faced by insurance companies are growing and evolving at an alarming rate. This has been spurred by many factors, including the internet of things (IoT). While the IoT presents opportunities for insurers, it also exposes security gaps. The severity and frequency of cyber-attacks are likely to increase. Insurers must commit to protecting sensitive customer information in a compliant and reliable way. The cybersecurity threat is huge. It is time for insurance companies to reboot their approaches to cybersecurity. Common cybersecurity threats facing the insurance industry Cyber-extortion Cyber extortion is increasingly becoming a common problem. Some types of ransomware attacks are so effective that victims may be forced to meet the attacker's demands and pay a hefty bribe to get their systems running again. Automated threats Credential cracking, vulnerability scanning, bad bots, credential stuffing and denial of service can potentially shut down a company’s systems quickly. Identity theft and loss of confidential data Identity theft may result from system vulnerabilities to data breaches. For instance, files stored on a firm's local servers may not be protected adequately. Insurers collect and store sensitive personal client information. This information can be particularly valuable for attackers to sell in black markets. They can use it as a tool for fraud, extortion, unauthorized borrowing and many other financial crimes. Business disruption and reputation damage Cyber-attacks can seriously disrupt business. For instance, a cyber-attack on Sony Pictures erased its computer infrastructure, including telephone directories, emails, voicemails and business records like contract templates. A malicious attack like this on an insurer could disrupt operations for months. See also: Cybersecurity for the Insurance Industry The foundation of any insurance business is policyholder trust. If an insurance company were to suffer a data breach exposing policyholder information or a cyber-attack that renders it unable to conduct normal operations, that trust would be shaken. This, in turn, can lead to reputation damage that may hurt the confidence of investors, consumers, policyholders and rating agencies. Four tips for boosting security 1. Assess your defense capabilities realistically Pressure-testing the company's defenses can determine whether they can repel targeted, high-impact attacks, whether external or internal. The testing includes vulnerability assessment, testing programs, penetration tests and scenario-based testing. Consider hiring a cyber-security firm to test your defenses. 2. Invest in early detection Insurers need to continually invest and innovate to thwart potential attackers. Early detection is crucial. Otherwise, a cyber-attack can sit undetected for weeks. Efficient and quick detection and response will help determine the source of the attack, the systems targeted, extent and cause. Then, the threat can be neutralized before damage is done. Insurers need to invest in technology. There is a wide range of software solutions that provide near-real-time threat detection. 3. Making cybersecurity everyone's job While implementing sophisticated systems will reduce external threats, insurers tend to neglect internal threats such as human error, which could include revealing customer data in response to a convincing phishing email. Cybersecurity awareness among employees can significantly decrease the risk of cyber-attacks resulting from human error. Alert employees can provide early detection. An Accenture survey found that up to 98% of security breaches that are not detected by a firm's security team are discovered by employees. 4. Learn from the past and evolve Effective cybersecurity requires insurers to learn from previous cyber incidents and use the learning to improve planning and technology investments. Solutions include:

• Upgrading systems: Using last-generation or unpatched security software provides easy fodder for cyber attackers. Speak to your IT consultant about upgrading your systems.
• Migrating systems to the cloud: The cloud provides users a wide range of compliant and secure storage solutions. Choose a cloud provider that offers the highest possible security.
• Implementing appropriate security software, protocols, and appliances: This will effectively shield data and systems from automated threats.
• Establishing a disaster recovery plan: Despite all efforts, systems can be breached. Have a detailed up-to-date plan so that you can respond effectively to any problem, major or minor.

See also: Global Trend Map No. 12: Cybersecurity   Cyber-crooks are relentless and determined. Security is an continuing battle. You can’t afford to let down your guard a second. Staying one step ahead of hackers takes constant effort.

The relentless pace of the digital age continues to change insurers’ strategies. However, for more than a decade, two constants have been the need: 1) for a customer-oriented mindset and 2) for adapting quickly to an increasingly fast-paced world. As carriers have tried to come to grips with new ways of doing business, one of the biggest recent developments at the corporate level is Experience Design owning a seat at the table. Why? For starters, Experience Design has hard-won expertise and plays a vital role in the customer experience and brand architecture. In addition, Experience Design teams are good at solving problems and often have experience doing so in multiple industries. Instead of trying to figure out after strategy planning what the future could be, involving Experience Design will help carriers define it from the outset. Building on existing strengths Although some industries are further ahead in digital capabilities, design and customer experience than insurance, most insurers do have some real strengths. First and foremost, they tend to have a very solid understanding of their customers. Moreover, they have extensive experience adapting products and services to changing market conditions. See also: In Search of a New ‘Dominant Design’ However, digital has profoundly changed the way carriers interact with customers and how quickly and frequently they need to act. New technologies have amplified the need to listen more closely to customers and respond to their needs in weeks and months instead of months and years. Almost all carriers can do a better job taking the insight they possess and quickly turning it into something actionable, such as more flexible and adaptive products and more frequent and meaningful interactions with customers (and on their terms). Always on What makes this change in focus difficult for most insurers is that they’ve historically operated in a stop-start, point-A-to-point-B manner. But, in a digital environment, constant improvement is now the rule of thumb. Recognizing that work gets done but is never really finished is a major shift in thinking for the industry. See also: How to Redesign Customer Experience In response, leading carriers are no longer looking for a big launch but are incrementally improving products, even if they evolve into something quite different than their original form. This continuous cycle of launch, learn, adapt and relaunch is where Experience Design excels. As carriers increasingly involve creative solutions and get better at working in the always-on business cycle, their product offerings and relationships with customers will improve.

Last year saw some of the deadliest and costliest wildfires in California history. With the long-term impacts of the Camp and Woolsley fires still being felt months later, many in California are already thinking ahead and considering how best to prepare for future wildfire seasons. The problem is only going to get worse due to climate change: The total area exposed to wildfires is increasing, and the wildfire season continues to grow longer. Recognizing this new reality, the California Senate recently passed a bill that would authorize the state to purchase insurance to mitigate the costs associated with natural disasters such as wildfires. When fires threaten a community, all evacuees (even those lucky enough to have homes they can eventually return to) will incur financial impact – from lost wages to the cost of emergency accommodations. These expenses are not generally covered by insurance, leaving a serious protection gap at a vulnerable time. Parametric insurance products could provide immediate financial relief in the form of automatic pay-outs for insureds living in the evacuation zone – these products present an intriguing protection option for California residents and communities. Automatically Triggered by Pre-Defined Events Parametric insurance, also known as index insurance, is an innovative product that pays a fixed sum when a precisely defined event takes place – for example, wind of more than a certain speed at a specified location. The insurance is not a direct substitute for conventional insurance because of the "basis risk" – the potential gap between the policyholder's loss and the payout. But the upsides are certainty and a rapid payout, because there is no claim to be made or adjusted. Those upsides could be appealing in certain circumstances. Parametric insurance is increasingly being used in the developing world to fund rapid relief and recovery in response to natural disasters, such as drought leading to crop failure. There are moves to develop products that will make a partial payout in advance (for example, if a major storm is forecast) to fund even earlier intervention. As the impact of climate is felt more widely, we may see an increased take-up of parametric insurance in the developed world, too. California wildfires could be a particular case. Evacuation Costs Impose a Financial Burden Emergency relief shelters generally provide aid within the first few days of fires, but displaced evacuees soon have to look to other housing options. Reports on the aftermath of disasters show that nearby hotel and motel rates jump. Aside from housing costs, evacuees are often forced to pay out-of-pocket expenses for food, gas and emergency supplies. Evacuees with homeowner or renter insurance coverage may have traditional insurance policies that eventually help with these costs. Policies with inclusions for “Additional Living Expenses” (“ALE”) typically cover food and housing costs, furniture rental, relocation and storage and extra transportation expenses. However, under the traditional insurance model, insurers will reimburse residents for ALE only after a claim has been filed and adjusted. See also: How to Fight Growing Risk of Wildfire   Regardless of whether an evacuee is eligible to receive ALE, the scale of destruction leaves many evacuees uncertain as to how they will afford cost-of-living expenses in the immediate-run. Evacuees unable to afford these costs are subsequently left at the mercy – and often slow response – of government-funded emergency relief. Parametric Insurance Can Provide Financial Emergency Relief Parametric insurance can fill the protection gap, providing wildfire evacuees with rapid funding when they are forced to evacuate. Recently approved California legislation will require local emergency notification systems to warn residents of impending danger, and these warnings could serve as a trigger. A parametric insurance product offered to local municipalities may likewise afford cities and counties with quick and predictable funds. Government emergency relief funds are initially paid to cover state emergency operations. Local municipalities affected by wildfires rely heavily on relief from non-profit organizations, but, when such relief cannot adequately address immediate and longer-term financial needs, the burden is placed back on the residents to pay expenses out of their own pockets. Parametric insurance policies can be crafted to uniquely address the needs of regions threatened by climate catastrophes. For example, some of those exposed might have traditional insurance policies with significant excesses. Parametric insurance could be used in conjunction with these, so the policyholder receives a speedy payout when cash flow is tight during an evacuation, and then make a conventional claim for the balance of the loss. Whether used independently or in conjunction with other insurance, a parametric insurance product will lessen many of the financial burdens associated with wildfire evacuations. A Greater Role for Parametric Insurance Potential wildfire evacuees are one group that may benefit greatly from a parametric insurance product, but they aren’t the only Californians who could benefit from the predictability and efficiency these products can afford. Parametric insurance is currently used to insure crops from failure due to various weather conditions. This product can similarly be modeled to wineries and commercial agriculturists in Northern California who are concerned that wind, ash and debris from wildfires could diminish quality. With climate change increasing the devastation of wildfires in California, parametric insurance can play an important role in responding to the increasing financial burdens placed on the affected residents. See also: The Challenges Ahead in 2019   Beyond California The number of natural disasters that have wreaked havoc in California recently make the state a natural outlet for parametric insurance products, but it is far from the only area of the country where these products may be useful. Hurricanes routinely leave long-term damage throughout the U.S., from Texas to Puerto Rico to New York; in fact, the number of weather-related loss events in this country has tripled since the 1980s. The ability to provide rapid funding for relief, recovery and reconstruction efforts is an essential need, and parametric insurance provides an effective solution. There is no simple or one-size-fits-all solution to natural disaster protection. But novel forms of risk transfer such as parametric insurance hold a great deal of potential. This resilience-linked product is an excellent example of how the insurance industry can respond to weather-related threats in effective ways, and represents a key opportunity for future innovation.

The occurrence of school shootings, store robberies and job-related fatalities have all contributed to the increase in cases of post-traumatic stress syndrome (PTSD) in the workplace. This session at the RIMS 2019 Annual Conference and Exhibition discussed the need to address the issue on a broader basis. Speakers included:

• Dr. Teresa Bartlett, senior vice president, medical quality, Sedgwick
• Denise Algire, director, risk initiatives, and national medical director, Albertsons
• Dr. Steve Wiesner, on-the-job medical director, workers’ compensation service, Kaiser Permanente

Post-Traumatic Stress Disorder (PTSD) is a complex disorder that affects the memory and emotional responses of a person who has experienced or witnessed an event that involved actual or threatened death or serious injury. The worse the trauma, the more likely it is that a person will develop PTSD and the worse the symptoms. The most severely affected patients are unable to work, have trouble with relationships and have great difficulty parenting their children. See also: The Need to Be Open on Mental Illness   MRI and PET scans show changes in the way memories are stored in the brain for patients who suffer from PTSD. The disorder actually changes the portions of the brain that regulate the fight or flight response and the area where memories are coded and stored. Symptoms are generally grouped into three types: intrusive memories, increased anxiety and avoidance/numbing. Prognosis depends on the patient’s health prior to developing the disorder but is improved with early treatment, preferably within the first 12 months. Patients with PTSD are more likely to have amplified pain and stress reactivity when they are injured, leading to longer-tail claims. Major life-threatening events that can lead to PTSD:

• Combat or military exposure
• Sexual or physical assault
• Childhood sexual or physical abuse
• Serious accidents, such as a car wreck
• Terrorist attacks
• Natural disasters, such as fire, tornado, hurricane, flood or earthquake

When PTSD occurs in the workplace, an employer’s program should address early intervention, specific functional limitations, treatment consistent with care and time away from work as part of the treatment plan. Clinical involvement early in the claim process is critical. A successful employer program should include critical incident response as well as continuing guidance and counseling. Critical incident response gives the employee a chance to express feelings or reduce stress and gives management an opportunity to show concern for colleagues, both of which can help the employee re-acclimate when the person returns to work. See also: How to Help Veterans on Mental Health   There are many work accommodation considerations to take into account for employees suffering from PTSD. Modifying specific environments that trigger memories of the original stressor can be very helpful. For example, an employee who was present for an armed robbery could be transferred to a different location, if possible. Allowing work-at-home or flexible scheduling opportunities can also be effective, to allow the employee ample time for mental health treatment. There are an extraordinary number of other possible accommodations that may be needed, addressing alertness/concentration, decreased stamina, memory loss and stress intolerance. If an employer is committed to safely reintroducing the employee into the work environment, the employer will need a clear and actionable plan.

The dispiriting news that 11 people died this year in efforts to summit Mount Everest brought back memories of my callow youth, when I volunteered to make the attempt for a story for the Wall Street Journal. There may also be a hopeful lesson for how the insurance industry can mature.

The Everest attempt would have been part of an informal series in which I played daredevil. I sailed across the Atlantic in a 42-foot boat, having never even set foot on a sailboat before, and wrote a front-page piece about the adventure (including 70-foot waves). A couple of years later, a friend convinced me that I was just dumb enough to go to a school for professional wrestlers and provide an inside look. After I wrestled a match on cable-TV in 1989 and wrote my article, the WSJ's managing editor showed the video to the Dow Jones board of directors and won me a corporate award for service above and beyond the call. He told me by summoning me to his office and saying he had good news and bad news. The good news: "I have a $1,000 check for you." The bad news: "I'm looking for someone to swim around Manhattan."

I responded that I wasn't much of a swimmer but that I'd happily climb Mount Everest if he'd put up the fees. His response was incorrect but potentially life-saving: "Nah," he said, "it's getting too easy."

Fast-forward to 1996, and we all learned just how dangerous Everest still was even though Sherpas were installing ropes and ladders for the tourists everywhere they could. 1996 was the year when journalist Jon Krakauer (a veteran climber, unlike neophyte me) summited Everest, then witnessed the series of events that left eight climbers dead. 

I got the message and never again considered an attempt, but the number of climbers keeps growing—as does the death toll. Sixteen Sherpas died in an avalanche in April 2014 as they set up base camp. Nineteen people died in an avalanche on the mountain a year later when a 7.8 earthquake devastated Nepal, killing more than 9,000. Five people died on Everest in 2018, a year in which more than 250 foreigners summited. And this year, 11 died despite no particular issues with weather. 

So, what's my hope for the insurance industry? Doesn't Everest just show that people can convince themselves to ignore even deadly risks?

My hope stems from seeing how much more information is available to climbers and guides than was available in 1996, the season that Krakauer chronicled in "Into Thin Air," and in understanding how much improvement is still possible. The increased "wiring" of the mountain could serve as an example of how technology can head off catastrophes—the sort of thing that insurers increasingly need to do, rather than focusing on pricing the risk and paying claims after a loss.

In 1996, there was barely any communication with those at the top of the mountain -- just some occasional connection via a satellite phone—so information about weather was limited for climbers. One who summited that year prepared to bask at the top, only to look out and realize that a major storm was fast approaching. He recognized the cloud formation solely because he was a commercial airline pilot and not, say, a journalist. He hightailed it down from the summit and, through sheer happenstance, survived the whiteout that led to the eight deaths. 

By contrast, a doctor saved himself last year through technology. He brought along an oxygen saturation meter, which, when pressed against his skin, told him that a feeling of weakness wasn't just something to push through: His blood was dangerously low in oxygen, showing that he was headed toward a lung condition that is often fatal at high altitude and that he needed to head down immediately and abandon the ascent.

Many people succumb to what climbers call "summit fever"—you've worked so hard, and you're so close, how can you turn back even if your body feels like it's shutting down? Ten of the 11 who died this year had summited and were on their way down when they collapsed. Fitbit-like medical technology will increasingly let climbers measure their physical state and understand when their health is at a critical level and that they need to turn back to save themselves. 

Information about conditions near the summit will keep getting better, too, and not just for the weather. Many of us have seen the photo showing stop-and-go traffic near the summit on May 22, but few have seen the photos from a Sherpa in the same spot a week earlier, when nary another soul was in sight. As climbing parties become more connected, it'll be easier to manage traffic. Maybe you move your attempt up a day or back a day if you think you're just going to sit there for hours in the "death zone" (above 8,000 meters, or roughly 26,000 feet), where the altitude takes a monstrous toll.

Everest will still have its allure—K2 is a much harder ascent, but who brags about climbing the world's second-highest mountain? And many will enable climbers. Nepal, such a poor country, isn't going to turn away people willing to pay $11,000 apiece for a climbing permit and to generally pump money into the economy. Guides, charging some $35,000 a pop on top of the permits, aren't going to turn away clients, either. There will always be something asynchronous about a summit attempt, too—months of training and weeks of adjusting to altitude end up in daily or even hourly decisions about weather and in instantaneous assessments of health.

But there's still hope that much better information will lead to better decisions about when to take the risk and when to wait for another day. That's the plan from sea level, anyway.

Cheers,

Paul Carroll
Editor-in-Chief

As insurers strive to become more relevant to their customers and more efficient, they have embraced the strategic importance of their data. Insurance companies have been using various data streams to predict property damage and loss for generations. But while they have been collecting increasingly large stockpiles of consumer data, until recently they have lacked the tools and talent to operationalize it -- particularly with the level of transparency required by regulatory bodies -- to drive better products and services and operational efficiencies. Advances in AI and machine learning have enabled insurers to improve the customer experience and boost policyholder retention while cutting claims handling time and costs, eliminating fraud and protecting against cybercrime. These new tools and platforms have generated increased interest in using data science across the industry, and insurance companies have been investing accordingly. According to a recent study, 27% of large life/annuities insurers and 35% of large property/casualty insurers are expanding their data science efforts to some degree, while 13% of large life/annuity insurers are piloting an initiative. Midsize insurers are similarly active in the space, with 20% of life/annuity carriers and 24% of property/casualty carriers looking to expand their data science efforts. See also: Turning Data Into Action   But while investments in AI are growing, insurance organizations are often finding that their existing analytics and business intelligence technology and talent aren’t capable of meeting their current and expanding needs. Challenges in resources, technology infrastructure and the ability to operationalize models quickly and efficiently can prevent insurers from fully leveraging AI and data science to drive business impact. To overcome these challenges, and maximize the ROI on AI investments, insurance companies must look to innovative solutions such as data science automation. While data science is becoming a valuable tool in the insurance industry, implementing a data science program is not easy. A typical enterprise data science project is highly complex and requires the deployment of an interdisciplinary team that involves assembling data engineers, developers, data scientists, subject matter experts and individuals with other special skills and knowledge. This talent is scarce and costly. This is neither scalable nor sustainable for most insurance organizations. Data science automation platforms fully automate the data science process, including data preparation, feature engineering, machine learning and the production of data science pipelines - enabling insurance organizations to execute more business initiatives while maintaining the current investments and resources. Data science automation allows data scientists to focus on what to solve rather than how to solve. End-to-end data science automation makes it possible to execute data science processes faster, often in days instead of months, with unprecedented levels of transparency and accountability. As a result, insurance organizations can rapidly scale their AI/ML initiatives to drive transformative business changes. There are several key areas where data science automation can make a big impact in the insurance industry. For increasing operational efficiency, AI-based automatic underwriting and claims management will be a major trend that we will see in coming years. In customer relationship management, AI will be used more frequently to help profile customer behaviors, helping insurers to get a better and deeper understanding of their customers’ wants and needs. This, in turn, will help to drive revenue growth. See also: Role of Unstructured Data in AI   In the near future, data science and AI will be widely implemented in the insurance industry, and the barrier to adoption for data science and AI will become low. Once this happens, accumulated critical use cases will be key differentiators for insurance companies implementing these technologies. Data science automation accelerates the data science process, enabling insurers to explore 10X more use cases than with the traditional method of data science. Early adopters have already started to leverage automation to scale their data science initiatives.

So what convinces people to support an idea, whether the support be their time (e.g., joining your project), money, endorsement or any other backing to help you and your idea? Our research suggests that people and organizations will be influenced primarily by three related innovation-specific factors:

• Human capital: who you are as a leader of innovation
• Social capital: who you know with expertise and resources
• Reputation capital: what you’ve done to warrant a reputation for innovation

The effect of these three types of capital can be multiplied by impression amplifiers that help you gain attention and credibility for your ideas. How exactly are potential supporters influenced by these factors? In academia, we use what we call a simultaneous equation model to describe how these factors work together. Sponsors are simultaneously weighing all these factors: whether you have the innovation skills as a leader to pull this off (who you are as a leader of innovation), whether you are well-connected with others who will need to support your project (who you know with resources or expertise) and whether you have a track record and reputation for innovation success (what you are known for). Potential sponsors may weigh each of these factors somewhat differently, but they consider all these parts of your innovation capital to decide whether to support you and your ideas. These combined parts work together like gears in an engine (which is why we have depicted the figure above as a set of gears). As you get each gear moving, it can have a flywheel effect. The flywheel effect, first coined by management expert Jim Collins, refers to the process of getting a huge flywheel (say, a massive 5,000-pound metal disk) into motion. Initially, attempts to move the flywheel produce almost no movement—it is almost impossible to imagine the flywheel at speed. Then, slowly, the wheel gathers speed, and suddenly the momentum of the flywheel kicks in your favor. You push no harder than during the first rotation, but the flywheel goes faster and faster. Each turn builds on the work done earlier, compounding your investment of effort. Eventually, the huge, heavy disk flies, with almost unstoppable momentum. The innovation-capital engine—with its three gears and the lubricant of impression amplifiers—can propel a person’s innovation capital in a similar way. This analogy is relevant because building your innovation capital starts with small steps that can eventually have big outcomes. Innovation capital outliers—leaders like Bezos and Musk, who are numbers one and two in our ranking—didn’t start out being that different from the rest of us. They accumulated their innovation capital through small steps and then got momentum from the flywheel cycle. Today, they have more good ideas to champion because more people bring good ideas to them; they develop more social connections because of their reputation—people want to know them. And because they have access to a greater number of good ideas and more social connections, they can build their reputation for innovation by launching more innovations. These components are related in a positive way—more of one component leads to more of another (academics call this relationship mutual causality, but you might just think of these components as reinforcing each other). Resource holders simultaneously consider all three components when deciding to give someone their time or resources for an innovation project.

The ecosystem of hazard models and data is rapidly expanding, and this is exciting news for the P&C industry. But with all the hazard data that is being generated, especially during catastrophic events like hurricane and wildfire, comes the need for a more streamlined way to access it. Data quality and modeling is continually improving—more accuracy, better science, higher resolution—which we have witnessed here at SpatialKey working with providers like KatRisk, JBA, RedZone, Swiss Re, Impact Forecasting, HazardHub and a host of others for a number of years now. But while the quality and quantity of data in the market is abundant, some carriers are lagging behind when it comes to accessing and interpreting these models because their systems are disjointed and outdated. Accessing data from disparate sources, or “system hopping,” delays response time and can sabotage a carrier’s downstream customer satisfaction. Three key challenges that insurance organizations face related to the accessibility of hazard data are:

1. Disparate solutions and proprietary formats
2. Access to hazard data during time-sensitive events
3. Ability to compare multiple views of risk

Let’s take a closer look at the impact of these challenges and what can be done to solve them: 1) Problem: Disparate solutions and proprietary formats While data choice is abundant, you may find that you’re still hopping between platforms to access and visualize hazard data and models in the context of your exposure data. A key reason for this is the proprietary nature of some trusted industry catastrophe models. They can drive inefficiencies by limiting where data can be visualized and how. This leads to the necessity of piecing together multiple disparate solutions to fully understand the extent of an event.  Without the ability to quickly and efficiently calibrate views of risk, you’ll be left with more questions than answers. Solution: Rather than visiting multiple platforms to piece together the impact of an event relative to exposures, leverage multiple views of risk via a single platform—while having them in-hand sooner. This way, you can better estimate losses and allocate your event response resources. See also: Effects of Weather Are Gathering Force   2) Problem: Access to hazard data during time-sensitive events Timely procurement of trusted and up-to-date footprints during the course of an event can also be a challenge without a centralized solution to access data and contextualize it with advanced analytics and visualization tools. Data providers like KatRisk, JBA and Impact Forecasting, in particular, are generating their views of risk well ahead of an event and before many other data providers. A comprehensive view of an event means having data readily in-hand before, during and after an event—and preferably via a single solution where you can compare multiple footprints in the context of your portfolio data. Solution: Quickly integrate data from both private and public sources, so you can quickly extract insight from it—and get back to the more important aspects of event response, especially customer outreach. 3) Problem: Lack of ability to compare multiple views of risk The ability to compare multiple hazards and models in one place is increasingly important with complex events like hurricane, which involve multiple perils (i.e. wind, surge, flood) from multiple providers. While more views of risk can be a benefit, you can quickly hit information paralysis. Which model got it right? Which model is closest to my organization's view of risk? As we’re all aware, models and their outputs are nuanced. Decisions come down to identifying the right models and model components that best represent your lines of business, geography and business practices. During Hurricane Michael, for example, our clients had access to event footprints for wind, surge and inland flood around-the-clock from expert data providers. These up-to-date, multi-peril footprints, coupled with our financial modeling, could be evaluated and compared in real time, helping clients understand potential exposure and ready response efforts. This pre-landfall view of Hurricane Michael from within SpatialKey is showing NOAA surge and wind bands overlaid with sample portfolio data. NOAA updates are available approximately every four hours during hurricanes, along with regular updates from expert providers like KatRisk, JBA, Impact Forecasting and others. This enables you to view and compare multiple models from within SpatialKey for a more comprehensive understanding of an event. Solution: A platform like SpatialKey helps you assess if a model is the right fit for your business—enabling you to step through methodology and figure out the questions that models provoke but don’t answer. Hurricanes and wildfires, in particular, expedite the need to evaluate footprints to understand the business impact, as deriving insights is extremely time-sensitive. For example, you may be interested in the geographic boundary of a major flood to get boots on the ground. Or, you may want to understand the gauge readings (rain, river, surge measurements, etc.) in an effort to process and validate claims. It’s time to streamline your access to expert data Event response is a time-consuming effort, and it’s when speed and efficiency are needed most. In the early days of an event, you need to know how you and your customers will be affected and to what extent. But, you may feel like you have no time to get answers to stakeholders—and having to hop between disparate sources only delays your understanding. You need to streamline your access to expert data while providing a centralized location to compare views of risk and visualize data in the context of your portfolio. See also: Turning Data Into Action   Now, with just one place to access and analyze data during time-sensitive events, you have...

• Increased your operational efficiency by reducing (or eliminating) disparate solutions.
• Streamlined your access to the most up-to-date footprints and models.
• Increased your understanding of risk with the ability to compare multiple hazards and models via a single solution.
• Gained an immediate understanding of your actual financial impact (so your stakeholders are happy).
• Focused your resources on what matters most: mitigation and customer outreach.

In Part 2 of this series, we’ll discuss how you can spend less time processing hazard data and more time driving better decisions. 

Insurance company executives are being pressured by board discussions, distribution channel partners and customer service requirements to more aggressively leverage the “shiny objects” that insurtech offers. Artificial intelligence (AI) is one of insurtech’s brightest contributions, and it seems natural for insurers to use advances in AI — including machine learning (ML), natural language processing (NLP) and robotic process automation (RPA) — to leapfrog competitors. Unfortunately, not every insurer is ready for AI or able to take full advantage of the opportunities in this category of emerging technologies. There are, however, several ways insurers can prepare and evolve to a position of strength from which AI can make a strategic business impact. 1. Ditch Dirty Data For a variety of reasons, insurers tend to have a good amount of “dirty” data, rife with inconsistent formats or standards, incomplete conversions resulting from merger and acquisition activity and data transfer from paper files. A proliferation of dirty data can put insurers in the untenable position of sacrificing whatever valuable intelligence may exist in historical files to a “Day Forward” strategy. Insurers looking to prioritize AI projects must invest in cleansing bad data and improving data mastery. Those efforts will naturally include improving access to, and use of, both structured and unstructured data. The “magic” of AI gives the impression this technology is a silver bullet capable of maximizing the value of the unstructured data prevalent in handwritten forms, PDFs, images, email and text messages and social data, which increasingly inundate insurer workflows. However, the organization of clean and available data is a precursor to AI implementation. See also: AI and Results-Driven Innovation   A recent report by Eric Weisberg and Mitch Wein of Novarica, “MDM in Insurance: Expansion and Key Issues,” details the need for insurers to invest more heavily in improving data mastery and hiring for positions such as chief data officer or data scientist, instead of purely tech talent. “Insurers are placing a priority on data initiatives to support their predictive modeling and AI programs,” Weisberg and Wein wrote. “High data quality is imperative for digitization where data is being exposed to outside parties. Existing and emerging data regulations are also driving a need for improved data governance. Chief data officers and multi-tiered data governance organizations are becoming more prevalent as data is increasingly being treated as an asset. Challenges exist with organization, resourcing, process and funding that can stymie the results of well-intentioned data programs.” 2. Cultivate the Right Culture The fast-evolving nature of technology often means insurers are in a fluid state of decision-making about deployment. As innovation further penetrates traditional industry settings and transforms basic processes and products, insurers must decide if the organization’s culture and leadership are truly capable of committing to the journey of transformation, let alone arriving at the destination. New tools, such as AI solutions, will demand new skills of managers who have built careers leading and inspiring people, and who understand the importance of change management to the organization. So, sorting out the boardroom and operational priorities of the CFO and CIO, or the VP of IT and COO, can help ensure solid business cases and implementation strategy for innovation — such as AI initiatives. 3. Prioritize the Policyholder In addition to cleansing dirty data and strengthening internal change management, preparing to better leverage AI should include a re-prioritization around the policyholder and the customer experience. Insurers need more customer-centric processes from the ground up and a reinvention of existing products and processes that treat the policy as an attribute of the customer instead of the other way around. Customer acquisition is notoriously expensive, and insurers face the additional challenge of relating an age-old industry and product to a new generation of consumers. To be successful, the gap between old and new, and between company and customer, must be narrowed substantially. AI can aid such efforts through innovations such as natural language processing (NLP), which recognize information included in voice conversations or recordings and then quickly and accurately deliver relevant policy files or information. Chatbots can also improve the speed of customer service interactions, and ultimately the speed at which policyholder concerns are resolved. Claims service is good example of a process in which insurers are already starting to see the benefits of incorporating AI solutions, and are using this technology to do everything from reporting first notice of loss (FNOL) to initiating claim processing, or even deploying an adjuster, if necessary. See also: Future of Claims: Automation, Empathy   Defining Goals As insurers prioritize spending on AI initiatives and implementations, the danger is ignoring persistent shortfalls in important areas — such as data mastery, operations and even underwriting. And, it is important to recognize that innovation implemented in the form of an AI solution alone is not, and never will be, a viable strategy. AI can be an enabler of a strategy. But without clearly defined goals and a flexible operating model capable of supporting an evolving and demanding policyholder portfolio, even a successful AI implementation can end up as no more than a footnote. Process automation, machine learning and other types of AI initiatives will continue to make for compelling business cases. To realize full potential and benefits, those tools should be focused on winning clients and implementing accessible, 24/7 customer service and operationally optimizating to support competitive differentiation. Cost savings from AI will typically flow as a by-product. But, without leadership, champions who embrace and drive change and organizational data mastery, the AI tools will be underused and unlikely to fulfill the promise of growth and service excellence.