You know you have a great product or service. And you may have lots of facts and figures and benefits to back up why you're the best. But just throwing data at potential customers (even if it's truly impressive data) won't move them to buy. People don't respond to logic. They respond to emotion. That's why you'd better get good at storytelling—fast. Stories create emotion, and emotion is what people remember. Stories help you engage and, more importantly, teach your audience. If you don't tell a good story, your message will be lost in the media jungle. Google processes more than 3.8 million searches per minute. That's a lot of people looking for answers. This is happening because the way people buy has changed. People no longer respond to outbound tactics like spamming and cold calling. Instead, they research products and services and find what they're looking for on their own. The message for companies is clear: You must provide lots and lots of content that's engaging and persuasive enough to pull in readers and win their business. This is called inbound marketing, and it's the way businesses today "get found"—by helping, educating and entertaining prospects with valuable, relevant and consistent content. Content pulls customers through the four stages of HubSpot's Inbound Marketing Methodology: Attract, Convert, Close and Delight. In other words, you create and share content—through blog posts, emails, videos, case studies, guides, etc.—that attracts the right people to your site, converts them into leads, helps close them into customers and delights them so they'll become promoters of your brand. Your goal is to make a human connection, and storytelling is how you do this. It's about resonating with people who need your help and guidance. A well-crafted story helps you create contrast between choices. It helps prospects make sense of the decision they're about to make, whether it's deciding on a product or service or making a purchase. See also: To Shape the Future, Write Its History   Here are some tips for discovering the story you want to share with the world. First, know what your story is not. It's not data and assertions about ROI. It's not just your business's history. It's also not cliché, and it's not what everyone else is saying. Sure, you may think you provide the best customer service in your industry, but that's not your story. Storytelling is about standing out, not blending in. Focus on your why.  Ex-advertising executive and author Simon Sinek is known for his Golden Circle concept. The Golden Circle is all about starting with why. Sinek says most people communicate by starting with what they do and eventually work their way back to talk about how and why they do what they do. But unique and successful companies like Apple or Google communicate with an "inside-out" type of thinking. They start with the why and only then do they talk about the how and what portions of what they do. Know your characters.  All stories have characters. With content marketing, the people—or characters—are your readers. Good storytelling can't happen without valuing and understanding your audience and responding to their wants and needs. When potential customers can get the answers to their questions and see themselves as characters in your story, they'll be more likely to use your product or service and experience the happy ending you offer. Choose your point of view.  While keeping your buyer persona in mind, you should also determine the point of view your story will have. Will it be first person, second person or third person? There's no right or wrong option. It will depend on your buyer persona, the story you're trying to tell and the format of the story. In the first-person point of view, the character is you. When you say, "I saw this," or, "I learned that," you're speaking in the first person. This type of language is more confessional. It can help you establish a personal connection with the reader or build authority. Try using first person when there's a known person, an author, behind the content. This could work for a blog post, video or even an e-book if the author is noted. In the second-person point of view, the character is your audience. It's when you say things like, "You'll see," or, "You'll learn." When using "you" language, it's important to understand your buyer personas and know their pain points and goals. Tell the story in a way that shows empathy. The third person is the "he said/she said" type of language. Case studies about your customers are a good example of using the third-person point of view. These stories can be fictional or nonfictional. Present, and resolve, your conflict. Once you know who the characters are for your story, it's important to understand the conflict they face. If your story lacks conflict, you're probably not telling a story. Instead, you're telling a pitch, a tagline, a unique selling point or a plain statement. This approach won't resonate with your audience, and from a content marketing perspective it won't get you views, shares, conversions or customers. You need to understand the buyer's journey and the conflicts they might face at each stage. What problems are your buyer personas facing in the awareness stage? Those are the conflicts that should be in your story. Wistia is a good example. It is a brand that provides professional video hosting. Its purpose is to empower everybody to get more out of video, and all of its content and storytelling—which is done through funny, engaging educational videos along with blog posts, guides, help articles and webinars—circles back to this purpose. One blog post is titled "Improve Your Audio: How to Reduce Echo in Your Video." In this case, the reader's battle with echo is the conflict, and it's stated right there in the headline. The rest of the blog post explains how to resolve the conflict. Finally, get to the resolution.  Where there's conflict, your audience will naturally want some sort of resolution. It should wrap up the story but should also clearly call your audience to action. It should fulfill the story's purpose. For content marketing, a resolution could be next steps or even a call to action for more content. Either way, don't leave the audience hanging. Find a way to connect to your audience on an emotional level.  TOMS is a slip-on shoe company that focuses on spreading social good. Here is its powerful story: Everyone needs shoes, but not everyone has the money to pay for them. So, with each product you purchase, TOMS will donate a pair of shoes to a child in need. This strikes an emotional chord with their audience and compels them to buy. This is an example of how a shoe retailer created a much bigger story that makes their customers feel like they're changing the world by simply purchasing a pair of shoes. And they've sold more than 75 million pairs of shoes, which means they've also given over 75 million pairs of shoes to children in need. Find a way to infuse your story into every piece of content you create. Storytelling is the perfect way to help readers begin the journey from stranger to customer, and it can deepen your relationship with your existing clients. Remember, people want and need to feel connected. If you tell the right story, you can capture their attention, connect with them emotionally, and win their loyalty. Justin Champion is the author of "Inbound Content: A Step-by-Step Guide to Doing Content Marketing the Inbound Way," which can be ordered here. 

The 2018 hurricane season opened with the arrival of subtropical storm Alberto on the coast of Florida. Natural disasters such as these regularly imperil human lives and trillions of dollars of infrastructure. Although we can’t stop them, we can limit their financial repercussions through the development of more accurate predictions based on an updated approach to modeling catastrophic risk. The Flawed Assumption Stationarity is the name for the concept of data remaining unchanged—or stationary—over time. When applied to climate science, it refers to the assumption that the earth’s climate is not changing. The vast majority of climate scientists believe the stationarity assumption is incorrect, and any approaches based on this assumption are fundamentally flawed. Yet traditional catastrophic climate risk models are built on the assumption of stationarity. They project the future based on past statistics and the assumption of a static climate. Insurers actually use this approach with reasonable success for regional, national and international insurance policy portfolios. However, when stationarity is applied to risk analyses for specific structures or large commercial properties, the model breaks down. Localized Assets The problem is that risks to localized assets are not homogeneous across regions and properties. Localized predictions require data that accounts for the dynamics of the local environment. Those dynamics include not only a changing climate but human-engineered alterations, as well. Simply breaking ground for a new building affects potential flooding scenarios. To accurately assess and mitigate potential risk, developers, municipalities and insurance companies need models for the individual block and street and are not constrained by stationarity. Creating a dynamic model that collects and analyzes data with such localized resolution is not a simple matter of “downscaling” old methods. It requires a different strategy and discipline, with single-site analysis as a core objective. See also: Role of Big Data in Fighting Climate Risk   Risk Modeling Reimagined Incorporating natural and human-architected factors in a dynamic, integrated model is fundamental to an asset-focused solution that delivers accurate, actionable information. Such a solution requires comprehensive and current data, powerful big data analytics and a flexible design that can easily incorporate new modeling techniques as they become available. At Jupiter Intelligence, our solution is built on a cloud-based platform designed specifically for the rigors of climate analysis and links data, probabilistic and scenario-based models and advanced validation. ClimateScore runs multiple models based on a changing climate, such as weather research and forecasting. ClimateScore’s models are continuously fine-tuned using petabytes of constantly refreshed data from millions of ground-based and orbital sensors. Novel machine learning techniques reduce local biases of scientific simulations and help the system continually improve as new observations become available. Forgoing stationarity and adding the flexibility of a cloud model, current data from multiple sources and state-of-the-art analytics, machine learning and artificial intelligence technology produces asset-level predictions that are accurate from two hours to 50 years in the future. Case Study: Miami Understanding how developed Miami’s coast has become with localized data down to the individual block and street can help insurance companies, municipalities and developers assess the potential risk and determine cost-effective solutions. Engineering firms need this data to evaluate the potential effects of flooding at a particular site and simulate how effective individual coastal protection measures are in protecting properties and neighborhoods from flooding over the life of these structures. Public agencies also need this granularity to figure out how vulnerable their assets (ports, airports, transit, waste water treatment and drinking water facilities) are to a changing climate. Similarly, private entities want to assess exposed assets (substations, buildings, generators and data centers) and critical systems that may need to be redesigned to handle changing conditions. One critical condition to evaluate is the capacity of the electrical grid to handle peak demand during longer and more intense heat waves. See also: Low-Risk Doesn’t Mean No-Risk  New Risk-Transfer Mechanisms Stationarity-based catastrophic risk models were never intended to assess risks to specific assets. To mitigate asset-level risk, all aspects of the private sector, as well as government bodies at the international, national and local levels, must make informed decisions based on accurate, current, highly localized data. Property values, liability risk and lives are at stake. With dynamic models, current data and modern analytics, mitigating risk is feasible. This type of information resource also will support new risk transfer mechanisms, including private insurance—and help reform obsolete mitigation strategies. This article was originally published at Brink News, here.

There’s been plenty of discussion in all sorts of forums about how artificial intelligence (AI) could undermine jobs in many big industries. AI can certainly improve efficiency and productivity. The big benefit of this technology, however, is its ability to enhance the performance of workers. AI has huge potential to assist workers in all facets of the insurance industry. Underwriting, especially, offers great opportunities for workers and intelligent machines to collaborate.  Underwriters are having to contend with a multitude of new risks, many of them highly complex and unfamiliar. What’s more, underwriters must also manage an abundance of new data sources. These additional streams of data are certainly a boon for insurers. They can provide sophisticated insights, often in real time, into a wide variety of risks. Managing this deluge of information, however, is becoming increasingly challenging. Recent advances in AI can help underwriters manage their increasingly complex workloads as well as improve their decision-making. Our research shows that most insurers have been slow to apply AI to their underwriting processes. They still rely heavily on large teams of underwriting professionals. Unfortunately, many of these underwriters spend much of their time performing mundane tasks such as manually entering data into online applications. We found that most underwriters spend less than half their time processing core information. Furthermore, less than a quarter of their time is spent selling or engaging with brokers. See also: Innovation Imperatives in the Digital Age   AI can help underwriters work far smarter. It can free them to focus on high-value activities and help them make faster, more accurate decisions. Already around 33% of insurers are starting to systematically harness the data they receive from multiple sources. By using AI applications such as intelligent data solutions, these organizations could gather and organize structured and unstructured data from a wide range of internal and external sources. The data could then be aligned according to the requirements of the insurers’ underwriters so they could quickly assess its importance. About a quarter of the insurers we surveyed are implementing intelligent processes in their organizations. By applying such AI processes to their underwriting, these insurers could improve substantially the performance of key workers. Such applications could include:

• Using cognitive robotics to sort and address basic service requests from agents.
• Deploying intelligent agents to respond to queries from agents and customers and provide them with basic information.
• Introducing self-adjusting win-probability calculators that help underwriters prioritize their tasks.
• Employing smart anomaly-detection systems that identify changes in renewal requests that might require an underwriter’s attention.
• Applying an intelligent demand-analysis system to identify potential new products.

These applications are all likely to enhance an insurer’s underwriting capability. As the applications constantly learn and improve with experience, their contribution will increase substantially. See also: Cloud Takes a Starring Role   In my next blog post, I’ll discuss how insurers should prepare their workforces for the introduction of AI. Until then, take some time to look at these links below.

• Accenture Technology Vision for Insurance 2018
• Realizing the full value of AI
• Model behavior. Nothing artificial.

The case for omni-channel capabilities is compelling. We are in an age where customers have many options for communicating. And the companies that can best provide them with capabilities to connect anytime, anywhere, via any method will increasingly be the winners. A recent personal experience highlighted both the potential and the pitfalls of providing omni-channel capabilities. Offering a variety of options can lead to increased customer engagement and retention, but poor execution can have the opposite effect.

On a recent day, bad weather was causing havoc for airlines and travelers. In the space of a few hours, I had four different itineraries due to delayed, canceled and re-routed flights. As a frequent flyer, I regularly rely on automated alerts regarding flight status, extensively use the airline app and leverage the call center to address significant changes or problems. Along the way, I also get confirmations or alert messages via e-mail and check the flight boards at the airport. All of these are great options for the company and customer to communicate. When the system works well, it is quite useful and gives me a leg up on other travelers with advance notice on changes.

Unfortunately, it does not always work well. On this challenging day, the various channels were hopelessly out of sync. I was getting automated calls from the airline giving me flight updates for flights that I was no longer booked on. The airline app indicated that I was still booked on yet a different (earlier) flight. I received e-mails about flight changes that had already been superseded by new flight changes. It was confusing, to say the least, and a frustrating customer experience.

See also: A Management Guide to Omni-Channel

This example goes to show that near real-time synchronization is not always good enough. Actions do not need to be updated across all channels within seconds, but delays of 30, 60 or 120 minutes are unacceptable. You may be asking what this has to do with insurance, an industry that typically has interactions with customers only a few times a year. The answer is: If you are going to pursue true omni-channel operations, the system needs to work – and it needs to be real-time.

As the world becomes more digital and more connected, the frequency of interactions with customers will increase dramatically. Smart homes/buildings, wearable devices, connected vehicles and other rapidly emerging solutions offer great potential for the insurance industry. However, one implication is that the omni-channel environment will become even more complex, and the demands for real-time actions will increase. Imagine reacting to an alert from a smart home device regarding an overflowing sump pump. Not only is quick action required, it must also be synchronized with the homeowner’s app and any phone calls to or from agents or the insurer. Communicating frequently with customers to partner in risk management, improved health and well-being and financial management is the future of the industry. And those customers will want to communicate in every way imaginable (including talking to live human beings).

This is a cautionary tale. One can imagine the kinds of scenarios just described regarding travel delays if they were applied to insurance customers’ problems – and the same or similar negative effects that such disastrous communication could have on them. Omni-channel capabilities will have to be coordinated in context and in time to provide true customer satisfaction.

There’s a lot of it about. Insurtech and technology, that is. New ways of doing stuff. Breaking traditional distribution models and deconstructing established supply chains. Who could not be excited? But there’s another side to this coin, and that’s the issue of established practice. Insurance isn’t a new gig, like telematics, but something that’s been around for three centuries. Some might argue even longer, as there are records of even the ancient Egyptians sharing and aggregating risk. Protecting the few by collaborating with the many. Over the centuries, insurance hasn’t been an easy ride. What do we mean by appropriate compensation, or, in insurance parlance, by the principle of indemnity? How to deal with those at fault, or, in insurance language, the matter of subrogation. See also: Where Will Unicorn of Insurtech Appear?

But in the old way of doing things, we all knew where we stood. Insurance contracts had evolved over decades, and where there had been differences in interpretation the legal system had sorted things out for us. There was a sort of certainty and framework to our business and a more certain relationship, even if the topic of trust remains contentious -- the level of trust between policyholders and carriers has always been low, despite a degree of contractual certainty.

Now, here we are in a Brave New World of insurance. Things will never be the same because of technology, the experts say. Some say insurtech is mainly just about new distribution channels, customer management and operational efficiency, but that leaves the rest of the insurance proposition.

It feels like we're throwing a ball onto a sports field and asking the two competing teams to sort out the rules for themselves.

Will there be winners and losers? Of course. The winners will be the legal profession, which will spend years, perhaps, discussing where the liability for death rests as a result of a driverless vehicle incident. Was it the manufacturer - as a product liability issue? Was it the occupant of the vehicle - extending the concept of occupiers liability? Was it the system administrator, which ran the system and which surely must be involved somehow? Maybe even the victims themselves: "Don’t you know you need to be more careful, with all these unmanned gadgets all around us?’"

We can’t all just contract out of responsibility. The proverbial buck must rest somewhere.

Think forward a few decades. Let’s accept that the insurance industry will have been re-engineered and reimagined, with robots, chatbots and wobots. Let’s assume that physical risk is calculated in a more granular way and that underwriting risk management is absolutely aligned to the risk appetite of a carrier. And we have somehow managed to be proactive, to have better responsiveness to climatic change and everything else. And ubiquitous devices provide us with bottomless barrels of information, from which our systems draw insight through advanced analytics.

See also: 3-Step Approach to Big Data Analytics

Someone, somewhere, will need to address the question -- what does all this mean contractually to the insurance industry? After, all isn’t insurance just no more than a contract, between two parties? Or was that concept somehow lost, somewhere inside the Innovation Hub, or among the bits and bytes of technology?

Isn’t it time that someone slowed the momentum of change and had a real hard think about the legal implications for insurance?

At a time when innovators are trying to start with customers—not with our old ways of doing business—and work backward to what products, processes and systems should be, J.D. Power reports that the customer experience with auto insurers has made marked progress. Satisfaction with auto insurers has actually risen even though prices have been climbing steadily.

What's the secret?

J.D. Power cites increased digital interaction with customers, especially for monthly billing. The firm says: "Customer satisfaction is at its highest when customers take care of transactions themselves and save the high-value interactions for live channels." The firm says that 73% of those customers surveyed said they wanted to verify payment receipt digitally, that 70% wanted to pay digitally and that 66% wanted to order proof of insurance cards digitally.

Underscoring the interest in more digital interactions, J.D. Power says that 10% of those surveyed said they participated in usage-based insurance programs, up from 8% in the surveys last year and the year before.

In general, the firm says customers credit auto insurers with being better able to interact via multiple channels, ranging from a face-to-face meeting with an agent to a fully digital transaction executed directly with the insurer, and appreciate the "omni-channel" approach.

The firm concludes: "The auto insurers that increase customer satisfaction across all facets of the customer experience make price just one part of the overall relationship.” (The full summary is here: http://www.jdpower.com/press-releases/jd-power-2018-us-auto-insurance-study)

My take:

The point about self-service is key. It may seem counterintuitive that customers want you to do less and them to do more for themselves, but, let's face it, companies aren't much fun to deal with. Customers are told they need to provide their account number, understand many things about your processes, correct errors that companies make in data entry, listen to bad music or obnoxious sales pitches if they've called in and are on hold, etc. Who needs it? Customers in all industries have consistently shown that they'd rather handle interactions digitally while sitting in front of the TV or listening to music. So, help your customers help you by having them take as much work as possible off your plate.

J.D. Power sounds a bit too optimistic to me both about how much progress auto insurers have made and about how much more loyal customers will be despite rising prices. It's still tough out there, and insurers have a long way to go.

But progress is progress, and we should all celebrate gains when we see them.

Have a great week.

Paul Carroll
Editor-in-Chief

Based on the trend toward value-based health insurance and reimbursement, health benefit plans are being designed to reduce barriers to maintaining and improving health and to promote higher-value healthcare services. Value-based reimbursement requires providers to track and report a host of adverse events and population health measures, including biometrics, patient engagement and other measures required to demonstrate quality performance. Unlike the traditional fee-for-service model, value-based care attempts to align incentives of providers to deliver the right care in the right setting in lieu of maximizing the revenue of each encounter by delivering more services. Providers receive incentives to use standardized, evidence-based medicine, engage patients, upgrade health IT and use more advanced data analytics to optimize their clinical and financial performance. When patients receive more coordinated, appropriate and effective care, providers are rewarded. Accessing Care Quality and Safety Data Plan sponsors and their benefits consultants or brokers who advise them need access to information about care cost, along with the quality and safety performance of those hospitals and physicians delivering care to their plan members. See also: Taming of the Skew in Healthcare Data   Quality measures are essential in optimizing the benefits of value-based models for all stakeholders. Success for all stakeholders depends upon how well healthcare providers can manage quality of care within tighter financial parameters. This presents an opportunity for benefits consultants and brokers who are well-positioned to act as trusted advisers in educating and defining how best to design benefits plans to optimize for value. As educators and advocates, they can guide plan sponsors toward partners who will help them evaluate provider quality and safety. Research shows that many U.S. employers that offer health insurance to employees are unfamiliar with objective metrics of health plan quality information. This gives benefits consultants and brokers an opportunity to outline the advantages of evaluating hospital quality to ensure that plan designs and benefits options include only high-quality hospitals and physicians who provide services at the lowest costs and encourage plan members through incentives to avail themselves to this narrower group of providers. The Challenge: Hospital Ranking Variability The significant challenge is the prevalence of numerous hospital quality rating methodologies. Even the slightest differences in adjustment methodology, data source, time period and inclusion/exclusion rules can produce differences in the hospital or physician ratings. This variation makes it more difficult for hospitals and physicians to prioritize and improve the quality of care delivered. For instance, hospital ranking organizations, such as U.S. News & World Report, Healthgrades, Centers for Medicare and Medicaid Services (CMS) and Leapfrog, reflect substantially different results, fostering confusion to those less literate in healthcare analytics. In 2016, CMS gave 102 hospitals its top rating of five stars, but only a few of those were considered as the nation’s best by private ratings sources such as U.S. News & World Report or viewed as the most elite within the medical profession. First-tier academic journals like JAMA expressed deep concern about the lack of academic credibility in the methods used to assess performance and aggregate the conclusions into a single rating across many different measures. Plan sponsors and their benefits consultants or brokers must educate themselves on assessing provider quality. While there is a myriad of rating services, many do not include elements essential to a precise and comprehensive assessment of providers. See also: Healthcare Data: The Art and the Science   Ratings approaches that use reputation or self-reported data should be considered less reliable than objective outcomes measures using patient level claims data. Additionally, hospital overall surveys or patient reported outcomes do not offer a valid basis for comparison. It is also not possible to use a single outcome measure – for example, risk-adjusted mortality -- as a proxy for all outcomes like complications or readmissions because provider performance varies widely across measures. For a comprehensive assessment, all available measures should be incorporated for a specific clinical category. Lastly, aggregating outcomes data into composite scores must be scientifically sound. As more employers seek greater value for their healthcare dollars, and as benefits consultants and brokers continue to pursue opportunities to help them reduce the upward cost spiral, quality ratings are an important first step toward realizing these goals and advancing the quest for improved employee health.

One of the outcomes of economic and technological changes has been the rise of on-demand insurance products, offered both by insurtech startups and incumbents alike. This includes products with continuous underwriting attributes, microinsurance products and insurance offerings for workers in the gig economy. These offerings aren’t typically grouped together, but they share an on-demand aspect that wasn’t required or technologically possible in the past. Continuous underwriting refers to the use of regularly updated (and possibly real-time) policyholder data to rapidly determine consumer risk and adjust policy terms and prices accordingly, as opposed to traditional term-based updates and renewals. Some forms of continuous underwriting have been around for a long time (example: pay as you go Workers’ Comp, with monthly updates based on submitted payroll) but now has applications to many lines. Microinsurance refers to coverage of smaller risks via rapid underwriting; including on-demand products like travel or event insurance, renters’ insurance broken out for specific high-value household items or pay-per-mile auto coverage. Gig economy insurance is most familiar to those outside the insurance space: as more and more freelance and “gig” opportunities like Uber and Postmates emerge, carriers are developing products to keep these independent contractors covered in a part-personal, part-commercial hybrid coverage. See also: On-Demand Insurance: What’s at Stake   While these three arenas of modern insurance might seem disparate in their final forms, they are emerging today due to a new consumer-focused approach to product definition and the connected technology necessary to allow a real-time approach. This foundation for all of them is built on three pillars: Data: On-demand insurance requires data, if not in real time then something close to it. If insurers are only getting updates as to policyholder risks and scheduled items after an end-of-term audit, then only a traditional approach will work. But as connected technologies and the Internet of Things have created a continuing pipeline of data, a new approach emerges. Insurers now have the ability to tap into discrete data points about coverages times and risks in an automated fashion, including: When is someone driving their car for Uber vs. for personal use? When is a business stocking high amounts of valuable goods? What is monthly payroll for workers’ comp? Product: It’s not enough to have access to the data. Insurers can’t just adjust rates on the fly. Instead, they need to take a consumer-first approach to modeling their insurance product. This means the restructuring and sale of a product with a variable pricing agreement and a flexible term. Done properly, this will allow the insurer to have the most insight into the collective risk and allow the consumer to have a transparent product that covers them for exactly what they need when they need it. Systems: Just because the data is available and the business has rethought the product structures doesn’t mean the infrastructure will be able to support it. On-demand products mean real-time web service calls and at least some component of automated underwriting decisions. Variable rates mean a rating engine that can calculate new rates on the fly based on updated risk info as well as a billing system that can adapt to variable billing amounts and dates. Without flexible and agile core systems, an insurer can’t roll out new products that behave in nontraditional ways. Insurers may be able to make progress with an on-demand offering even if they only have one or two of these pillars. Workers’ comp insurers, for example, have offered pay-as-you-go for a long time via manual form submission. But to make new products viable for a mass audience—and to compete with the consumer-driven ethos of Silicon Valley startups—automated data needs to be simple and convenient to turn on and off. This might take the form of a mobile app with a button to turn a microinsurance product on or off or perhaps the form of an automated data feed to a third-party system like payroll. Conversely, all three pillars are valuable to an insurer even if it hasn’t fully embraced an on-demand approach to their products. See also: Reinsurance: Dying… or in a Golden Age? Real-time data allows an insurer to understand its overall risk profile at any given moment and to make decisions and new sales and renewals. If, for example, you are selling a commercial liability policy and have up-to-date info about a business’ risks, it’s helpful even if individual policy pricing isn’t affected. In fact, this is how automotive telematics typically works: Auto insurers are gathering masses of data that demonstrates real-time risk and driving behavior, but they aren’t using it to do continuous underwriting/rating. Likewise, rethinking a product structure to take a more consumer-focused approach can happen even within the constraints of traditional insurance offerings or without real-time data. And, obviously, having modern and flexible core systems allows new product rollouts, better automation and digital interactions regardless of what products are sold. New insurance products like microinsurance and continuous underwriting aren’t just about gathering data or having a modern core system. Rather, they are based on a multi-faceted approach: understanding risk in a semi-real-time way; selling a different type of product; and having the core systems to handle it.

Wearables continue to be the hottest topic in smart technology, because of gadgets like Fitbits, Apple Watches and Nike Fuelbands. But what about a wearable that uses big data to revolutionize workplace safety? In a world where almost 1,000 workers don’t come home each day due to workplace injury, understanding how workplace incidents happen and taking steps to prevent future injuries should be a company’s top priority. Insurers want to provide the most efficient workers’ compensations and P/C policies, and now they can from the data and machine learning of wearables. Wearables are providing efficiencies in gathering data that can then be processed to provide insights for workplace injury trends. Automated collection of individualized worker safety data at scale is far more efficient than the traditional observation techniques used by safety experts to collect risk data. Wearables don’t require employees to log information or have their cell phone constantly handy, and they offer a seamless information transfer between users, especially important in industries with high employee turnover rates. At MākuSafe, we’re developing a wearable solution that collects and tracks environmental data, which is processed through MākuSmart, our cloud-based machine learning platform, to help manufacturing facilities build a culture of safety. See also: Workplace Wearables — Now What?   So, we understand that wearables are essential for the safety management of an organization. But wearables can provide data just as valuable to insurance carriers. Manufacturing companies and warehouses across the world are losing time and money on avoidable safety hazards and compensation. Data from workplace wearables creates remediation steps to help streamline reducing worksite risk and allow carriers to generate tailored advice for policies and more efficiently justify premiums. IoT capabilities fill this picture in even further, with the ability to alert safety managers to potential risks or even take automated steps to help mitigate risks based on identified trends. With insurance companies often only having limited visibility into the risks policy holders’ workers are experiencing, IoT devices give risk reduction professionals the eyes and ears they need to understand what environmental conditions could be contributing to worker hazards. That means quicker intervention when data shows leading indicators of risk are present, instead of waiting for an injury or claim. Armed with this more complete picture of workplace risk, thanks to more accurate and precise trend data, insurance carriers can target, select and price risk more specifically for policyholders and accelerate time to value on policies. The individualized view of risk permits safety and risk mitigation experts to precisely prescribe remediation steps that are specific to worker risks and better measure the remediation efficacy. None of this data is biometric—rather, workplace wearables like the one from MākuSafe track the environment around an employee, not from the employee. It is intended to generate a 360-degree view of a worker’s risk exposure. Through data analytics and machine learning, wearables can transform from an informative personal health-monitoring device to an essential workplace data tool, without invading employee privacy. See also: The Case for Connected Wearables   The predictive value of individualized workplace safety data can clearly expose risks before they turn into an injury. With this in mind, insurance companies should be looking for companies like MākuSafe to provide solutions for their manufacturing clients, while warehouses and manufacturing companies should be jumping at the chance to test these money/time/life-saving devices. By building a strong partnership between data-driven intelligence, workers and the resources that can be deployed by insurance companies and other safety providers, workplace risks can be reduced and, ultimately, more workers will make it home safely to their friends and families each day.

Warren Buffett’s caution about underwriting cyber-insurance put the spotlight on one of the big challenges facing carriers today – how to address a slew of new insurance risks. The Oracle of Omaha told shareholders at the Berkshire Hathaway annual meeting that he didn’t want the group’s insurance business to pioneer cyber-cover because the risks were largely unknown and potentially too big. Berkshire Hathaway might write some cyber-policies to stay competitive, Buffett added, but it would not be among the top three providers in this market. Underwriting complex new risks such as cyber-insurance, as well as meeting the rising demand for cover for other risk-heavy occurrences such as natural catastrophes and corporate fraud, promises substantial revenue for carriers. Global premium revenues for cyber-insurance, for example, could hit $7.5 billion by 2020, according to researcher Statista. Cover related to digital products and services could also yield healthy additional income. The new revenue streams are welcome news for many insurers that have watched income from traditional products plateau in the past few years. However, as Buffett points out, venturing into uncharted territory can be hazardous -- especially when we don’t know the scope of the hazards. Catastrophe cover, for example, which must now contend with uncertainty related to climate change, cost U.S. insurers dearly last year. The effects of three major hurricanes, Harvey, Irma and Maria, as well as the extensive wildfires in California, all contributed to a spike in underwriting losses. The net underwriting deficit among U.S. property and casualty insurers leaped from $4.7 billion in 2016 to $23.2 billion the following year, according to a report compiled by research firm ISO and the Property Casualty Insurers Association. Insurers are not only being forced to make calls on new types of risk. They must also handle the growing complexity of the underwriting required for some of their established offerings. The spread of corporate ecosystems and supply chains across many varied countries, for example, has heightened the complexity of commercial risk assessment. So, too, has the rise in trade and business regulations imposed by governments around the world. What’s more, insurers must also accommodate a flood of new data streams. While these additional sources of data provide valuable insight into commercial risks and consumer behavior, they also compound the complexity of insurers’ underwriting systems and processes. To meet the rising challenge of new and more complex underwriting requirements, insurers need to get a lot smarter. Improving workers’ skills and hiring more talent won’t be enough. Insurers need to deploy intelligent technology. Only by using artificial intelligence (AI) will underwriters be able to manage the new, complex risks that are confronting them. Our research shows that more than 75% of insurers plan to use AI to automate tasks in the next three years. Many of these applications are intended to improve efficiency and productivity. The big gains in AI, however, are likely to be achieved by using this technology to improve decision-making. In my next blog post, I’ll discuss how advances in AI can help underwriters make smarter, quicker decisions. Until then, have a look at these links. I think you’ll find them useful.

• Accenture Technology Vision for Insurance 2018
• Realizing the full value of AI
• Model behavior. Nothing artificial.