We are in the midst of the shift from the information age to the digital age, which is realigning fundamental elements of business that require major adjustments to thrive, let alone survive. As we noted in our new report, Greenfields, Startups and InsurTech: Accelerating Digital Age Business Models, new greenfield and startup competitors are rising from within and outside of every industry, including insurance, to capture the post-digital age business opportunities of the next generation of buyers. By shifting to meet the forces of change, these companies are positioning themselves to be the market leaders in the post-digital age. Those that do not make the shift risk not only the loss of customers but also market share and relevance in the coming new age of insurance. See also: 6 Charts on Startups, Greenfields, Incubators   Sometimes, the next big thing isn’t easy to spot. The disruption of the insurance industry is in the early days, so predictions are difficult. Will the new greenfields and startups become the next market leaders? If history is a guide, the answer is yes … some will. Just consider Progressive and how many dismissed it early on. Now it is a top 10 insurer in the U.S. Or consider what has happened in other industries with companies that are defunct because they missed the shift:

• Streaming video: Blockbuster failed to see this trend. It filed for bankruptcy in 2010 and Netflix is now worth more than $61 billion.
• Mobile games: In 2011, the president of Nintendo North America suggested that mobile game apps were disposable from a consumer perspective. Today, Pokemon Go has 65 million users. Is that disposable?
• Apple iPhone: Former Microsoft CEO Steve Ballmer reportedly commented that the first Apple iPhone would not appeal to business customers because it did not have a keyboard and would not be a good email machine. Apple iPhone single-handedly disrupted and redefined multiple industries and continues to do so.
• Autonomous vehicles: In 2015, Jaguar’s head of R&D stated that autonomous vehicles didn’t consider customers’ cargo. Since then, Jaguar Land Rover has invested $25 million in Lyft to join the autonomous trend.
• On-premise enterprise software vs. cloud-based SaaS platforms: In 2003, Thomas Siebel of Siebel Systems said Microsoft would roll over Salesforce in the CRM market. In 2005, Oracle acquired Siebel Systems for $5.85 billion. Salesforce’s market cap, in contrast, is more than $60 billion.

Insurance Industry Change and Disruption At no time in the history of insurance can we find as many game-changing events and a rapid pace of advancement occurring at the same time. At the forefront is the increased momentum for insurtech, and the greenfields and startups within, creating high levels of activity, excitement and concern on the promise and potential of insurance disruption and reinvention. When you add it all up, the insurance industry has many characteristics that make it an attractive target for aggressive investments in innovation. First, its size is enormous – based on industry data, it is estimated that premiums written are more than $4.7 trillion globally. Second, it faces multiple challenges that offer opportunities for exploitation by nimble, efficient and innovative competitors. Insurtech advancements and the forces of change see no significant slowdown. The momentum for change that has been building is unstoppable. Industry advancements, cultural trends and IT reactions are gaining speed as they gain strength and a framework for stability and growth. It is pushing a sometimes slow-to-adapt industry by challenging the traditional business assumptions, operations, processes and products, highlighting two distinctively different business models: 1) a pre-digital age model of the past 50-plus years based on the business assumptions, products, processes and channels of the Silent and Baby Boomer generations and 2) a post-digital age model focused on the next generation including the Millennials and Gen Z, as well as many in Gen X. Greenfields and Startups Make the Boardroom Agenda The market landscape is rapidly changing. During 2016, Lemonade launched. Metromile decided to become a full-stack insurer, leaving its MGA days behind. New MGAs entered the picture, including Slice, TROV, Quilt, Hippo and Figo Pet Insurance, to name a few.  Existing insurers made market debuts with new startups including Shelter’s Say Insurance with auto insurance for millennials, biBerk from Berkshire Hathaway for direct small commercial lines and Sonnet Insurance as the digital brand from Economical Insurance in Canada, among others. Add to this the projected shrinking of insurable risk pools due to the emergence of autonomous vehicles, connected homes and wearables and the domino effect of these on other industries, and it’s not hard to imagine a future with traditional carriers fighting over a much smaller pool of customers where only the most efficient, effective and innovative will survive. As a result, discussion surrounding greenfields, startups and insurtech moved into the board room of every insurer and reinsurer trying to understand how to leverage the shift to the digital age and develop strategies and plans to respond. Yet some insurers have a blind spot in recognizing the competition both from outside and within the industry, and the critical need to begin planning a new post-digital age business model. The result is a growing gap between knowing, planning and doing among leaders and fast followers or laggards, which is rapidly becoming insurmountable due to the pace of change. Closing the Gap with Greenfield and Startup Business Models Assuming that most insurers grasp the need for a greenfield and startup mentality to grow, what remains is to aim all efforts toward accomplishing an organizational shift. How do you move your company from the pre-digital age to the post-digital age and close the gap? It requires leadership to build consensus. It requires vision to aim in the most market-ready direction. And it requires a new business paradigm that will allow for change. We must redefine and re-envision insurance to enable growth and remain competitive. While many have made progress in replacing legacy systems and traditional business processes, this is not enough. These systems, while modern, were built around pre-digital age business assumptions and models, not to support the range of needs in a post-digital age model driven by a new generation of customers. Like other industries, today’s insurance startups and greenfields need and want options that do not require investment in significant infrastructure or upfront costs and therefore seek a cloud business platform solution to maximize options and minimize costs and capital outlay. See also: How to Plant in the Greenfields   A modern cloud business platform provides an advantage for greenfields and startups, breaking down traditional boundaries, IT constraints and age-old business assumptions about doing business, while building up the ability to rapidly develop and launch new products and services. The platform is a robust set of technology, mobile, digital, data and core capabilities in the cloud with an ecosystem of innovative partners (many insurtech technology startups) that provides the ability to launch and grow a business rapidly and cost effectively. Will established insurers suffer at the hands of tech-savvy, culture-savvy competition? Some may, but only if they allow themselves to. There will be constant pressure from greenfields and startups to outdo each other in the race to better meet the needs and demands of a new generation of buyers in a post-digital age for insurance. For traditional insurance companies, the need to re-invent and transform the business is no longer a matter of if, but of when.  Insurance leaders should ask themselves: Do we have a strategy that considers transformation of both the legacy business and creation of a new business for the future? Who are our future customers and what will they demand? Who are our emerging new competitors? Where are we focusing our resources…on the business or on the infrastructure? A new generation of insurance buyers with new needs and expectations creates both a challenge and an opportunity that a greenfield and startup business model can capitalize on to incubate, launch and grow. The time for plans, preparation and execution is now — recognizing that the gap is widening and the timeframe to respond is closing.

Many critical infrastructure systems, such as those that control the electric grid, oil and gas refineries and transportation, are now getting linked to the internet. That makes them easier to manage and maintain but also could put them in the line of fire for cyber attacks. I recently discussed the issues involved in upgrading and protecting these critical industrial control systems with Patrick McBride, chief marketing officer at Claroty, a startup that intends to secure the operational technology networks that run companies’ infrastructure systems. A few big takeaways from our conversation: Old systems, new protections When industrial systems were built, sometimes decades ago, no one considered the need for digital protections. “The systems were never designed, especially 10, 15, 20 years ago, with cybersecurity in mind,” McBride told me. Their primary design goals were the safety of the workers and the resilience of the systems, he said. “Security wasn’t even an afterthought. It wasn’t a thought.” See also: How Tech Created a New Industrial Model   Now, a new class of tools is coming online to help monitor these legacy systems. Using behavior analysis and anomaly detection, they are designed to catch intruders early in the attack life cycle. “Monitoring technology is going to play a huge part in this environment,” McBride said. Mishmash of systems leaves exposures Big industrial plants are careful about what they put on their networks, but some are putting wireless and other access points on systems as time-saving techniques to gather data more efficiently. When organizations began to recognize the need for cybersecurity, some traditional IT security vendors repurposed existing technology, McBride said.That didn’t work particularly well, because in the industrial control systems, the networks speak to other kinds of protocols.“You’ve got a whole set of overwhelming business value from pulling data out of those plant systems and being able to provide that information back to the executive,” McBride said. For example, there are a lot of Windows XP machines in industrial environments that keep air conditioning going, or run chemical manufacturing plants and refineries. Potential for escalating industrial attacks In December 2016, attacks on the Ukrainian power grid cut off a fifth of all electrical power in the capital city of Kiev. The purposeful takedown was attributed to Russia. The troubling fallout: Threat researchers around the world have found indications of the type of malware used in Ukraine on other energy and industrial companies’ networks, McBride said, showing that hackers are at least probing for vulnerabilities. See also: It’s Time to Accelerate Digital Change  

But threats from nation-states are only one issue. “There are other categories that people are really starting to worry about. If you combined the ease at which it is to gain a foothold on these networks and the relative ease you can attack these systems, it’s not hard,” McBride said. “You don’t have to squint too hard to say … ‘Terrorist organizations might want to do this or buy expertise to help them do that.’”

This post originally appeared on ThirdCertainty.

WannaCry was a wake-up call. Petya is a wake-up call. Last I checked, wake-up calls were meant to bring about change. After WannaCry, we saw a massive surge in patching around the globe, not to mention a 22-year-old “accidental hero” in the U.K. who helped halt the malicious software. It’s proof that beating the drum continuously to public and corporate institutions about serious cyber defense tactics doesn’t seem to do the trick, and once again we will see a tangible drop in cybersecurity activity until the next big attack. It will only keep getting worse. See also: 5 Best Practices in Wake of WannaCry   The question is quite simple—why aren’t organizations doing more about this? We witness the answer every day: Most organizational leaders refuse to support their internal teams when asked for procedural change or proper funding for cybersecurity defenses—which cuts their bottom line. In practice, it’s quite easy to see the lack of emphasis given to cybersecurity when it warrants only 3-6 percent of IT budgets, and oftentimes that number includes risk management. Moreover, our community just now is scratching the surface of providing tangible cybersecurity reports to the organizational board level, meaning its level of import is still not equal to that of numerous other reporting requirements. There are strict physical safety measures imposed on numerous industries, like seat belts and airbags, yet we need look only at the current U.S. administration and its public stance on cybersecurity to see an instance of unbelievably insufficient governmental policy. The entire intelligence community and the cybersecurity community that supports the government knows and has known the Russians have sophisticated teams and methodologies that have been used to attack us for years. This administration seems to have turned a blind eye on our national defense given their consistent refusal to admit Russia’s complicity. See also: WannaCry Portends a Surge in Attacks   This makes a bold statement that the White House has no intention of preventing, at a policy level, cyber attacks. There are still gaping holes in the federal CISO and White House CISO positions and we haven’t received any movement in policies or executive orders of any substance. This article originally appeared on ThirdCertainty. It was written by Paul Innella.

Insurance companies have long faced obstacles in optimizing their asset portfolios as the balance sheets of highly regulated, complex operating companies providing various financial products. Traditional one-size-fits-all methods to solve agency problems and hesitancy toward adopting new technologies have held them back. Over- or underutilization of quant models as well as the absence of a clear process to manage multiple tradeoffs have also prevented insurers from achieving optimal returns and customization. On the other hand, investment professionals and third-party managers have avoided insurance company asset management due to strict and multiple layers of industry regulation, and their complex capital/liability structure. The pressure to get better returns, along with structural changes and emerging financial technologies, has caused insurers and asset managers to reassess their strategies and look for ways to transform their old ways of investing. That’s where the practice of Enterprise Driven Investing can help. See also: A New Way of Thinking on Assets   Enterprise Driven Investing (EDI) for insurers is a business management process that attempts to address several pitfalls, improve decision-making and enhance results. The goal of EDI is to achieve a high level of portfolio customization in the most financially efficient manner. This is done through a four-step process. Step 1: Establish the full set of financial variables and set priorities EDI begins by establishing and prioritizing the complete set of financial considerations. The fact that there are multiple business factors that affect each other is the principle characteristic that distinguishes Enterprise Driven Investing from Liability Driven Investing and creates this first step. These considerations include form of ownership, liabilities from a global encyclopedia of risk, actuarially complex policy terms and product options, taxes, liquidity requirements, colliding capital objectives, affiliate structures, competing rating agencies and several regulatory regimes that are rarely coordinated and, in combination, are the most complex in business. EDI’s first step captures the complete set of these variables and then challenges the board and senior management team to establish those that are primary, secondary and less relevant to their organization. Step 2: Design a portfolio objective, and related performance measures, from the financial priorities Insurance asset management in any form is as much a design challenge as an investment one. Careful design of the primary objective is the gatekeeper to successful EDI. Portfolio objectives for these entities are no different than for other portfolios in that they are two-dimensional measurements of risk and return. The definition of each, however, has financial attributes linked to an operating company. Return can be total, net investment income (NII), cash flow, a combination or something else entirely. Risk can be portfolio volatility, CVaR, TVaR, economic shortfall, Solvency II capital charges, etc. Even the best selection will have shortcomings. A poorly conceived objective alone can offset, entirely, the talents of a high-performing investment team. Success in the design phase will occur if four guide rails are in place:

1. Company-specific customization – Investment objectives should be dictated by market segment based on lines of business, ownership structure, scale and domicile.
2. Clarity of timeframe – This should be longer term but explicit (e.g. three years).
3. Proper selection and calibration of constraints. Sensitivity analysis is the radar that is used to navigate through changing circumstances and costs.
4. Establishment of investment skill metrics - Legitimate performance evaluation of both internal and external managers remains one of the most challenging and increasingly important design requirements in insurance asset management.

Step 3: Establish a strategy to meet the portfolio objective with full consideration of the impact on factors not directly expressed in this objective A portfolio objective expresses an insurer’s most important return and risk measurements. The challenge is balancing the portfolio objective with other dynamic financial parameters. One response to this challenge has been portfolio optimization with multiple constraints. While helpful, relying on a single output from these analyses has weaknesses. For example, it introduces black box risk and naïve precision. It also fails to consider important variables and masks the relative significance of various assumptions and financial relationships. As a practical decision-making tool, EDI avoids these weaknesses by highlighting the collateral impact on key trip wires from changes motivated by the portfolio objective. While all companies estimate the changes in portfolio strategy against the portfolio objective, many do not grasp the shadow-pricing sensitivity of the objective to variation in constraints or, conversely, are blind to the impact of rebalancing on the full set of financial variables. For many companies, this sensitivity is both substantial and unknown. Managing sensitivity, to self-imposed limits, in particular, advances EDI from a passive to an active philosophy. Step 4: Explore ways to improve tradeoffs through higher order changes EDI begins by creating a comprehensive set of company-specific financial considerations, then establishes priorities (including the portfolio objective), and forms investment strategy after highlighting relationships between these variables regarding direction and leverage, through sensitivity analysis. In its most advanced form, creative reengineering resets trade-offs to a more favorable state and forms new ones. A few categories for these ideas are summarized below, but the opportunities are by no means confined to these topics:

• Improved capital and tax efficiency – Developing strategies to increase returns, reduce volatility, improve portfolio diversification and reduce capital charges.
• Bifurcation of assets based on line of business volatility rather than asset class volatility – Organizing the balance sheet by reserves and capital based on the volatility lines of the business.
• New approaches to asset/liability management (ALM) - Structured finance experts should create bespoke products that improve ALM, in the same way they have used their expertise for capital efficiency.

See also: How to Manage Strategic Relations  While EDI is the future of insurance company investing, it is also a framework for all investors to manage the tradeoffs of hyper-customization and pure investment efficiency. Also, because EDI explicitly recognizes, rather than avoids, the full set of enterprise variables, it represents a major advancement in balance sheet management from LDI and one-size-fits-all quantitative methods. As such, EDI reveals otherwise hidden paths to significantly better results. Finally, as a stable, but dynamic business management process, EDI principles accommodate the full spectrum of emerging financial theories and technologies.

Recently, I wrote about a startup called Aviva. (My comments were based on an article I read.) Aviva's CEO said, “What’s our long-term goal? To go from ask-it-once to ask-it-never — so customers don’t have to answer any questions at all.” How can coverage be booked without asking ANY questions? Why, using big data, of course. Wouldn’t a better goal be to first ask the necessary questions to assist consumers in identifying their unique exposures to loss, then match those exposures (where possible) with the proper insurance package to minimize the likelihood that a consumer will experience a serious or catastrophic financial loss? At my semi-annual checkups, my doctor asks me a lot of questions. Would it be an improvement if he didn’t ask me any questions? Maybe for his bottom line, but not for mine. Who can’t spare an hour once a year to prevent financial ruin? See also: Forget Big Data; You Need Fast Data   In another blog post, I wrote about the startup Slice, which apparently plans to write on-demand home-sharing and ride-sharing insurance without an application. How? Presumably by using big data, of course. In still another blog post, I wrote about Lemonade, which writes homeowners insurance using a phone app without a lot of pesky questions that are designed to identify exposure gaps of individuals and families. Lemonade, too, seems to be relying on black-box algorithms and our friend big data. Let’s take Slice. It claims: “All the information that insurance carriers ask you is all publicly available. So instead of taking up your time to give us this info, we use our clever SliceBots to collect it.” So, ALL of the information that Slice needs to properly insure all of your unique exposures to loss is publicly available? At one time, I saw a Zillow logo on a startup’s web site. Is that where, for example, homeowners' information might be obtained? Or might such a startup go directly to tax and other records where this information is obtained? How reliable is this “big data”? Is it vetted at all if customers are not asked any questions? Still another startup is Hippo. Backed by a number of investors, including Trulia, this is how Hippo's big data approach works, according to an article from Forbes: “According to the company, with Hippo, consumers can go from quote to purchase in minutes, as quotes are delivered in 60 seconds after answering three simple questions. Customers can get a personalized Hippo quote online, by phone or even through Facebook Messenger. The company leverages technology and data from multiple sources (such as property records, permit filings and aerial photography of roof conditions) to streamline the application process and provide ongoing risk monitoring. By leveraging data, Hippo saves customers time, while also garnering more accurate information that cannot be provided from subjective human answers alone. By cutting out the middleman, more accurately assessing risk and increasing technology efficiencies, Hippo is able to pass savings on to consumers.” There happens to be a home for sale in my neighborhood. Out of curiosity, I checked it out on both Zillow and Trulia. Zillow says it’s a 1-story home, Trulia says it has two stories. Zillow says two-and-a-half baths, Trulia says three-and-a-quarter baths. Zillow says the lot is 1.6 acres, Trulia says it's 0.48 acres. Zillow says the home is 2,968 sq. ft., Trulia says it’s 3,891 sq. ft. Just in the replacement cost valuation of the home alone, think these discrepancies might make a difference in coverage limits? See also: Healthcare Needs a Data Checkup   In my case, I owned a home that was 1,000 sq. ft. larger than the country tax records showed. Over the course of 30-plus years, attic space had been converted to living space, but the records from which “big data” might be drawn were never updated. When discussing this issue in an online forum, one of the participants said Zillow showed his home being 2,400 sq. ft. (the same size in the tax rolls), whereas it’s actually 4,683 sq. ft. Big data is one thing. Big, BAD data is another. Who is vetting the information, bots and algorithms? Certainly not regulators, given the open-arms welcome one startup got from a state insurance department. Is anyone listening? Does anyone care?

One of the most basic lessons you learn in first year business school is the SWOT analysis - strengths, weaknesses, opportunities and threats. And it's a great framework to apply to your business to understand what you do well, what you can improve on and where the greatest threats to your company lie. But how about a SWOT analysis on ourselves? Where are our blind spots? What do you struggle with? Here's a simple framework to give it a go: Strengths: What are your strengths as an entrepreneur? What do you do particularly well? Or, in the words of Chris Sacca, what's your "unfair advantage?" Perhaps you're great with product design. Or perhaps your distinguishing characteristic is your ability to sell. Or maybe you can work a room like nobody's business. Knowing your strengths tells you what added value you can uniquely bring to your business. See also: The Need for Agile, Collaborative Leaders   Weaknesses: You might be a terrible planner. Or you might procrastinate like nobody's business. Or you might dread making sales. You might also feel uncomfortable admitting it or talking about your weaknesses. But unacknowledged weaknesses are business killers. They slowly eat away at the core of your business, with little hope of ever changing the situation. So pay particular attention to weaknesses as you do your personal SWOT -- and be as honest as possible with yourself as you do. Opportunities:  Opportunities can be chances to build on your strengths and rectify your weaknesses - either through self-improvement or by adding additional members to the team with complementary skills. But of course, opportunities can only be leveraged if weaknesses are recognized and acknowledged -- yet another reason that honesty is so essential in the process of conducting your personal SWOT. Threats:  Finally, threats can come from multiple places. Your skills may no longer fit the needs of the business you're in. You might face competition from others who do have these skills -- and if you're unable to acknowledge (and work on) your weaknesses while at the same time leveraging and accentuating your strengths -- you could find yourself in a precarious professional position. Along these lines is the threat that you as the leader might lack the self-awareness or courage to look yourself in the mirror and conduct an honest, self-reflective SWOT analysis in the first place. Doing an honest, self-reflective personal SWOT analysis is useful for anyone at any stage of a career. But it's especially useful for entrepreneurs, who need such a wide-ranging set of skills to achieve their goals and find success in their business. Have you conducted a personal SWOT analysis? If not, what's holding you back? See also: Where Are All Our Thought Leaders?   Visit here to receive my free guide to 10 cultural codes from around the world, and here for my very best tips on stepping outside your comfort zone at work. Andy Molinsky is the author of Reach and Global Dexterity. 

Some of the best innovation success stories are built out of the lessons learned from watching the attempts of others as they either falter or flourish. The following is a compilation of what we believe are the best practices for innovation, through anecdotal and use case examples. In an effort to help and inspire insurers along the innovation journey, SMA has grouped the 10 innovation best practices into three phases and then defined each of the 10 best practices. See also: Innovation: ‘Where Do We Start?’   Top 10 Best Practices for Innovation Phase 1: Getting Started: Begin the Innovation Journey

1. Don’t Be a Lone Wolf
2. Institutionalize Innovation
3. Reframe Business Strategies and Plans
4. Explore the Insurtech Landscape

Phase 2: Gaining Momentum: Learn From Successes and Failures

1.  Have a Champion for Each Cause
2. Pivot, and Then Pivot Again
3. Expand Your Failure Appetite

Phase 3:  Creating Advantage: Leverage Innovation for the Competitive Edge

1. Leverage Customer Insights
2. Innovate Across the Enterprise
3. Make Innovation Continuous

P&C insurers spend as much as 30% of the cost of the product on distribution. That’s a hefty price to pay to get your offerings to consumers, only to have them be dissatisfied with the experience. As consumers demand more convenient options for purchasing insurance, leading P&C insurers have found a way to reduce costs and improve the ease of the buying experience by digitizing manual processes. What’s Holding Insurers Back? What’s holding most insurers back from meeting the speed and performance of a customer experience leader like Amazon? In a nutshell, siloed legacy systems. See also: P&C Core Systems: Beyond the First Wave   We know that insurers that have overcome legacy system challenges to achieve top digital capabilities grow revenue at 1.5 times the rate of less enabled competitors. We’ve said this before, so let’s break it down to discover what’s holding the rest of the industry—those still married to their aging systems and processes—from achieving the same results:

• Sub-prime processes: Consider what it’s like to purchase items from an online retailer. You search for an item; peruse the list of available products; find the one that fits your parameters, and in a few seconds make a purchase. This shopping experience pervades our modern culture, so why should it be any different in insurance? The answer again is legacy systems, as customers must provide reams of data on everything from the type of engine that runs their car to the framing in their house just to receive a price on available coverage. Entering this level of information takes time, adding on costs and generating consumer frustration.
• Product silos: In P&C insurance, everything lives in its own universe. Auto policies operate from one back-office system, homeowners from another, motorcycle from yet another and so on. When a customer wants to purchase multiple lines of coverage, information needs to be entered into all of those systems, requiring separate applications and sometimes separate agents to do the job. Because this data gets entered manually, the work costs the insurer in efficiency and errors.
• Non-standardized data: Given that P&C insurers operate from silos, what happens when P. John Smith, (he likes to be called John) living on Main Street in Scituate, RI, approaches carrier agents for auto and home coverage or even starts the process online himself. For the majority of insurers, information would have to be entered twice, once for auto and once for home, into two different systems. Suppose the agent, or John himself, enters the required identifying data for auto. Then, when it’s time to re-enter the data for the homeowners policy, John or a different agent decides to speed up the process by omitting his first initial, shortening his name to John Smith and his street address to Main St. We now have non-standardized data, where one individual is represented in two different ways across the insurer’s data warehouse. These simple discrepancies make it difficult to locate John when he calls with questions or about his renewal, reducing agent and online efficiency.

In case you didn’t realize it, all of the scenarios above relate to manual or inefficient data handling, something insurers can improve to reduce costs and acquire more customers. Why Digital Reigns Supreme (Hint, It’s Automation) Digital leaders grow faster in part because they eliminate much of the inefficiency and costs that plague their less-enabled counterparts. They also create a more satisfying customer experience, resulting in stronger acquisition and retention. McKinsey estimates that as much as 45% of work activities could be automated today, but let’s focus on the quote-to-issue lifecycle for a moment. This is where most of the manual data entry occurs. Automating the quote-to-issue lifecycle takes much of the chore of data entry off the plate of agents and consumers. With a leading digital distribution platform, the small amount of information that is subject to manual entry is entered only once, while the nitty-gritty details are drawn from verified third-party sources. Applications are completed in a fraction of the time, streamlining the quoting, binding and issuance process, while eliminating many of the manual tasks associated with generating a policy. Insurers can see double-digit error rate reductions, as much as a 70% decrease in data entry costs and a 50% increase in agent efficiency. But that’s only the tip of the iceberg. The real story is in customer satisfaction. When insurers use a digital distribution platform that unites product silos, consumers and agents are able to quote, bind and issue multiple products from a single application. Imagine agents and consumers quoting multiple products in less time than it used to take to quote one. By uniting product siloes and adding application prefill capabilities, a leading insurer has reduced data entry and streamlined the quoting, binding and issuance of products. The result is quote conversion rates of 35% through agency channels and 53% direct-to-consumer. See also: Data and Analytics in P&C Insurance   As consumers unite to turn the insurance industry on its ear, it’s time for insurers to leave the dark ages behind and emerge into the light of 21^st-century distribution. To learn how to evolve quickly and simply, without major upgrades or overhauls to existing systems, download our infographic, Direct-To-Consumer: The Future Of P&C Insurance.

When “Car and Driver” magazine debuted more than 60 years ago (originally titled Sports Cars Illustrated), nobody could have envisioned the approaching changes that would transform life as we knew it – including all things automotive and consumer. Today, the expression “car and driver” suggests a completely different meaning as automobiles are becoming “driven” by software and technology and their owners are becoming passengers – and increasingly we are riding in vehicles we don’t even own but rather share or rent. But while we await our future, current innovations in vehicle and consumer technologies have already emerged to create a transition period full of complex challenges and issues accompanied by potentially significant opportunities for all participants. While much attention is being paid to the emergence of telematics and the connected car, and seemingly endless amounts of investment capital are flowing to the many innovative and promising startups sprouting in this fertile global environment, something even more consequential is also beginning to evolve. Auto insurers and auto makers – once basically adversaries – are beginning to cooperate around many of the related opportunities.   See also: 3 Technology Trends Worth Watching   These two industries, which serve and share a common customer base, have traditionally been wary of one another because they had so many conflicting interests. Carriers insure the people who drive the cars that OEMs make, and, when accidents inevitably occur, liability is frequently brought into question to protect the interests of one from the other. In addition, franchised new car dealers, upon whose success OEMs depend for sales and vehicle distribution, earn significant revenues from selling a variety of related products and services – including warranties and insurance, another area of potential conflict. Finally, when insured vehicles end up in collision repair shops as a result of accidents (which happens more than 20 million times a year), insurance carriers do their best to manage repair costs by encouraging these shops to find and use less expensive parts, which costs OEMs and their franchised new car dealers significant parts sales revenues. And, at a higher level, insurers and OEMs value and fiercely protect their customer relationships and have no interest in sharing them with others.    However, these dynamics are quickly changing as new mobile technologies are rapidly transforming consumer behavior and expectations and as new connected car and automated driver assist technologies begin to present significant new challenges as well as exciting opportunities to both auto insurers and OEMs. It is far from a given that today’s auto market share leaders will enjoy similar shares of future autonomous vehicle sales, and it is equally uncertain as to by whom and how these vehicles will be insured. Tesla is positioning itself to do both. And so the ancient proverb that “the enemy of my enemy is my friend” seems to apply very well here. Evidence of insurer/OEM partnerships, both direct and indirect, is plentiful and growing daily. Insurer/OEM connected car partnerships date back to as early as 2012 and include State Farm/Ford, Progressive/GM OnStar, Allstate/GM OnStar and Nissan/Liberty Mutual. In 2015, Ford conducted a “Data Driven Insurance” pilot program that provided participating drivers with their driver history for use in obtaining auto insurance. In 2017, GM OnStar began offering its subscribers 10% discounts on auto insurance from participating carriers including National General, 21st Century, Liberty Mutual, State Farm and Plymouth Rock.   And data and analytics information providers Verisk and LexisNexis Risk Solutions, which collect data and analytics solutions for use by the insurance industry, have both recently launched telematics data exchanges with OEM participants including GM and Mitsubishi. Consenting connected-car owners have the option to contribute their driving data and seamlessly take advantage of insurers’ usage-based insurance (UBI) programs designed to reward them for how they drive. Other innovative telematics data models include BMW CarData, which allows owners to share customized data with pre-approved third-parties such as insurers, auto repair shops and other automotive service providers. Drivers can obtain custom insurance coverage based on their exact number of miles driven while repair shops could automatically order parts in advance of service appointments. For carriers, existing data pools and analytics tools will become less useful than real-time data streaming from connected cars coupled with increased proficiency in predictive modeling and machine learning. OEM/insurer partnerships can enable both parties to share the costs and co-develop big data mining technologies and advanced analytics methodologies to benefit their respective businesses. Insurers can improve underwriting and claims processes while OEMs can improve vehicle safety, design and performance. Data provided by connected-car devices could be used to initiate claims processing, order damaged parts, triage required collision repair and manage other third-party services (e.g. towing, rental, appraisal) and record accident dynamics as well as occupant placement. OEM/insurer partnerships sharing this data could lead to better claims service and satisfaction and more reliable injury claim evaluation. OEMs could use this data to improve vehicle and occupant safety and could ensure that repairs are performed at properly certified collision repairers and that appropriate parts are used in the repair. OEMs and insurers can partner to offer customers innovative customer experiences, becoming primary points of contact for risk prevention and new hybrid insurance products as well as dealer parts, service and sales opportunities. New revenue sources for both parties could include Intelligent GPS for theft recovery, real-time notifications of traffic and other travel inconveniences, intelligent parking, location-based services, safety and remote maintenance services. Cost duplication from currently overlapping services such as roadside assistance and towing could be eliminated by single-sourcing such services. See also: The Evolution in Self-Driving Vehicles   To be sure, other telematics data business models have emerged that could threaten OEM/insurer partnerships.  In June 2017, BMW and IBM announced the integration of the BMW CarData network with an IBM cloud computing platform that could help as many as 8.5 million German drivers who grant permission to diagnose and repair problems save on car insurance, and take advantage of other third-party services. IBM can also collect data from other OEMs over time, and BMW plans to expand the program to other markets. And technology companies, including Automatics Labs and Otonomo, are seeking consumer consent to sell data through their exchange platforms. While we await the day that self-driving vehicles dominate our roadways – which will no doubt make many of these driver data initiatives basically irrelevant – we have the most pragmatic of all reasons why OEM/insurer partnerships make sense. Participants can mitigate their risk and reduce their investments in these costly but still relatively short-term opportunities as they position their companies for the as-yet-undefined future of transportation and insurance.

In May, I wrote about The Three Phases Insurers Need for Real Big Data Value, assessing how insurance companies progress through levels of maturity as they invest in and innovate around big data. It turns out that there’s a similar evolution around how insurers consume and use feeds from the Internet of Things, whether talking about sensor devices, wearables, drones or any other source of complex, unstructured data. The growth of IoT in the insurance space (especially with automotive telematics) is one of the major reasons insurers have needed to think beyond traditional databases. This is no surprise, as Novarica has explained previously how these emerging technologies are intertwined in their increasing adoption. The reality on the ground is that the adoption of the Internet of Things in the insurance industry has outpaced the adoption of big data technologies like Hadoop and other NoSQL/unstructured databases. Just because an insurer hasn’t yet built up a robust internal skill set for dealing with big data doesn’t mean that those insurers won’t want to take advantage of the new information and insight available from big data sources. Despite the seeming contradiction in that statement, there are actually three different levels of IoT and big data consumption that allow insurers at various phases of technology adoption to work with these new sources. See also: 7 Predictions for IoT Impact on Insurance   Phase 1: Scored IoT Data Only For certain sources of IoT/sensor data, it’s possible for insurers to bypass the bulk of the data entirely. Rather than pulling the big data into their environment, the insurer can rely on a trusted third party to do the work for it, gathering the data and then using analytics and predictive models to reduce the data to a score. One example in use now is third-party companies that gather telematics data for drivers and generate a “driver score” that assesses a driver’s behavior and ability relative to others. On the insurer’s end, only this high-level score is stored and associated with a policyholder or a risk, much like how credit scores are used. This kind of scored use of IoT data is good for top-level decision-making, executive review across the book of business or big-picture analysis of the data set. It requires having significant trust in the third-party vendor’s ability to calculate the score. Even when the insurer does trust that score, it’s never going to be as closely correlated to the insurer’s business because it’s built with general data rather than the insurer’s claims and loss history. In some cases, especially insurers with smaller books of business, this might actually be a plus, because a third party might be basing its scores on a wider set of contributory data sets. And even large insurers that have matured to later phases of IoT data consumption might still want to leverage these third-party scores as a way to validate and accentuate the kind of scoring they do internally. One limitation is that a third party that aggregates and scores the kind of IoT data the insurer is interested in has to already exist. While this is the case for telematics, there may be other areas where that’s not the case, leaving the insurer to move to one of the next phases on its own. Phase 2: Cleansed/Simplified IoT Data Ingestion Just because an insurer has access to an IoT data source (whether through its own distribution of devices or by tapping into an existing sensor network) doesn’t mean the insurer has the big data capability to consume and process all of it. The good news is it’s still possible to get value out of these data sources even if that’s the case. In fact, in an earlier survey report by Novarica, while more than 60% of insurers stated that they were using some forms of big data, less than 40% of those insurers were using anything other than traditional SQL databases. How is that possible if traditional databases are not equipped to consume the flow of big data from IoT devices? What’s happening is that these insurers are pulling the key metrics from an IoT data stream and loading it into a traditional relational database. This isn’t a new approach; insurers have been doing this for a long time with many types of data sets. For example, when we talk about weather data we’re typically not actually pulling all temperatures and condition data throughout the day in every single area, but rather simplifying it to condition and temperature high and low at a zip code (or even county) on a per-day basis. Similarly, an insurer can install telematics devices in vehicles and only capture a slice of the data (e.g. top speed, number of hard breaks, number of hard accelerations—rather than every minor movement), or filter only a few key metrics from a wearable device (e.g. number of steps per day rather than full GPS data). This kind of reduced data set limits the full set of analysis possible, but it does provide some benefits, too. It allows human querying and visualization without special tools, as well as a simpler overlay onto existing normalized records in a traditional data warehouse. Plus, and perhaps more importantly, it doesn’t require an insurer to have big data expertise inside its organization to start getting some value from the Internet of Things. In fact, in some cases the client may feel more comfortable knowing that only a subset of the personal data is being stored. Phase 3: Full IoT Data Ingestion Once an insurer has a robust big data technology expertise in house, or has brought in a consultant to provide this expertise, it’s possible to capture the entire range of data being generated by IoT sensors. This means gathering the full set of sensor data, loading it into Hadoop or another unstructured database and layering it with existing loss history and policy data. This data is then available for machine-driven correlation and analysis, identifying insights that would not have been available or expected with the more limited data sets of the previous phases. In addition, this kind of data is now available for future insight as more and more data sets are layered into the big data environment. For the most part, this kind of complete sensor data set is too deep for humans to use directly, and it will require tools to do initial analysis and visualization such that what the insurer ends up working with makes sense. As insurers embrace artificial intelligence solutions, having a lot of data to underpin machine learning and deep learning systems will be key to their success. An AI approach will be a particularly good way of getting value out of IoT data. Insurers working only in Phase 1 or Phase 2 of the IoT maturity scale will not be building the history of data in this fashion. Consuming the full set of IoT data in a big data environment now will establish a future basis for AI insight, even if there is a limited insight capability to start. See also: IoT’s Implications for Insurance Carriers   Different Phases Provide Different Value These three IoT phases are not necessarily linear. Many insurers will choose to work with IoT data using all three approaches simultaneously, due to the different values they bring. An insurer that is fully leveraging Hadoop might still want to overlay some cleansed/simplified IoT data into its existing data warehouse, and may also want to take advantage of third-party scores as a way of validating its own complete scoring. Insurers need to not only develop the skill set to deal with IoT data, but also the use cases for how they want it to affect their business. As is the case with all data projects, if it doesn’t affect concrete decision-making and business direction, then the value will not be clear to the stakeholders.