In our new consumer research report, The Rise of the New Insurance Customer: Shifting Views and Expectations, we captured the views and expectations of today’s consumers in the midst of the disruption and change rapidly unfolding in the insurance industry. Insurers, MGAs, reinsurers and others must embrace this shift by understanding changes at play and accept that everything we have known about insurance was good for yesterday, but not good enough for today or tomorrow. The trends are fueled by the insurtech movement that wants to take advantage of the disruption and by a rapid, perpetual shift in customer expectations. Our research took a deeper dive into the people component, to understand 11 key insurance industry perceptions across the spectrum of researching, buying and servicing, consumer response and the implications for the insurance industry. Specifically, the research dives into this shift with more insights on the move to digital, an expected shift by millennials and Gen Z — and highlights that Gen X is often dramatically aligning with the Millennial and Gen Z consumer behavior. See also: Dare to Be Different: New Ways to Communicate With Customers The Rise of the New Insurance Customer compares insurance against nine other industries across the spectrum of consumer experience. The resulting perspective is that insurance is not “easy to do business with.” Some key insights from the research are:

• Insurance is “dead last” in terms of industries that are easy to do business with and are a good value. Life and annuities is significantly lower than P&C compared with the other industries/businesses with which consumers regularly interact.
• The Net Promoter Scores across the industries/businesses show insurance as relatively low.
• No industry is perfect when it comes to creating customer experiences for research, buying and servicing, but online and national retailers set the standard for all industries. We refer to this as the “Amazon effect.”
• Millennials and Gen Z clearly show different expectations than the silent generation and baby boomers. Gen X often aligns with millennials and Gen Z, highlighting the gap between traditional insurance over the last 50 years to insurance today and looking forward.
• The generational gap reflects an insurance industry steeped in tradition, where business models, business processes, channels and products are becoming rapidly irrelevant for the younger generations. The result is an open door to fresh, culture-savvy competition.

The implications for insurers are enormous. Over the last decade or so, many insurers have focused on transforming their businesses by replacing their legacy core systems with modern solutions surrounded by digital and data solutions. But the rise of new customer expectations does not necessarily align with these transformations. Why? Because many insurers did not anticipate the needs of the rise of the new insurance customers by transforming their business models, channels, products, services and engagement to meet the new generation of buyers. The result will be a potential shift in market leadership, with customers selecting insurers that best meet their needs and expectations. In North America, for both P&C and L&A insurers combined, this puts $1.4 trillion of premium at risk. The large differences between the generations on many aspects of the insurance experience highlight that established insurance companies (decades or centuries old), were built for the two older generations, the baby boomer and silent generations, which are declining in size and revenue power. In contrast, the two younger generations, Gen Z and millennials (and increasingly Gen X) have different experiences and behaviors that are at the core of why insurers need to redefine and reinvent themselves. Loyalty is now influenced by how well insurers meet their needs and expectations for products, engagement and value. The five generational groups underscore a shift that insurers must make to be relevant and competitive. It is a fundamental shift of a decades-old traditional business model, products, process and technology that were built to support the focus on products, mass standardization, operational efficiencies and automation. These are no longer effective in a market that demands customer-driven, personalized engagement, innovative products, simplification, transparency and everything digital. It’s time for “it’s always been this way” thinking to go away. Each company must ask itself strategic questions, such as: “How do we bridge between the past, today and the future? How do we keep current customers loyal and engaged as we redefine our business for a new generation?” If traditional insurers don’t ask these questions and act, others will. Both existing insurance companies and new entrants are responding, as evidenced by the large amount of activity in the insurtech space. Many think there is a better way for insurance to work, and they are acting on this belief and getting significant capital to make it a reality. In so doing, they have the opportunity to steal substantial market share from those companies that don’t ask themselves and act on the same questions. See also: How to Get Broader View of Customers And while many of these are in the early stages and are yet to be proven, consumers are very interested in these efforts, as demonstrated by the early results of Haven Life and Lemonade. Consumers are looking for fresh alternatives to age-old formulas. They will note whether an organization is completely new, with an innovative idea, or whether the organization is established but progressive in its approach. They will also know which organizations may be established, but not willing to cater to their preferences. In all cases, they will be looking at value, service, ease and understanding. How should insurers proceed? There are alternative paths that insurers can take depending on their strategies and resources. But the bottom line is that, based on the perceptions, reality and implications outlined in the research, companies must stop talking about the opportunities and being digital, and start doing something about it by using the disruption and change as a catalyst for “real change.” This change requires companies to rethink their business model and realign it with the customer needs and expectations of those who will be their customers for the next 10 to 20 years, not those from the past 10 to 20 years. There needs to be a renaissance of insurance to capture the revenue growth potential presented by the rise of the new insurance customer.

Business is a contact sport. Some companies win while others lose. That won’t change. There is no way to guarantee success. Make the best decisions you can, and then fight the battle in the marketplace. Yet research into more than 2,500 large corporate failures that Paul Carroll and I did found that many big decisions are doomed as they come off the drawing board—before first contact with the competition. Why? The short answer is that humans are far from rational in their planning and decision-making. Psychological and anthropological studies going back decades, including those of Solomon Asch, Stanley Milgram, Irving Janis, Donald Brown and, more recently, Dan Ariely, consistently demonstrate that even the smartest among us face huge impediments when making complicated decisions, such as those involved in setting strategy. In other words, humans are hard-wired to come up with bad decisions. Formulating good ones is very difficult because of five natural tendencies: 1. Fallacious assumptions: If “point of view is worth 80 IQ points,” as Alan Kay says, people often start out in a deep hole. One problem is the anchoring bias, where we subconsciously tend to work from whatever spreadsheet, forecast or other formulation we’re presented. We tend to tinker rather than question whether the assumptions are right or whether the ideas are even worth considering. Even when we know a situation requires more sophisticated analysis, it’s hard for us to dislodge the anchors. See also: Downsizing: Common Sense in Decision-Making May Lead to a Trap   Another strike against expansive thinking is what psychologists call the survivorship bias: We remember what happened; we don’t remember what didn’t happen. We are encouraged to take risks in business, because we read about those who made “bet the company” decisions and reaped fortunes—and don’t read about those that never quite made the big time because they made “bet the company” decisions and lost. 2. Premature closure: People home in on an answer prematurely, long before we evaluate all information. We get a first impression of an idea in much the same way we get a first impression of a person. Even when people are trained to withhold judgment, they find themselves evaluating information as they go along, forming a tentative conclusion early in the process. Premature conclusions, like first impressions, are hard to reverse. A study of analysts in the intelligence community, for instance, found that, despite their extensive training, analysts tended to come to a conclusion very quickly and then “fit the facts” to that conclusion. A study of clinical psychologists found that they formed diagnoses relatively rapidly and that additional information didn’t improve those diagnoses. 3. Confirmation bias: Once people start moving toward an answer, they look to confirm that their answer is right, rather than hold open the possibility that they’re wrong. Although science is supposed to be the most rational of endeavors, it constantly demonstrates confirmation bias. Ian Mitroff’s The Subjective Side of Science shows at great length how scientists who had formulated theories about the origins of the Moon refused to capitulate when the moon rocks brought back by Apollo 11 disproved their theories; the scientists merely tinkered with their theories to try to skirt the new evidence. Max Planck, the eminent physicist, said scientists never do give up their biases, even when they are discredited. The scientists just slowly die off, making room for younger scientists, who didn’t grow up with the errant biases. Planck could just as easily been describing most business people. 4. Groupthink: People conform to the wishes of the group, especially if there is a strong person in the leadership role, rather than ask tough questions. Our psyches lead us to go along with our peers and to conform, in particular, to the wishes of authority figures. Numerous psychological experiments show that humans will go along with the group to surprising degrees. From a business standpoint, ample research, supported by numerous examples, suggest that even senior executives, as bright and decisive as they typically are, may value their standing with their peers and bosses so highly that they’ll bend to the group’s wishes—especially when the subject is complicated and the answers aren’t clear, as is always the case in strategy setting. 5. Failure to learn from past mistakes: People tend to explain away their mistakes rather than to acknowledge their errors, making it impossible to learn from them. Experts are actually more likely to suffer from overconfidence than the rest of the world. After all, they’re experts. Studies have found that people across all cultures tend to think highly of themselves even if they shouldn’t. They also blame problems on bad luck rather than take responsibility and learn from failures. Our rivals may succeed through good luck, but not us. We earned our way to the top. See also: How to Lead Like a Humble Gardener   While it’s been widely found that some 70% of corporate takeovers hurt the stock-market value of the acquiring company, studies find that roughly three-quarters of executives report that takeovers they were involved in had been successes. The really aware decision makers (the sort who read articles like this one) realize the limitations they face. So, they redouble their efforts, insisting on greater vigilance and deeper analysis. The problem is that that isn’t enough. As the long history of corporate failures show, vigilant and analytical executives can still come up with demonstrably bad strategies. The solution is not to just be more careful. Accept that the tendency toward decision-making errors is deeply ingrained and adopt devil’s advocates and other explicit mechanisms to counter those tendencies.

Anonymous theft and abuse of business data is a growing risk for many organizations. Most security initiatives aimed at this problem begin with piecemeal technical controls, such as trying to block and account for things like USB drives or mobile devices with software and policies. However, zeroing in on technical countermeasures first is looking at the problem upside-down. Instead, companies should first and foremost ask whether their corporate cultures are inviting insiders’ malicious and risky behavior — or whether these cultures are functioning to deter it as a first line of defense. See also: How to Measure ‘Vital Signs’ for Cyber Risk   The continuing Wells Fargo controversy is a perfect case in point. Media accounts claim Wells Fargo managers pressured employees to meet aggressive growth quotas by signing up account holders for new accounts and financial services they never requested — reportedly netting the bank significant income in new fees and service charges. In effect, workplace cultures like this create a slippery slope, fostering a wider range of “fallout” insider threat behaviors. When an organization’s culture creates opportunities for abuse, motivated employees may be more disposed to comb through that organization’s data for a side business, copy records on behalf of a rival or sell files to cyber criminals. The sheer scale of this contributing risk factor becomes clear when you consider that  high-pressure sales environments exist in many companies — to varying degrees. This is yet another example of why security and data privacy risks always begin and end with business factors and people, not technology. Employees pressured into abusing data without penalty set an increasingly toxic precedent. Moreover, managers’ use of private, “unofficial” mediums outside of corporate oversight — such as text messages or personal email — to request or facilitate questionable conduct only reminds would-be malicious insiders that they will not arouse suspicion if they, too, use such tools in the workplace. How prevalent is this conduct? The answer matters because these behaviors are risk variables that are as important as patch levels and app permissions. Recent bank investigations are a reminder for CEOs and chief information security offiers (CISOs) alike that transparency, ethics and cybersecurity go hand in hand. As complex as fighting myriad cyber risks can be across companies’ changing IT assets, too few decision-makers recognize the power of healthy leadership and corporate culture as a scalable, enterprise-wide defense. See also: Better Way to Assess Cyber Risks?   Soul-searching in the wake of today’s headlines should include serious thoughts about making an ethical, highly visible business culture the first line of deterrence against ubiquitous insider risks. Accountability and leadership should play a larger role in safeguarding data and keeping business partners in line long before factoring in USB drives and mobile devices. More stories related to insider threats: Sophisticated email monitoring can help companies detect insider threats Inattentive employees pose major insider threat Insider threats pose major cybersecurity exposure This post originally appeared on ThirdCertainty. It was written by Dan Velez.

The term "the first 100 days" was coined in a July 24, 1933, radio address by President Franklin D. Roosevelt, who was referring to the 100-day session of the 73rd United States Congress between March 9 and June 17, 1933. As a board member, I think it is a great idea for a new CEO to think in a similar fashion and prepare a memo outlining what he/she will be doing for the first 100 days of the new job. The first 100 days is the time when the new CEO determines the culture of the organization, gets his/her arms around the finances and the budget and determines who has what skills and experience to help lead the organization and who he/she can rely upon to achieve the goals set by the board. There are several books on the market outlining the steps a new CEO should take in his or her first 100 days. See also: Insurance CEOs See Wave of Disruption   As a parallel to the first 100 days concept for the president of the U.S., when one starts a new job with a new company, the culture tends to give the person a level of credibility and deference from leaders in the organization that is not usually afforded to existing employees. I call it Teflon. For me, the game was to retain the Teflon beyond the first 100 days, or to work on my “Teflon renewal process.” I would do that by outlining my goals and expectations in a 100-day memo — and then I would achieve the goals set out in that document. Every time I was promoted, got a new boss, was involved in a restructuring or saw my role changed, I would prepare a memo for my boss (and myself) outlining my plans for the first 100 days. The document outlined my 100-day goals as well as my mid-term and long-term goals for my department. It also provided insight into my key performance indicators and into the strengths and weaknesses of the team. The memo outlined my expectations for what I would accomplish as well as the expectations of what I needed from my people to accomplish the goals. More importantly, the memo got me into the habit of doing what I needed do on a daily basis for me to be successful in my new role. This memo also resulted in establishing the way in which I would communicate with my boss. As a best practice, I recommend everyone consider preparing such a document when they get a new job or role. I also recommend that, as a manager or supervisor, you ask your employees to outline their goals and expectations in their own 100-day document. See also: CEOs Defy Common Sense on Wellness Now all I have to do is to prepare my 100-day plan for when I am at home — I need some Teflon with the wife. Here is an article that provides some detail on how to produce a 100-day action plan for a CEO.

It is no secret to commercial lines insurers that the market is hyper-competitive and has been so for years. There is very little to suggest that this is going to change. But there are other changes afoot, as evidenced by the new entrants, from the ranks of both traditional insurers and reinsurers as well as startups leveraging leading new technologies. And these new entrants are changing the commercial lines landscape. The startup impact was especially noticeable at the InsureTech conference in Las Vegas in October. While there are several critical success elements, few would argue about whether agents, brokers and MGAs are front and center. Unlike personal lines, where direct-to-consumer channels are growing, commercial lines are still dominated by the independent agent and broker channels. And due to risk complexity and the need for advice, this is not likely to change soon. At the InsureTech conference, Brian Duperreault, chairman and CEO of Hamilton Insurance Group, and past CEO of Marsh & McLennan, emphatically made the point that consumers do not want agents and brokers to go away -- they are critical to risk decisions. See also: The Uberization of Insurance   Given the imperative of having a strong agent and broker network, commercial lines insurers need to understand what it takes to ensure a successful outcome. Clearly, being easy to do business with is pivotal, and a key component of that is agency connectivity. To gain insight into what this means to commercial lines insurers and distributors, SMA conducted primary research. The recently released report Agency-Carrier Connectivity: Commercial Lines Insurers provides and explores the results. One thing was immediately clear. 90% of commercial lines survey respondents indicated that by 2020 new capabilities for agency connectivity will be a game changer. No insurer can ignore a game changer, but the road to a successful business outcome is not necessarily an easy one. 67% of survey respondents indicated that improving the agent experience is the No. 1 business driver for investing in agency connectivity. Yet four out of the seven barriers to investment revolve around a lack of business commitment and clarity. The good news about that result is that insurers are in direct control of these barriers! Survey results show the majority of insurers would prefer an automated exchange of data and information with distributors, with limited phone, paper or email pdf exchange. But legacy constraints, data mapping/inconsistencies and a “spaghetti bowl” of processing problems stand in the way. It would be easy to believe the issue is technology, but survey results point in a different direction. In fact, they point directly at culture and focus. Arguably, the most troublesome problem is that culture -- the idea that “We’ve always done it this way” -- stands in the way of an automated process. Given the age-old belief that commercial lines are seen as art and not science, culture is a huge issue that must be recognized and addressed. Acquiring new business is a serious problem in today’s competitive commercial lines market. According to SMA research, the No. 1 stumbling block to meeting production goals is quality and fit of submissions. Given this pressure, it would seem logical that investments would have been made to deal with this. But despite the fact that agency upload and download have been around for a very long time, commercial lines insurers still feel that their needs are either not being met at all or are met in only a limited measure. Important processes such as quoting, binding, policy documents, billing and risk management are either not being addressed through connectivity technology or there is neutral value attached to that technology. And with IT budgets stretched, technology providing only neutral value is tantamount to having poor value. See also: 3 Ways to Improve Agent/Insurer Links   More and more, agents' and brokers' decisions about where they will place their business are being driven by who is easy to do business with, even though underwriting expertise and claims capabilities will always be very important. Technology for connectivity is central to fostering incumbent loyalty and drawing the attention of a new generation of distributors. It is critical that all commercial lines insurers have a solid road map for investment around connectivity. Understanding what the potential barriers are and what peer company investments are being made will allow commercial lines insurers to move forward faster and with less overall risk. The commercial lines insurer report can be found here. Before the end of the year, the personal lines insurer view of connectivity will be published, as will the agent and broker view.  So, stay tuned!

For years, insurance companies have taken steps to improve the life insurance underwriting experience in the hope of removing obstacles and decreasing not-taken ratios. To that end, some have forgone the traditional exam altogether in favor of simplified issue. But the truth is, consumers still aren’t flocking to life insurers, and the results of these efforts have been incremental. Force Diagnostics has taken a different approach. We’ve developed a consumer-centric process featuring rapid testing that delivers results in 25 minutes. Tests are performed outside of the home in retail clinics and pharmacies, and results are immediately transmitted directly to the carrier’s underwriting engine for immediate processing. Because of the speed to results, innovative insurers and reinsurers could offer an accurate quote for life insurance to their consumers within 24 hours. And with the benefit of testing with fluids (HbA1C for diabetes, cotinine for nicotine, lipids for cardiovascular risk and the presence of the HIV virus, as well as body mass index and blood pressure), insurers may offer the majority of their products quickly and with assurance. See also: Next Generation of Underwriting Is Here   The potential results of using this new process can be seen in this underwriting performance calculator. Once the calculator is downloaded, you may select a typical life insurance policy from a dropdown menu and enter assumptions that reflect an existing underwriting process. The calculator then shows a comparison on underwriting costs, internal rate of return (or IRR) increases, issued policy increases and the potential effects on persistency. At the end, total costs per app are calculated, as are total profits. There is tremendous value in improving the customer experience throughout the underwriting process.

Soon-to-be published editions of dictionaries will list "InsureTech" as one of the newest words. We all own a piece of that new word and all that comes along with it. More than a new word, it is becoming a new world in the insurance industry. We're on an InsureTech expedition. Having spent decades of my life developing products, marketing programs and delivery systems in the life insurance vertical, I feel compelled to share some insights into the unique characteristics of the life insurance segment within the InsureTech movement. I will offer a recipe for an end-to-end digital system that bypasses legacy system quagmires and shifts digital life insurance sales into warp speed in both the consumer-direct and agent-broker categories. But first a few words about what makes life insurance different from other types of insurance, along with some commentary on the state of affairs in today's market. Life Insurance Is Optional Let's think about the major types of insurance that consumers buy. Auto, home, health and life. We are required by law in all but two states to have auto insurance. If you have a mortgage on your home, you are required by the lender to have homeowners insurance. Federal law now requires that most of us must have a health insurance policy. These types of coverage are not optional. You don't see articles about a trillion-dollar middle market coverage gap in the auto and homeowners insurance segments. See also: What’s Next for Life Insurance Industry?   But there is a trillion-dollar life insurance coverage gap in the middle-market today in the U.S. Why is that? First, the process of obtaining a life insurance policy for typical middle-market needs is overwhelming, tedious, intimidating and mysterious for consumers. We're talking about a basic term life policy with coverage of $250,000 or $500,000 or, OK, perhaps a million dollars of coverage in some cases in the middle market. Seems like it should be easy. But, even though we have seen price reductions across the board during the past 20 years for term life, individual life insurance ownership has actually decreased. The buying process is broken. Second, combine the antiquated buying process with the fact that the purchase of life insurance is optional, and consumers repeatedly push the chore of buying life insurance to the bottom of their to-do lists. To make matters worse, because the fulfillment process for these smallish policies is so expensive for brokers and agents, they cannot make a profit focusing on the middle market. You end up with an unmotivated distribution system and a trillion-dollar coverage gap. You Also End Up With a Trillion-Dollar Opportunity I've taken it upon myself to write down the recipe for a digital process for capturing a sizable share of that opportunity. This is what we need to mix together to end up with a complete system that is capable of starting with "Hello" and ending minutes later with a completed transaction: an "in-force" policy for the consumer.

• User-friendly graphical user interface for both consumers and agents. (You would be surprised.)
• Easy quote engine -- provides all relevant price quotes so you don't jump back and forth looking at one quote at a time. First thing I notice about most designs is that you have to keep re-entering inputs to see different quotes instead of being able to scan all of them on one screen.
• Digital life insurance application process. Simple application language. Find just the right balance between just enough questions and not too many questions per screen.
• Decision time. Consumer-direct or agent-assisted? Both models will become more numerous in the marketplace. Carriers need to understand that many consumers need and want some level of assistance. So, carriers need to be prepared to offer chat and over-the-phone assistance to complete the online process. Perhaps even full-blown call center agent "take over" of the application process when the applicant calls for help. Or some combination of these.
• Collection of contact information from website visitors who are "just looking" so that carriers can conduct email and phone nurturing campaigns. Carriers need to understand and appreciate the tremendous dollar value of these campaigns and not leave a huge percentage of potential revenue on the table.
• Compliance with Do Not Call and telecommunications statutes and CAN-SPAM. By the way, CAN-SPAM is widely misunderstood, and many marketers do not understand the generous powers it provides to contact potential customers via email. Email is still the "killer app" it was labeled as many years ago. Text messaging is a first cousin for certain market segments. Special language is needed on website(s) dealing with consumer permissions to use their mobile number.
• Secure payment gateway to provide PCI-compliant credit card processing and deliver premium payments to the carrier. The ability to accept consumers' checking or savings account numbers for payment is also necessary. Payment screens need to be seamless, transparent and simple.
• Secure digital signature interface for consumer-direct and face-to-face sales as well as agent-assisted phone sales. All are slightly different. All are important. Again, seamless, transparent and simple.
• Behind-the-scenes secure interfaces to the Medical Information Bureau (MIB), motor vehicle records (MVR) provider and pharmacy records (Rx) provider must be built to provide capability for real-time queries and retrieval of third-party data.
• If the life insurance product being purchased does not require a medical exam ("non-medically underwritten," which requires no blood or urine tests), then the process can proceed to the next step, which is the underwriting decision engine. If the design and pricing of the life insurance product do require blood and urine testing ("fully underwritten"), then the system will present a screen in the process for an appointment to be scheduled. Many designs are getting away from blood and urine testing, but, to be realistic, these tests will still be needed in many cases for years. This topic deserves to be considered in the system design sessions.
• Underwriting decision engine that compiles all answers provided by the consumer on the digital application form with the MIB, MVR and Rx data. In real time, the underwriting engine then renders a decision on the application. Some straight-through systems are considering using third party software for this. Others have their own, proprietary engines that afford much faster adjustments to the underwriting engine rules and settings. Controlling the underwriting engine technology also can be the difference between a "go" or a "no go" answer when seeking to add features, change processes, edit code and take other similar actions, which are needed on a continuing basis, and sometimes quickly.
• As applications are approved, the system must package the approved policy for the state of issue with all the necessary additional pages, such as HIPAA forms, Consent to Do Business Electronically forms and other pages, which can vary from state to state. This policy package must be provided to the new customer, the policyholder, in real time using a secure link for downloading.
• All data pertaining to the new customer's file must be transferred to the carrier's administrative system in real time. A new customer is born.
• Finally, a deep and broad suite of analytics must be baked into the system's DNA and designed to manage the business being put on the books on a daily basis. Take this data in real time and reinforce what is working. Correct that which is not. Just this one necessary component alone could be the topic of an article several times the length of this one. We'll get right on that.

See also: InsurTech Can Help Fix Drop in Life Insurance   These are the many pieces that I truly believe are necessary to work together perfectly to achieve the kind of disruption that is so necessary. We're already all over this one.

When it comes to retirement, a significant cultural and demographic trend is taking place. Twenty-five years ago, only about one worker in 10 planned to stay in the workforce beyond age 65. Today, that number has risen to more than 50%. In fact, according to the 16th annual Transamerica retirement survey, 82% of 60-somethings expect to work or are already working past age 65. Today’s boomers are not ready to stop working. They are better-educated, more physically fit than their parents and perhaps not as financially comfortable as they'd like to be. In short, they are defying stereotypes about an aging workforce and redefining retirement. It's not strictly a financial matter. “Money and access to healthcare are of considerable importance,” reports the AARP Public Policy Institute, “but so are the desires to remain active, make a contribution and maintain social relationships at work. Furthermore, these workers often enjoy what they are doing.” Boomers are still working today at ages when their parents and grandparents had retired. This is particularly true in a service industry like insurance where physical strength is not an issue. Think of today’s 65-year-old worker as yesterday’s 50-year-old worker. What is surprising is that few insurance firms have adapted to this new trend or have put policies and practices in place to accommodate today’s retirement reality. Yet, when asked to name their greatest challenges, many insurance firms concede that finding and keeping qualified staff is at the top of their list and state that the loss of talented and experienced older workers is a key concern. This issue is further confirmed by the accounting firm PwC’s report that concludes the industry faces “a potentially massive loss of skilled, knowledgeable workers” as large numbers of older insurance workers approach their traditional retirement dates. The report notes that hiring millennials is only part of the solution and does not adequately address the transfer-of-knowledge issue. See also: Why Are We Still Just Talking Diversity?   Perhaps it isn’t so surprising after all. There remains a litany of stereotypes and negative perceptions to explain why firms tend to reject older workers, including:

• Hiring managers tend to see older workers as more likely to be burned out, slow to accept or adapt to new technologies, more likely to miss work due to illness and poor at working with younger workers, especially younger supervisors; and
• Many assume older workers are less creative, less productive, slower mentally and more expensive to employ than their millennial counterparts.

But current research negates these stereotypes:

• According to a study this spring prepared by Aon Hewitt for AARP, “workers aged 50-plus can help employers address current and future talent shortages.” AARP and others have long argued that older workers are reliable, flexible and experienced and possess valuable institutional knowledge.
• Writing in the AARP Bulletin on “The Value of Older Workers,” T.R. Reid reminds us that hiring skilled, vintage workers can be “a boon to employers, a boost for the U.S. economy and a bonus for the workers. For employers, the ‘unretired’ provide a pool of experienced labor that has proved to be productive, dedicated and loyal.”
• One of last year’s blockbuster movies, The Intern, took notice of boomers reentering the workforce. Robert DeNiro played a septuagenarian whose experience and life skills help turn around the business run by the wonder-kid played by Anne Hathaway. His character pinpoints the reasons employers should want an older worker: “I've always been a company man,” he declares. “I'm loyal, I'm trustworthy and I'm calm in a crisis.”

Researchers at the University of Kentucky surveyed large and small companies to assess how employers evaluate their older workers. They uncovered seven perceived benefits of hiring older workers:

1. Dedication to the organization
2. Customer-service orientation
3. Dependability
4. High productivity
5. Life experiences
6. Strong work ethic
7. Institutional knowledge

When it comes to actual job performance, Peter Cappelli, a management professor at the Wharton School of Business, is quoted in the AARP article, “The Surprising Truth about Older Workers,” as observing that older workers outperform their younger peers. “Every aspect of job performance gets better as we age,” he says. “I thought the picture might be more mixed, but it isn't. The juxtaposition between the superior performance of older workers and the discrimination against them in the workplace just really makes no sense.” Nathaniel Reade summarized Cappelli's findings: “Older workers tend to be motivated by causes like community, mission and a chance to make the world a better place; younger workers are more driven by factors that directly benefit themselves, such as money and promotions. But perhaps the greatest asset older workers bring is experience — their workplace wisdom. They've learned how to get along with people, solve problems without drama and call for help when necessary.” In a Sept. 18, 2015, U.S. News and World Report article, “5 Reasons Employers Should Hire More Workers Over Age 50,” Maryalene LaPonise suggests we “forget the myth that older workers are outdated and expensive. The best are loyal and competent and may even help a business’s bottom line.” She sees their experience, confidence, "relatability," loyalty and ability to save companies money as the key reasons to hire older workers. In a Brookings blog, World Bank economists Wolfgang Fengler and Johannes Koettl write, “A binary system of working 100% until retirement and then suddenly moving to 0% at an arbitrary age of around 65 is one of the great anachronisms of today's labor market.” They argue the whole idea of a "retirement age" should be retired. See also: How Should Workers’ Compensation Evolve?   At WAHVE, we agree in the power and performance of experienced workers. It is clear that the attitudes of insurance firms toward older workers need to be reset. For our part, WAHVE is helping reimagine both staffing and retirement in the insurance industry and bridges the gap between insurance firms’ staffing needs and seasoned professionals’ “work-life” balance preferences as they phase into retirement.

Venture capital money has poured into insurance technology to the tune of more than $3 billion in the last 18 months. Much of this capital has financed companies founded under ambitious missions and goals: “It’s a digital-first, direct-to-consumer agency — we’ll be a carrier in 18 months.” "We’re a mobile-first, full-suite personal insurance shopper.” “We’re leveraging IoT and blockchain to completely redefine underwriting — in a way that makes sense to consumers.” Wow, sounds disruptive. Anyone close to the insurance industry has heard some variation of these assertions countless times over the past 18 months as entrepreneurs have identified the antiquated insurance industry as one ripe for disruption. There’s no arguing that insurance is a relative laggard in a financial services industry that has seen material innovation over the past decade. Advancements in payments, investment management and lending have permanently altered the way consumers think about banking and the movement of capital, while insurance has remained relatively underserved by technology. See also: Find Your Voice as an Insurance Agent   Technological innovation across industries has typically come in two ways: through uprooting incumbents or through empowering the existing system. In industries and business lines that are largely commoditized, the former approach has historically created lasting value — with Uber being a prime example. Meanwhile, industries that require expertise or personal touch have generally innovated incrementally by enabling existing channels — Charles Schwab or Sabre are examples. Most agree technology will improve the user experience in insurance. Most agree technology will provide new access to data sources to improve underwriting. But will technology actually displace the incumbents and existing institutions? Is that the approach that will work in insurance? In some cases, yes. In the case of the commercial insurance, we generally think not. Disintermediation? We see empowerment. At first glance, commercial insurance may seem like an ideal candidate for meaningful disruption. It is a substantial market with more than $240 billion in premiums written annually; it has a brick-and-mortar, aging distribution channel (39,000-plus retail agents with an average age over 50); it primarily operates with pen and paper communication; and it largely functions in a data vacuum. In addition to these structural features, many small commercial policies can now be quoted, rated and bound instantly (they don’t need human review). This seems like a startup opportunity in a box. With these market fundamentals in hand, a host of direct-to- consumer digital insurance brokers or companies (MGAs) are entering the market with the intent of disintermediating existing channels and delivering instant policies to small commercial insureds. Examples of some of these are Next Insurance, Embroker, Coverwallet, Trym — and the list goes on. For some business owners, purchasing coverage in this manner may in fact be the best way to transfer their business’ risks. Though it is quite likely the amount of premium placed through digital channels will increase from its current number (around 4%), we see the incumbent brick-and-mortar retail brokers, who command 96% of placed premium, as incredibly entrenched for a host of reasons:

1. Commercial insurance is complex. Each business has unique risks that business owners struggle to understand. The alphabet soup that is commercial insurance and discussions of CGL, EPLI, E&O, coverage limits and deductibles are often met with blank stares. So, even if business owners understood their risks and could purchase coverage directly, they often are not familiar with what they are buying.
2. Brokers actually acquire customers quite efficiently, and it may be difficult (or impossible) to acquire customers online at the same cost that brokers do through more traditional means. Acquisition of small and medium-sized business (SMB) clients generally works best when done vertically or locally — most brokers, local to their area and experts in specific products, fit that mold.
3. Commercial insurance policies are not commodities. Each policy is unique and has its own specific set of coverages, endorsements and exceptions. An experienced agent has immense value to the insured navigating potential coverages.
4. There is no cost savings by going around a traditional broker. Brokers are free insurance consultants to the client, and there is no cost difference between going through a digital channel vs. a traditional one.
5. In commercial insurance, the buyer is insuring against certain things that could put her entire business at risk. A trusted adviser is, therefore, incredibly important in understanding various policies and insurable risks.

Unless there are significant advancements in artificial intelligence and reductions in marketing costs, we don’t see the possibility for meaningful disruption or displacement of the broker in the near term. We do, however, believe in a massive network of digitally empowered brokers. The digital broker Imagine a world where a technology platform gives a network of brokers the same digital tools that are being produced by the technology startups trying to replace them. The broker now has a digital interface that services insureds quickly while simultaneously providing expert advice that takes years to amass. See also: 3 Ways to Improve Agent/Insurer Links   Moreover, there is meaningful precedent for industries with the above dynamics to become empowered, not disrupted, by technology. Charles Schwab became a household name largely because it allowed wealth managers to break away from banks, freeing them from the operational overhead of having to build trading, reporting or portfolio management systems. Schwab enabled wealth managers to focus on being a high-quality consultant to its clientele. This has also been exhibited in real estate, where startups have empowered real estate agents with infrastructure and data science to provide a level of service and expertise on par with companies like Amazon and Google. Both of these examples illustrate the transformative effect of empowering existing, experienced distributors of complicated and operationally intensive products. We see this same future for commercial insurance brokers.

Unless you’ve been living under a rock for the last few years you’ve heard a LOT about big data. But if you’re like most insurance professionals, you didn’t go to school for computer science, and even though it sounds very cool you really haven’t gotten your head around a simple question: What the heck is big data? And how will it affect insurance? For the last several years, the world has been creating more data than it ever had in the past. Some call it the digital exhaust: Everything we do leaves a digital trail, and with a smartphone in every pocket, a laptop in every backpack and near-universal access to giant clusters of computers in the cloud, the sheer amount of data we are able to collect on everyone and everything has grown exponentially. Data grew to such large quantities that it no longer fit in the memories computers use for processing it, so whole new tools had to be designed to handle it. We started creating and saving so much data that there was a qualitative change, and all of a sudden we became able to extract new insights and create new value due to the large scale of the amount of data that we can access. Things are now possible that simply could not have been done at a smaller scale. One of the key changes that happened is that we started recording everything in a digital rather than analog way (computers instead of paper). As recently as the year 2000, only a quarter of all of the world’s information was digital. By 2007, more than 93% of the world’s information is now in digital format and can be much more easily read and analyzed by computerized tools! By 2013, more than 98% was digital. Why Is Big Data a Big Deal for Insurance? At its very core, insurance has always been an information business. We don’t make widgets. We help people and businesses manage their risks and help pay for the losses when they happen, and all of this is based on information, not on arranging physical atoms in any way. It’s literally a pure information business. See also: What Comes After Big Data?   For centuries, when faced with very large numbers of data points, society has depended on using samples. This applies even more to the insurance industry. Think back to CPCU 500; our ENTIRE business is based on the law of large numbers and on making statistically valid predictions about risk. (If you haven’t done your CPCU, stop reading this article right here and go get started on it! Here’s why, here’s how.) Sampling, and the law of large numbers, was necessary because we lived in a world of limited information, an analog world where most things didn’t get recorded in an easy-to-analyze way. We are now in a different world, in the digital era, and now, thanks to big data, we are approaching a world in which we won’t need to use samples anymore; we’ll have ALL the data. This will have huge implications for our industry. Historically, we had to work with samples because it was very difficult or impossible to collect all of the data, and because we didn’t have tools that could work with gigantic sets of data. Having ALL the data related to something, instead of a sample of it, allows us to see much more detail. For example: In the old analog world, our actuaries figured out that 16- to 19-year-old drivers were more likely to have an auto accident, and this became a key part of how we price auto insurance. In the new digital world of big data, we might be able to analyze every second a young person has ever driven and make a personalized price for his very own level of risk! That rate will be much more accurate because it is based not on some of the general data (the accidents had by insured 16- to 19-year-olds) but rather by ALL the specific data (every second of driving this person has ever done). By its very definition, actuarial science, which our entire business is built on, is “the discipline that applies mathematics and statistical methods to assess risk,” and one of the aims of statistics is to “confirm the richest findings using the smallest amount of data.” In other words, our entire business is built on making predictions using limited data. In a world of unlimited data, we will have to quickly become world class at analyzing and reacting to ALL the data, or we might be beat at our own game by those who do. The Why Doesn’t Matter, Only the What In the old world of small data, society spent a lot of resources trying to figure out the why behind things. Scientific and statistical studies started with a hypothesis, a prediction of how things worked, and then tested the available sample of data to see if that hypothesis was correct. If it wasn’t, then the hypothesis was modified and tried again. Most data was collected for a specific purpose, and it was very difficult to use it for other purposes without collecting a new sample. Today, with so much data around and more to come, hypotheses are no longer crucial. All that is needed is analysis for correlations. Before big data, because of the more limited amount of computer power we had, most analysis was for linear relationships (this causes that); with the new tools of big data analysis and the faster computers available today, we can find more complicated non-linear relationships (a, b, c, d, e, f, g independently predict x a little bit but together they predict x very accurately). It doesn’t matter that your system doesn’t know all the variables that go into a problem, only that it can predict the result. For example, Google has used big data to predict flu outbreaks faster than the Centers for Disease Control and Prevention (CDC) by letting the computer figure out which searches correlate with flu outbreaks. It doesn’t matter whether those people know that what they’re searching about is the flu, just that they’re searching on it and that when those hundreds of identified search terms happen in one area there’s a very good chance that area is experiencing a flu outbreak. In the new world of big data, the why something happens doesn’t matter, it only matters that we are now able to find the hidden patterns and find it or predict it. Society will need to shed some of its obsession for causality in exchange for simple correlations. One example of how an insurance company is trying to use big data to improve its underwriting is Aviva, which studied the idea of using credit report and marketing data to underwrite some life insurance applicants instead of the traditional blood and urine lab analysis. The idea is to identify applicants with higher risk of lifestyle diseases like high blood pressure, diabetes and even depression. The method uses lifestyle data that includes hundreds of variables such as hobbies, the websites people visit and the amount of television they watch, as well as estimates of their income. The traditional lab tests cost $125 per person while this new approach can be as cheap as $5.  This is an example of a correlational relationship being valuable and more efficient than relying on a causal relationship for prediction of an outcome. The More Data We Have, the Less Exact It Needs to Be In the old world of small data, statisticians and data analysts were trained to clean out outliers and try to get data that was as clean as possible. With big data, we are looking at vastly more data, which means that we can get away with less precision. It’s a tradeoff; with less error from sampling, we can accept more measurement error. The old tools (spreadsheets, relational databases, SQL, business intelligence tools, etc) were created to work on exact data; the new tools are designed to work with large quantities of imperfect data. The need for perfect data was a side effect of the limited tools we used to manage small data. See also: Eating the Big Data Elephant   Here’s a great example of why we can now get away with less exact data: Suppose we need to measure the temperature in a vineyard. If we only have one temperature sensor for the whole plot of land, we must make sure it’s accurate and working at all times: no messiness allowed. In contrast, if we have sensors for every one of hundreds of vines, we can use cheaper, less sophisticated sensors (as long as they don’t introduce a systematic bias). Any particular reading may be incorrect, but the aggregate of many readings will provide a more comprehensive picture. Data Is No Longer Stale After Its Original Use One of the very limiting features of the old world of data is that once a dataset was built for a particular use, it was very difficult to use it for another, so you have to know what you’re looking for before collecting the data.  Because you were collecting a sample of data and inputting it into a very structured format for future analysis, getting the right pieces of information was of paramount importance. In the new world of big data, all data becomes a new raw material to create value in new and creative ways, most of which were impossible in the old world. Because we are collecting data on everything, and our tools are more sophisticated in ability to arrange and rearrange that data, we are more able to use the information in a variety of ways. Think about it; that telematics device on your car collects a TON of data. Think about the data your smartphone collects about your habits each day. Every time you search on Google, it's recording not only what you search for but even the exact amount your mouse spent at different parts of the screen. Soon, we’ll even be able to track your eyes through the webcam when you visit our website. There’s just a TON of data out there that we’ll now be able to analyze and learn about our customers. Being Free of Sampling Will Allow Us to Know More Sampling quickly stops being useful when you want to drill deeper, to take a close look at some intriguing subcategory of the data. One of the key benefits of being able to collect ALL of the data about something is that we can dig further into the data and ask it fresh questions that we hadn’t even thought of when we started collecting the data. In the old paradigm of sampling, one would collect only what was directly asked for. If you noticed a pattern in that sample but needed something to explain or verify the pattern that you had not thought to ask for ahead of time, you would need to re-sample and get additional data to confirm what you found. Data No Longer Needs to Be Structured Traditionally, the way data was stored in spreadsheets and databases was structured, meaning that each field could fit a very specific type of data; a phone number field, for example, could only hold a 10-digit number. The problem is that only around 5% of all digital data in the world is structured in a form that neatly fits into a spreadsheet or database. That means we had no easy way to analyze the other 95%! Pretty much all data had to be cleaned up before analysis, which made everything smaller and more expensive. In the new world of big data, new tools such as Hadoop are able to analyze unstructured data in all shapes and sizes, 100% of data instead of just 5%. The tools can even analyze things like books, journals, metadata (data about data), audio, video and much more. Imagine being able to include every second of conversation digitally recorded from your call centers along with all your other data and analyze it all to find trends! This is one of the most powerful features of big data, and it is already being used in many call centers. Messier Data Will Help Us Insure Messier Things Big data’s ability to help us analyze messy data could help us insure harder-to-insure things. For example, ZestFinance, a company founded by a former chief information officer at Google, built technology that helps lenders underwrite small, short-term loans to people who have bad credit scores. Turns out traditional credit scoring is based on a few factors, while ZestFinance uses a huge amount of variables using big data, and it produces solid results; in 2012, the company's loan default rate was a third lower than the industry's. Big data might allow us to better underwrite risks for which we don’t have very good data, such as people who can’t get a driver’s license or commercial risks that currently can only be insured in the surplus lines market. Imagine all of the services, microinsurance and other innovations we’ll be able to develop. From Indemnification to Risk Prevention One of the techniques used with big data is predictive analytics, and pretty much every carrier is experimenting with it. The technique is being used to prevent big mechanical or structural failures: placing sensors on machinery, motors or infrastructure like bridges makes it possible to monitor the data patterns they give off, such as heat, vibration, stress and sound, and to detect changes that may indicate problems ahead. The underlying concept is that when things break down, they generally don’t do so all at once, but gradually over time. If we have sensor data and correlational analysis, we can probably figure out that something is about to break before it actually does. This can allow us to prevent claims from ever happening, thus moving from insurance as a loss-paying service to being a risk-prevention partner. See also: Forget Big Data — Focus on Small Data   Acknowledgement: Much of this article comes from Big Data: A Revolution That Will Transform How We Live, Work, and Think. Yes, you should read it! Yes, we get a small commission if you buy it using that link, and it helps us run and improve InsNerds. Want to support InsNerds? InsNerds is free, it's a labor of love, and it takes a lot of time. We have a ton of fun doing it, and we would really appreciate your support in keeping it running. If you've been helped by one of our articles, if we've helped you grow in your career, if you agree that our content is improving the insurance industry and that we are unique in what we do, please consider donating. We’ll use your donation to deliver even more career- and industry-changing content and to spread the word about that content far and wide in the insurance industry. You can make a recurring donation as small as $1 or a one time donation. This article originally published on InsNerds.com.