Picture a field army of insurance agents, whose mission is to help people live longer and no longer suffer from an addiction that benefits no one, not even the beneficiaries of a life insurance policy. Picture these agents not in fatigues but shirt sleeves, campaigning like citizen soldiers and delivering relief to their respective communities. Picture these agents neither in a battle of arms nor a contest of strength, but a war for the hearts and minds—and lungs—of smokers; of men and women who want to quit smoking; of individuals who want to end their cravings for tobacco and their consumption of nicotine. Picture a constituency equal to this army, whose lives would otherwise end in tragedy and whose deaths would have no meaning in a library of statistics. Picture more of the same in which, according to the Centers for Disease Control and Prevention (CDC), cigarette smoking is responsible for more than 480,000 deaths per year in the U.S., including more than 41,000 deaths resulting from secondhand smoke exposure. See also: Wellness Industry’s No-Good, Very Bad Year   Picture harmless ways to quit smoking. Picture seminars and workshops. Picture corporate partnerships and public meetings, where insurance agents are themselves agents of change rather than a series of changing faces; where the perception of agents changes to the reality of agents as leaders of every community they represent. We cannot afford to perpetuate the current image, which costs $170 billion per year in treatments for tobacco-related illnesses. We cannot afford to continue to ignore the obvious: that the economic cost of smoking a pack of cigarettes a day is $177 per week or more than $9,200 per year. We cannot afford to lose so many so often. We must not habituate ourselves to the emotional wreckage of a deadly habit; because the second we cease to absorb the enormity of this problem—the minute we distance ourselves from the size of this plague—is the moment we abandon our moral authority and the morale of Americans nationwide. Insurance agents have it within their power to do more than sell policies or find the best prices for smokers who want to buy insurance. They have the expertise to speak to smokers about the bottom line, that they can quit smoking without risking their already fragile physical or psychological health. They can help smokers convert the money they waste on cigarettes, as they lay waste to their bodies, into a commodity that protects their health and lines their pockets with cash: insurance. Coverage is what smokers need. An effective—and harmless—way to quit smoking is what these individuals must have. See also: 2018 Workers’ Comp Issues to Watch   Let us resolve to promote healthy living by saving the lives of those whose health is in danger. Let us eliminate the fog of uncertainty and the cloud of indecision, because we must not be enablers of cigarette smoking or passive smokers in our own right. Let us summon that field army into action. Let us earn this victory, so we may celebrate this achievement.

A few years ago, simply applying predictive analytics to insurers’ underwriting practice was enough to gain a competitive edge against the large portion of the market that was still operating with traditional methods. That ship has sailed with increased adoption of analytics, raising the stakes for companies that once enjoyed a first mover advantage. Currently, 60% of insurers have welcomed predictive analytics into decision-making and underwriting processes, and research continues to show correlation between predictive analytics integration in the property & casualty industry and improvement to top and bottom lines. Companies that view analytics as a necessary commodity for modern underwriting instead of the centerpiece to their decision making will find themselves falling short of their competition. The biggest differences between the winners and losers in analytics today is equal parts ideological and technical. In its recently published ROI study, Valen Analytics observed 20 insurance companies, representing $1.8 billion in premium, and compared their loss ratios and premium growth against the industry. The study showed that data-driven insurers consistently outperformed the market on both metrics.

• Between 2012 and 2017, the industry saw its loss ratios improve by 18 points, whereas these 20 carriers averaged improvements that were nearly twice that (loss ratios improved by 35 points).
• Between 2012 and 2017, industry-wide premium grew 18% on average, while the carriers studied grew by 53%.

For the first time since its inception, the ROI study isolated the impact of applied analytics on insurers with concerning loss ratios: those whose loss ratio were greater than 60%. This group of insurers saw loss ratios improve to market average within 12 months, and then outperform the market with each subsequent year. These results underscore the value of predictive analytics in insurance. See also: 3-Step Approach to Big Data Analytics   Below are three best practices that the insurers studied have implemented to draw the most value from their predictive analytics programs: Empower underwriters The considerably positive findings of Valen’s study do not imply that predictive analytics should replace traditional underwriters. Instead, research suggests that predictive analytics tools should aid traditional insurance writers. This year’s study found that underwriter performance improves 3x when they combine predictive analytics with expertise. A well-implemented analytics solution helps underwriters leverage powerful data that they wouldn’t be able to otherwise, and underwriters provide the expertise to make the final decision. In other words, an insurance underwriter’s wealth of knowledge and contextual expertise is a largely irreplaceable asset. Underwriters know the critical variances between the price suggested by the analytics model and the historical habits of a policyholder and can incorporate this information into their decisions. Thus, predictive analytics usage augments an underwriter’s decision-making process rather than supplements it. Streamline the workflow Predictive analytics enable insurers to accurately align price to risk exposure, helping underwriters price policies within the context of an insurer’s risk appetite, and oftentimes allowing insurers to implement straight-through-processing (STP) for specific types of risk. In doing so, insurers can eliminate the need for underwriters to be heavily involved in certain decisions and allow them to focus on the decisions that will have the greatest impact to a book of business. This, again, leverages the expertise of an underwriter. Incorporate the right data Insurers that have incorporated a consortium of anonymized data into their model-building initiatives tend to be better-positioned for growth. This additional information can be crucial to initiatives like expansion across states or business classes, often by identifying risks that might fall in a blind spot of institutional knowledge. In other cases, the incorporation of consortium data will eliminate sample bias in an existing book of business. For instance, an insurer that’s relied heavily on its expertise in knowing how to underwrite low-risk construction accounts in one state to build a data set that determines good risks in a new state will risk overfitting the model, essentially giving it too high a standard. This will leave an insurer vulnerable to underpricing risky accounts without third party data to balance the scales. Consortium data increases the predictive power of models and helped the group in our ROI study of analytically inclined insurers grow premium last year, even as the market declined. See also: Global Trend Map No. 5: Analytics and AI   For the third consecutive year, Valen’s ROI study has identified just how much value applied analytics can add to insurers. The carriers that have leveraged analytics and consortium data and empowered their underwriters have realized significant advantages over competitors to improve both profitability and growth.

Apparently, the wellness industry does not have a monopoly on invalid research. A study came out in The Lancet–the British equivalent of the New England Journal of Medicine — finding that the only safe level of alcohol consumption was: none.  As the principal investigator said: “Alcohol poses dire ramifications for future population health in the absence of policy action today.” This finding generated myriad headlines like this one at CNBC: Or NPR: And how often do those two outlets agree with Fox News? One thing you learn if you hang around wellness promoters long enough is that oftentimes a close perusal of the study in question shows the opposite of what the authors intended. Or, as we often say: “In wellness, you don’t have to challenge the data to invalidate it. You merely have to read the data. It will invalidate itself.” And the same is true here. For example, Denmark leads the world in the number of drinkers — and has life expectancy higher than about 90% of the world’s countries. The lowest alcohol consumers? Pakistan — which ranks #130 in life expectancy. You might say: “Wait, aren’t there many other factors involved in life expectancy?” And the answer is, of course there are. None of those were controlled for in any way in this meta-analysis.  To begin with, the more people drink, the more other unhealthy habits they are likely to have. But that’s not the crux of what is wrong with this study. Two other things should lead wellness professionals to the opposite conclusion: that light drinking is perfectly OK. The remainder of this post addresses those. Absolute risk vs. relative risk Absolute vs. relative risk is one of our (many) pet peeves. Here are two other examples that we have had to smack down:

1. The American Cancer Society warns of a 22% increase in colon cancer among people under 50, but it turns out that absolute rate of colon cancer in younger people is so low that the chances of your life being saved by screening at age 45 are about the same as your chances of being struck by lightning.  The media had a field day with that one, too.
2. Before that, speaking of colons, a study came out showing that red meat increased risk of dying from colon cancer. Once again, it turned out — using the data right in the study — that more people are killed by lightning than by colon cancer due to eating more red meat than average.  Yet, once again, the media had a field day.

From the media’s perspective, this makes sense. After all, who is going to click through on a headline that says: “Low-quality study finds trivial relationship between variables”? See also: 2 Studies of Why Wellness Fails   In the case of this alcohol study, looking behind the headlines proved equally insightful. (And thank you to Aaron Carroll of The New York Times‘ Upshot for suggesting it.) Here is the lead-in: Alcohol is a leading risk factor for death and disease worldwide, and is associated with nearly one in 10 deaths in people aged 15-49 years old, according to a Global Burden of Disease study published in The Lancet that estimates levels of alcohol use and health effects in 195 countries between 1990 and 2016. Based on their analysis, the authors suggest that there is no safe level of alcohol as any health benefits of alcohol are outweighed by its adverse effects on other aspects of health, particularly cancers. Read the first paragraph again. Two observations:

1. Almost no one dies between the ages of 15 and 49, so being responsible for “nearly” 10% of those deaths means that alcohol kills about 0.001% of people in that age bracket every year.
2. The authors have conflated two things: alcohol and excess alcohol. Virtually all of those deaths in that age bracket were due to the latter, a fact that the authors conveniently overlooked when demonizing any level. of consumption.

Reading a bit further in… They estimate that, for one year, in people aged 15-95 years, drinking one alcoholic drink a day [1] increases the risk of developing one of the 23 alcohol-related health problems [2] by 0.5%, compared with not drinking at all (from 914 people in 100,000 for one year for non-drinkers aged 15-95 years, to 918 in 100,000 people a year for 15-95 year olds who consume one alcoholic drink a day) Hello? A 0.5% increase in relative risk? And the increase in absolute risk (not calculated) is four per 100,000 people a year — or 0.004% a year.  Even two drinks a day increases absolute risk only by 0.06% a year. (Once you get beyond two drinks a day, the chance of harm accelerates exponentially…but that’s not news.) What the he** are employees going to consume instead? Our biggest beef with this study is the same as with just about every wellness program: Everything is off-limits. Even foods that are OK in moderation for most people — like full-fat dairy, salt, oils, cholesterol/eggs and red meat — are singled out for criticism by health risk assessments. And now alcohol. Unfortunately, the more foods you demonize, the less likely it is that any employee will pay any attention to any of your dietary pronouncements.  And to the extent they do. well, what are they going to eat instead? Here is Cerner telling people that non-fat yogurt is a “healthier choice.”  Trivia question: What added ingredient makes nonfat yogurt taste good? Here is Optum railing against oils: And Cerner, once again, this time incriminating dietary cholesterol, which of course has no impact on blood cholesterol for most people: Finally, here is Interactive Health hyperventilating about something-or-other in its HRA feedback to an employee. We don’t know what it is other than, given the provenance, it’s wrong. Fortunately, no employee is going to plow through this anyway. Conclusion Treat this alcohol finding the same way you would treat advice from most health risk assessments: ignore it.

FEMA flood maps are not particularly glamorous or technologically exciting. They have done their work for many years and, provided that they are up to date, are an effective way of communicating a generalized level of flood risk. FEMA flood maps have been the primary, flood insurance underwriting tool for the National Flood Insurance Program (NFIP). That program is currently billions of dollars in debt to the U.S. Treasury due to premium subsidization. Also, FEMA flood maps have been used extensively by local governments in their efforts to keep development out of the floodiest areas of the U.S. That has not worked out so well, either, as coastal and riverine developments in the most flood-prone areas have abounded over the past 30-years. As we saw with the Hurricane Harvey disaster in 2017, FEMA flood maps are far from perfect. Nearly a hundred thousand homeowners in the FEMA X Zone (500-year, or 0.2% annual risk) in Houston, who were told that they did not need or were not required to purchase flood insurance, had their homes severely damaged by Harvey’s heavy rainfall flooding. In 2016, we at Coastal Risk Consulting observed that FEMA flood maps did not provide comprehensive enough flood risk assessments to allow individuals and businesses to make accurate “buy, sell, protect and insure” decisions at the property level. So, Coastal Risk identified a number of improvements that could be made to the FEMA flood maps, including downscaling risk mapping to the individual property level, and made them publicly available for purchase at www.floodscores.com for any property in the U.S. FEMA flood maps don’t tell the whole story about your risk of flooding. First, as was seen with Hurricane Harvey flooding in Houston, FEMA flood maps don’t include heavy rainfall flooding risks to your home or business. Properties in FEMA X Zones, which don’t require flood insurance for federally insured mortgages, may definitely need to be insured to protect them from heavy rainfall flooding. Coastal Risk models heavy rainfall flood risk for every property in the U.S.; FEMA flood maps do not. See also: Emerging Market for Flood Insurance   FEMA flood maps also don’t include coastal tidal flooding risks to your home or business. Tidal flooding is a property damage “threat multiplier." When hurricanes come ashore at high tide or even a King Tide, which often occurs in the fall on the Atlantic Coast of the U.S., properties with existing tide flooding are at much greater risk of damage and loss than those that don’t experience tidal flooding. FEMA flood maps do not take this type of flooding into account, now and into the future as sea levels rise. King Tides occurs during the height of hurricane season. Because King Tides are due to astronomy and not weather, scientists know precisely when they will occur. FEMA flood maps also underestimate the height of hurricane storm surge, as compared with NOAA models. NOAA surge models typically show higher water heights than the FEMA Base Flood Elevations (BFEs), which are a key component of the FEMA flood maps. The higher the surge, the greater the economic damage and loss to properties, and the greater the risk of injury and death to those who don’t evacuate in advance of these storms.

In the not-so-distant past, before the insurance industry began to wake to the realities of the digital revolution, the decision-making process and implementation of new IT initiatives took years. In today’s insurance industry, with changes coming at rocket speed and business executives trying to stay in step (or even ahead) of the market, those years are being collapsed into months – or even weeks. Speed to market can make the difference between winning the race and being left at the starting gate. A new approach to IT implementation supports all facets of projects that have complex architectures and detailed documentation requirements. It helps deliver results fast, uncovering issues quickly and early in the process by organizing work into smaller functional segments. Cost savings and higher-quality results come from testing at intervals throughout the project – typical of an iterative approach – instead of at the end, as with a conventional waterfall structure. This is particularly important on the software side. According to an Accenture report on iterative approaches to software testing published earlier this year, “The insurance industry is undergoing rapid and disruptive change. By shifting to the left, insurers have an opportunity to accelerate projects that modernize, rationalize and consolidate their systems. Gone are the days of 24- to 36-month projects. Insurers need to act now and act fast to deliver new products across multiple distribution channels to mobile- and digital-savvy consumers.” See also: Insurtech’s Act 2: About to Start   As with the industry’s focus on customer-centric solutions, speed to market drives this shifting landscape. Insurance solutions today need to move as fast as the market moves, and that means implementations measured in weeks and months, not months and years. An Ernst & Young report, The Digital Opportunity in Insurance, puts this issue into focus: “Fueled by FinTech investments and InsurTech startups, insurance has become a hotbed of digital innovation. In response, insurers must embrace change and rethink business models to move toward a compliant, secure and digitally enabled operating model to enhance customer, employee, partner, and other stakeholder experiences.” The report goes on to show that those who fully embrace digital transformation will be the ones that can successfully meet tomorrow’s customer needs and respond to changing marketplace expectations. “To succeed, insurers must understand what’s possible and take decisive action to deliver value now and ignite long-term growth.” And the next wave of insurtech is already starting to take shape: a rapid migration toward solutions that are customer-centric, as opposed to the traditional carrier- and broker-centric models. At InsurIQ, we’re aiming to be at the vanguard of this movement with the development of solutions for the web-based purchase of insurance products in a shopping cart environment and end-to-end solutions from proposal generation through renewals, including underwriting workflows, policy administration, document fulfillment, premium accounting and producer management. Whatever new-generation approach an insurer decides to implement, a solid consultative relationship with a third-party solutions provider is key. See also: 3 Insurtech Firms Take a Star Turn   The pace of change in the insurance industry, and other businesses, as well, will keep accelerating in the short term and beyond. Carriers that do not emphasize speed to market as a primary objective of their IT initiatives will be left in the dust as other, forward-thinking companies sprint past them, picking up new clients as well as enhanced industry visibility.

What is it with insurtech brand names? Among the insurtechs that SMA is tracking (well over 1,000) are a wide range of names ranging from the clever to the practical to the bizarre. Having personal experience with naming, I can understand the challenges of finding something memorable, not already used, and lacking any negative connotations. There is always the option of functional naming; for example, Insuresoft clearly creates software solutions for the insurance industry. When I was recently in a whimsical mood, I decided to do an exercise to categorize insurtech brand names by a number of topics or areas, including food, animals and human names. This is a sampling of what I found: Food One could make a whole meal out of insurtech names. The main course could be Oyster, focused on workers’ comp. Fruit sides might be Pear Insurance or Pineapple. There are plenty of drink options with H2O, Lemonade and Soda Insurance. And dessert – everyone’s favorite – is not lacking in options, with Cake or Pie, or maybe even Marshmallow. See also: 3 Insurtech Firms Take a Star Turn   Animals Comic George Carlin used to wonder who took all the blue food. (Blueberries are not blue; they are purple!) But there are plenty of blue animals in insurtech, including Blue Owl, Blue Leopard and Blue Zebra. Then we have animals with descriptors like Bold Penguin, Pandadoc and PrecisionHawk. The insurtech menagerie also includes Hippo, Dolphin, Canary, Rhino and even a hybrid in CatDogFish. There is even a regular Zebra to go along with Blue Zebra. Human Names Why not anthropomorphize insurtechs? We do it with everything else. There is Bob – and if he gets lost there is FindBob. Abe, Albert, Frankie, Gabi and many others are named after people. Then there is Hi Marley, which does a nice job of creating something unique that also relates to the company's solution – leveraging texting and messaging platforms to communicate with policyholders and claimants. There is no question that many of these names are becoming known in the insurance industry, but there are pros and cons for using these types of names. One caution for those selecting names – think about search engine optimization (SEO) and how individuals will discover your site. With enough money, brand visibility can be built for any name. But in many cases funding is limited in the beginning stages, and the focus is more on building the solution and getting successful partnerships and projects underway. I have personally had great difficulty finding any information on some of these insurtechs – even just navigating to their websites – due to names that are so common that SEO is difficult. See also: Insurtech’s Act 2: About to Start   Another piece of advice (although I don’t claim to be a branding expert): Two-word names (separate or conjoined) offer more options for uniqueness than one-word names – Cake Insure, Young Alfred and TechCanary would be examples. Of course, brands are built, and companies succeed, based on the strength or their offerings, their innovation, their customer relationships/experience and many other factors. But I, for one, am glad that insurtechs are choosing names that are fun and interesting. So, what’s in a name? I guess it’s what you make of it.

Throughout a number of the recent conferences I have attended and conversations I have had with my clients, colleagues and incumbents in the space, the question of "what makes an insurtech/innovation initiative a success?" keeps coming up. This week, I explore the critical success factor. If you haven’t figured out by now through my writing, I like the softer side of our business. A lot. Perhaps it is the salesman in me and the fact that I have always been more relationship-focused than details-focused. In my most recent management roles, I always tried to delegate the detail-oriented tasks to the people on my team who were good at it, and have them provide me a summary of their findings so I could go manage the relationship/conversation with our various stakeholders. When looking at insurtech solutions, I tend to focus on customer outcomes more than the actual tech behind it. Yes, the tech is cool and interesting for me. The application of it is even more impressive. I’ve explored this concept a few times – here, here and here. This week, I’m going to take a slightly different look. That’s because there is one answer that keeps coming up to the question of "What makes an insurtech initiative a success?’, regardless of if I speak to incumbents, investors, startups/technology providers or consultants. That answer – the team. The team is what is ultimately going to make an insurtech initiative a success. There are some key factors that should be common, regardless of where you sit. In addition, there may be specific nuances to take a look at depending on what role you play in the initiative being undertaken. This week, I take a look at the common factors that should be in place, the different perspectives one may look at, as well as a revised formula for my "insurtech formula for success." See also: An Insurtech Reality Check   What are the common factors one should look at when partnering? To set the stage for what’s to follow, I would like you all to imagine the different types of partnering that can be out there for any insurtech initiative (for initiative, I mean an implementation, investment or building of a company). I will name a few (though this is not an exhaustive list):

• Incumbents (reinsurers, carriers, brokers, agencies, etc) partnering with startups/technology providers
• Startups/technology providers getting funding from investors (on the flip side, investors investing in a startup)
• Technology providers partnering with each other to offer a more robust solution to incumbents
• Consultants being used by incumbents, investors and technology providers to help implement, assess or "strategize"

Regardless of the type of partnership, there are a few key factors that should exist if you are going to partner with another party for an insurtech initiative.

1. Knowledge – Does this person/company have the knowledge to understand what pain point they are trying to tackle? Do they have knowledge of the industry? Etc.
2. Trust – Do I actually trust this person/company? I recently read this article from Inc., which outlined Google’s study on trust and how to demonstrate it. It’s quite fascinating, and I encourage you to have a read.
3. Likeability – Do I like this person/company? Even if I don’t necessarily like them, am I going to be able to tolerate them for the years to come? Many partnerships are long(ish)-term. If you don’t like someone that you are going to have to work with for a while, that could pose a big problem down the road.

These three factors, knowledge, trust and likeability, form the foundation for the types of relationships that I will describe below and must be inherent if you are to partner with someone (regardless of where you sit). What the factors are to look for, depending on where you sit Now that we’ve established a foundation, let’s look at the different types of relationships from a startup/technology vendor, incumbent and investor perspective. Startup/technology providers The three relationships I will look at here are; their incumbent partners (primarily for B2B), their team and their investors. The types of questions they should be asking for each of these parties are:

• For their incumbent partners (some of this is covered in the insurtech startup guide):
  1. What is their approach to innovation?
  2. How much has management bought into it? Are there KPIs assigned to initiatives, and what are they?
  3. Do they have a dedicated budget, resource, need and timeline for the proposed initiative?
  4. What other initiatives have they done, and who have they partnered with?
  5. How do they treat the startup/technology provider (does it feel as if they are a partner or just another vendor)?

• For their team:
  1. Do they have the right mindset to work in a startup (in the case of an early-stage company)?
  2. How flexible are they in working style?
  3. How do they handle pressure?
  4. What are their expectations?
  5. Do they want to build/grow something, or are they OK with the status quo?

• For their investors:
  1. How much control (other than just % of equity) is the investor going to want after the investment is made?
  2. How much is the investor willing to mentor/coach them?
  3. What resources will they put in place to help grow their business, while still letting them (the founders) run it?

Incumbents Using the term "incumbents," I mean any reinsurer, carrier, MGA, broker, etc. that may be looking to partner with an insurtech startup/technology provider. The two relationships I will cover here are; their technology partners and their internal teams. The types of questions they should be asking for each of these parties are:

• For their insurtech startup/technology providers (some of this is covered in the carrier guide):
  1. How much do they understand the insurance industry/specific line of business(es) we operate in?
  2. How much do they understand our specific company and nuances? (Incumbents – while it is nice that they do their research beforehand about you, you also need to bring them on this journey of understanding if you really want them to help)
  3. What has been their current experience with other partners? Not just from an ROI perspective, but also an implementation perspective.
  4. How patient are they to work within the confines of corporate governance?
  5. What is their approach to security and handling data?

• For their internal teams running an innovation initiative:
  1. For the person/team that is leading an initiative, do they have the right balance of understanding of governance plus the agility/flexibility to push an agenda that starts with change?
  2. Is this person/team going to seek to overcome internal pushback or see it as a wall that cannot be scaled?
  3. Is this person/team going to have the leadership ability (and swagger) to be the voice of change for our organization?

Investors I haven’t worked for an investment firm yet, so, it would be difficult for me to assess what sort of internal requirements they should have in place for their teams. I will only take a look at the types of questions they should be asking for the companies they invest in. These are similar to the questions above that incumbents should be asking to their insurtech startup/technology partners.

1. How much do they understand the insurance industry/specific line of business(es) they operate in?
2. How open are they to ceding some ownership of their company?
3. How coachable are they? Are they willing to have changes made/suggested to their business model, or do they feel like they know it all and are not willing to change?
4. How diverse is their team (from a variety of perspectives)?
5. How have they come up with their solution?
6. What sort of drive to they have, and how have they handled adversity?

I’m sure for all three of these parties, there are more questions to ask. What questions do you think of (depending on where you sit)?  Please comment below, I’m interested to hear! See also: 10 Trends at Heart of Insurtech Revolution   With all this, the "insurtech formula for success" needs an update Back in early May, I posted the insurtech formula for success as: Insurtech Success = customer experience + dollars and cents + compliance + applicable technology Whereas:

• Customer experience = what part of the customer experience are you trying to make better? Is it quoting/purchasing? Is it claims? Is it servicing? ("Customer" may not always mean policyholder. "Customer" may also mean the employees or partners -- like auto body shops -- that are part of the value chain. Think about which customer you are trying to solve a pain point for.)
• Dollars and cents = Does the solution help to increase sales or reduce costs? Our industry is a business, and that means we get measured by dollars and cents. Ultimately, a solution that is being put in place should do one of these things – increase sales or reduce costs.
• Compliance = is the solution in line with regulation? Is it compliant with existing internal guidelines? Is it fair to customers? Is it secure?
• Applicable technology = what is the right technology that should be used to meet this need? This should be the easiest part of the equation once the other three are determined.

This week, I’m making a slight change to the formula, which has also been reflected in the original article. Insurtech Success = (customer experience + dollars and cents + compliance + applicable technology)^team I love the circumflex/hat symbol (^) so much in this formula. It fits so aptly here. For those not as familiar, it literally reads out as "to the power of." Customer experience, dollars and cents, compliance and applicable technology are extremely important. They will only succeed by the power of the team that is driving it. Because after all, it’s not "you" nor "I," it’s "we" and "us’". You can find the article originally published here.

I have a complaint against insurance agents, not a claim for them to file. Rather, I do have a claim—and reason to complain—regarding their use of marketing copy. Too much of what these agents write says too little about who they are and what they do. Too much of what they say sounds too similar for there to be a major difference. It seems as if agents use the same words with slight variations in structure, to optimize their results with search engines. Search as I may, and I have searched far and wide, I cannot find what I want: originality. The lack of original copy—the general absence of creativity in business writing—is a symptom of personal laziness and professional indifference; as if it is acceptable to commit plagiarism, at home or abroad; as if standards of honesty do not matter; as if the theft of intellectual property is less a sin than a sign of flattery; as if numbers nullify the underlying wrongness of an act; as if the more common a problem is, the less problematic it becomes. Let me restate the problem: An insurance agent who does not care about the message he markets is not in the market to attract or retain clients. And yet, some marketers—including a contributor to Entrepreneur—all but admit that plagiarism is inevitable, because it is hard to come up with a unique idea. This excuse in the form of an explanation should not lessen the seriousness of this offense. Difficulty does not, after all, denote a license to steal. It does not condone—no one should misconstrue it to mean—that misappropriating content is fine, so long as one’s clients are content. See also: Underwriting, Marketing: Sync Up!   Insurance agents need to stop outsourcing their messaging to the unskilled and the unethical. It does not profit an agent to be at the top of Google search but at the bottom of what no search engine can retrieve: one’s soul. An agent who cannot ensure his own integrity should not attempt to insure anyone or anything. This rule applies to all businesses, but it matters most to how agents conduct themselves regarding the business of insurance; because each sale is an exchange of dollars and a transaction of trust; because you should not do business with someone you do no trust. If you do not trust the originality of what an agent says, why would you entrust this person with your money—or agree to buy life insurance from this man or woman? If insurance agents take the lead on this issue, it will benefit their industry and the business community as a whole. The biggest beneficiaries will be the people who have or want to purchase insurance. See also: Global Trend Map No. 8: Marketing   If they trust the originality of the message, they are more likely to listen to the messenger. They are more likely to insure themselves, without the urge to reassure themselves that a particular agent is reliable and trustworthy.

The following is an excerpt from a white paper, available in full here.  There have always been a lot of independent insurance agencies in the marketplace, just as there are today. But the competition seems to be increasing by the hour, thanks largely to the proliferation of digital technology and online marketing. Consequently, most agents hear the following comments quite frequently:

• “You insurance people are all the same.”
• “Sure, you can bid on my business insurance; we shop it every three years.”
• “Can you give me a quote on my business insurance? I’m just trying to keep my current agent honest.”
• “I’d like a quote on my automobile insurance.”
• “I noticed my homeowners insurance increased $25. Could you shop it around?”

And those are just a few examples. There are so many others because consumers have so many additional purchasing options that didn’t exist until recently. Whether it’s personal or commercial lines, consumers are constantly being educated that insurance is all about price. So if you sound like, look like and act like every other insurance agent or agency, people will assume that’s who you are — like everyone else. This leads to price-only selling, practice quoting and unpaid consulting. See also: A Contrarian Looks ‘Back to the Future’ One of the reasons the marketplace is so crowded is that most agencies have not differentiated. They simply do not have a compelling story of differentiation. When asked, “Why should I do business with you/your agency?” most will respond with the same standard, boring, "Generic Five" lines.

1. “We give great service.”
2. "We're local."
3. “We represent all of the major insurance companies.”
4. “We’ve been in business for 100 years.”
5. “We have the best people.”

That last one annoys me. Really? Is there a vortex in the universe that sucked all the best people in our industry into one agency?! Don’t get me wrong. I find that most agencies provide excellent reactive service, represent a slew of great companies, have been around a long time and have some great people. There’s no doubt about that. There’s just not a compelling reason why I should even consider you. You’re just not different. Furthermore, the vast majority of agencies simply have no formal, systematic selling and marketing process. For most, the “selling system” (or set offense, as I like to call it), is still focused on the old way of selling: Look, Copy, Quote and Pray. I’ll look at your policies, copy the information, give you a quote and then pray that the premium I present to you is less than what you’re paying today. I’ll continue praying that you don’t take my quote, give it to your current agent and tell the agent, “Match it, and you get to keep the business.” Do you have an “agency's way” of selling and marketing your products and services? As an agency owner or producer, how would you answer the following:

• What’s your 30-second commercial? What’s your two-minute infomercial?
• What’s your unique selling proposition (USP) — the unique and appealing ideas and things that separate you from all other “me too” competitors?
• What’s different in your process of risk assessment, risk transfer and risk prevention?
• Do you and all of your team members know your top five PODS, or points of differentiation, and do you actually deliver on them?

I realize that changing the consumer’s perception remains a challenge when TV’s Flo and the gecko saturate the marketplace. They, and others, spend billions on advertising. It's no wonder the consumer thinks it’s all about price! Plus, once you click through to a site, it has your data and tracks you indefinitely. This is not just in personal lines. Small to mid-sized commercial accounts are getting the exact same message. That’s because it’s irresistibly easy for the consumer or business owner to call a toll-free number or “click here for a free quote” on hundreds of different websites. Again, please don’t misunderstand my point. The reality is that when properly used, digital marketing can help aggressive independent agents fill their pipelines (although I fear that most are filled with suspects, not future ideal clients). But while digital marketing can provide you with opportunities, it’s up to you to seize them. Once you get an email, phone call or online alert, what do you do with it? See also: Future of Insurance Looks Very Different   When I talk to agency owners about the number of clients they’ve received from online contacts, I hear vastly different stories. What is one doing to get the prospect’s business that the other one isn’t? When an opportunity arrives, what are you/your producers doing and saying to connect with the prospect and close the deal? What’s your process, and how do you follow up on it?

Artificial intelligence is the new electricity. We hear it will fundamentally shift the balance of power between labor and capital, mostly by rendering labor obsolete. It will enable and empower transformative technologies that will rearrange the sociopolitical landscape and may lead to humanity’s transcendence (or extinction) within our lifetimes. As it changes the world, it will necessarily rewrite the rules of insurance. That’s the myth, and the nature of the headlines. Interestingly, insurance is heavy on intellectual property (think of proprietary underwriting models), technology and data. And AI is hungry; hungry for data, of course, but also hungry for systems that can be automated and for proprietary classification problems that can be improved. That places insurance right in the appetite of artificial intelligence and its promise of transformation. If we want to act on artificial intelligence’s transformational potential,  we need to understand what it actually is, separate the technologies from the hype and develop a practical understanding of what is required to implement AI-powered solutions in the insurance sector. This article will highlight these three steps and offers a realistic approach for carriers to take advantage of the opportunities. Defining Artificial Intelligence Unfortunately, our first step is also our hardest, as a working definition of artificial intelligence is difficult. The scope of the term AI is broad, and it requires careful consideration to avoid becoming hopelessly confounded with its own hype. It is also challenging to come to a clear definition of natural intelligence, which leaves us struggling for a definition of artificial intelligence because the latter is so often compared to the former. AI tends to be discussed in two flavors. The first is general artificial intelligence (also, artificial general intelligence and strong AI). GAI is machinery capable of human-level cognition, including a general problem-solving capability that is potentially self-directed and broadly applicable to many kinds of problems. GAI references are accessible through fictional works, such as C-3PO in Star Wars or Disney’s eponymous WALL-E. The most important feature of GAI is that it does not currently exist, and there is deep debate about its potential to ever exist. The second is usually referred to as narrow AI. Narrow AI is task-specific and non-generalizable. Examples include facial recognition on Apple’s iPhone X and speech-to-text transliteration by Amazon’s Alexa. Narrow  AI looks and feels a lot like software or, perhaps, predictive models. Narrow AI can be described as a class of modeling techniques that fall under the category of machine learning. See also: Seriously? Artificial Intelligence?   What is machine learning? Imagine a set of input data; this data has one or more potential features of interest. Machine learning is a technique for mapping the features of input data to a useful output. It is characterized by statistical inference, as advanced techniques often underlie machine learning predictive models. Through statistical modeling, software can infer a likely output given a set of input features. The predictive accuracy of machine learning methods increase as their training data sets increase in size. As the machine ingests more data, it is said to learn from that data. Hence, machine learning. Perhaps most important of all, machine learning (as an implementation of narrow AI) is real and here today; for the remainder of our discussion when we say "AI," we mean narrow AI or machine learning. Beyond the Hype The hype around AI and its potential is extensive. Silicon Valley billionaires opine on the potential implications of the technology, including comparing its power to nuclear weapons. Articles endlessly debate if and how quickly AI will structurally unemploy vast swaths of white collar workers. MIT’s Technology Review provides a nice summary of the literature, stating that up to half of all jobs worldwide could be eliminated in the next few decades. AI may well have this kind of impact. And the social, political and economic implications of that impact, especially around questions of potential large-scale unemployment, deserve careful long-term consideration. However, executives and business owners need to evaluate technology investments today to improve their current competitive position. From that perspective, we find it more practical to focus on examining which existing tasks could be automated by AI today. Enter Pigeons In 2012, researchers trained pigeons to recognize people based only on their faces as part of a study on cognition. Suppose you had millions of face-recognizing pigeons; this force of labor could be deployed in a comprehensive facial recognition system -- a system remarkably similar in function to the facial recognition AI of devices like modern smart phones. It turns out pigeons have also been trained to recognize voices, spot cancers on X-rays and count, among a host of other tasks related to headline-grabbing AI achievements. The metaphor is admittedly silly. Instead of pigeons, imagine an army of virtual robots capable of classifying information from the real world to produce a machine-readable data set. In machine learning language, these robots take unstructured data and make it structured. Said robots resemble the automation machinery of a factory; like spot welders tirelessly joining steel members to form automobile frames, our virtual robots tirelessly recognize if a face is featured in a photograph. In contemplating the question, what could be automated with AI, a useful starting place is the army of robots (or pigeons!). For example:

• What existing analyses could be improved or optimized? Could pricing or underwriting be improved using better classifiers or non-linear modeling approaches?
• What data currently exist at the firm that could be made available for new types of analysis? Claims adjusters’ notes can be processed by natural language algorithms and cross-referenced with photos of physical damage or prior inspections.
• What data would you analyze if it could be made available? What if you could listen to all the policyholder calls received by your customer service department and annotate which questions stumped the customer service representatives? Or which responses lead to irritation in the policyholders’ voices?

Bringing AI to Insurance What is an insurer to do? Start by not fretting. We propose two considerations to facilitate a sleep-at-night perspective. First, insurers are already good at AI or its precursor technologies. The applicability of AI in the present and near future is entirely based on narrow AI technologies. For example, natural language processing and image recognition are both machine learning implementations with working business applications right now. Both use predictive models to achieve results. The software may be artificial neural networks trained on vast data sets, but they are nonetheless conceptually compatible with things insurance carriers have used for years, like actuarial pricing models. The point is that the application of AI is an incremental step forward in the types of models and data already applied in the business. Second, sorting through the hype requires a staple of good business decision making: the risk-cost-benefit analysis. Determining which technologies are worth investment is within scope for decision makers that otherwise know how to make selective investments in growing the capabilities of their firm. The problems faced by a carrier are much bigger than sorting out AI if management lacks the basic skillset for making business investments. Providing an inventory of every application of AI is beyond the scope of this article. DeepIndex provides a list of 405 at deepindex.org, from playing the Atari 2600 to spotting forged artworks. Instead, suppose that AI, like electricity, will be broadly applicable across industries and functions, including the components of the insurance value chain from distribution to pricing and underwriting to claims. The goal is to identify and implement the AI-empowered solutions that will further a competitive advantage. Our view is that carriers’ success with AI requires three key ingredients: data, infrastructure and talent. Data: AI might be considered the key that unlocks the door of big data. Many of the modeling techniques that fall under the AI umbrella are classification algorithms that are data hungry. Unlocking the power of these methods requires sufficient volume of training data. Data takes several forms. First, there are third party data sources that are considered external to the insurance industry. Aerial imagery (and the processing thereof) to determine building characteristics or estimate post-catastrophe claims potential are easy examples. Same with the vast quantities of behavioral data built on the interactions of users with digital platforms like social media and web search. Closer to home, insurance has long been an industry of data, and carriers are presumed to have meaningful datasets in claims, applications and marketing, among others. Infrastructure: Accessing the data to feed the AI requires a working infrastructure. How successfully can you ingest external data sources? How disparate and unstructured can those sources be? Cloud computing is not necessarily a prerequisite to successful AI, but access to vast, scalable infrastructure is enabling. Are your information systems equipped, including security vetting, to do modeling in the cloud? Can you extract your internal data into forms that are ready to be processed using advanced modeling techniques? Or are you running siloed legacy systems that prevent using your proprietary data in novel ways? Talent: Add data science to the list of AI-related buzzwords. We claimed earlier that many of the advancements attributed to narrow AI are predictive models conceptually like modeling techniques already used in the insurance industry. However, the fact that your pricing actuary conceptually appreciates an artificial neural net built for fraud detection using behavioral data does not mean you have the in-house expertise to build such a model. Investments in recruiting, training and retaining the right talent will provide two clear benefits. The first benefit is being better equipped to do the risk-cost-benefit analysis of which data and methods to explore. The second is having the ability to test and, ultimately, implement. See also: 4 Ways Connectivity Is Revolutionary   In Aon’s 2017 Global Insurance Market Outlook we explored the idea of the third wave of innovation as propounded by Steve Case, founder of AOL, in his book, “The Third Wave: An Entrepreneur’s Vision of the Future.” The upshot of the third wave for insurers was that partnership with technology innovators, rather than disruption by them, would be the norm. This approach applies now more than ever as technological innovators continue to unlock the potential of AI. If you don’t have the data, or the infrastructure, or the talent to bring the newest technologies to bear, you can partner with someone that does. Artificial intelligence is real. While the definitions are somewhat vague - is it software, predictive models, neural nets or machine learning - and the hype can be difficult to look past, the impacts are already being felt in the form of chatbots, image processing and behavioral prediction algorithms, among many others. The carriers that can best take advantage of the opportunities will be those that have a pragmatic ability to evaluate tangible AI solutions that are incremental to existing parts of their value chain. “If you don’t have an AI strategy, you are going to die in the world that’s coming.” Devin Wenig CEO, eBay Maybe true, but that does not make it daunting. The core of insurance is this: Hire the right people, give them the infrastructure they need to evaluate risk better than the competition and curate the necessary data to feed the classification models they build. AI hasn’t, and won’t, change that.