I doubt that Google and Microsoft ever worried about the prospect that a book retailer, Amazon, would come to lead one of their highest-growth markets: cloud services. And I doubt that Apple ever feared that Amazon’s Alexa would eat Apple’s Siri for lunch.
For that matter, the taxi industry couldn’t have imagined that a Silicon Valley startup would be its greatest threat, and AT&T and Verizon surely didn’t imagine that a social media company, Facebook, could become a dominant player in mobile telecommunications.
But this is the new nature of disruption: Disruptive competition comes out of nowhere. The incumbents aren’t ready for this and, as a result, the vast majority of today’s leading companies will likely become what toast—in a decade or less.
Note the march of Amazon. First it was bookstores, publishing and distribution, then cleaning supplies, electronics and assorted home goods. Now, Amazon is set to dominate all forms of retail as well as cloud services, electronic gadgetry and small-business lending. And the proposed acquisition of Whole Foods sees Amazon literally breaking the barriers between the digital and physical realms.
This is the type of disruption we will see in almost every industry over the next decade, as technologies advance and converge and turn the incumbents into toast. We have experienced the advances in our computing devices, with smartphones having greater computing power than yesterday’s supercomputers. Now, every technology with a computing base is advancing on an exponential curve—including sensors, artificial intelligence, robotics, synthetic biology and 3-D printing. And when technologies converge, they allow industries to encroach on one another.
Uber became a threat to the transportation industry by taking advantage of the advances in smartphones, GPS sensors and networks. Airbnb did the same to hotels by using these advancing technologies to connect people with lodging. Netflix’s ability to use internet connections put Blockbuster out of business. Facebook’s WhatsApp and Microsoft’s Skype helped decimate the costs of texting and roaming, causing an estimated $386 billion loss to telecommunications companies from 2012 to 2018.
Similarly, having proven the viability of electric vehicles, Tesla is building batteries and solar technologies that could shake up the global energy industry.
Now, tech companies are building sensor devices that monitor health. With artificial intelligence, these will be able to provide better analysis of medical data than doctors can. Apple’s ResearchKit is gathering so much clinical-trial data that it could eventually upend the pharmaceutical industry by correlating the effectiveness and side effects of the medications we take.
As well, Google, Facebook, SpaceX and Oneweb are in a race to provide Wi-Fi internet access everywhere through drones, microsatellites and balloons. At first, they will use the telecom companies to provide their services; then they will turn the telecom companies into toast. The motivation of the technology industry is, after all, to have everyone online all the time. The industry’s business models are to monetize data rather than to charge cell, data or access fees. They will also end up disrupting electronic entertainment—and every other industry that deals with information.
The disruptions don’t happen within an industry, as business executives have been taught by gurus such as Clayton Christensen, author of management bible “The Innovator’s Dilemma”; rather, the disruptions come from where you would least expect them to. Christensen postulated that companies tend to ignore the markets most susceptible to disruptive innovations because these markets usually have very tight profit margins or are too small, leading competitors to start by providing lower-end products and then scale them up, or to go for niches in a market that the incumbent is ignoring. But the competition no longer comes from the lower end of a market; it comes from other, completely different industries.
The problem for incumbents, the market leaders, is that they aren’t ready for this disruption and are often in denial.
Because they have succeeded in the past, companies believe that they can succeed in the future, that old business models can support new products. Large companies are usually organized into divisions and functional silos, each with its own product development, sales, marketing, customer support and finance functions. Each division acts from self-interest and focuses on its own success; within a fortress that protects its ideas, it has its own leadership and culture. And employees focus on the problems of their own divisions or departments—not on those of the company. Too often, the divisions of a company consider their competitors to be the company’s other divisions; they can’t envisage new industries or see the threat from other industries.
This is why the majority of today’s leading companies are likely to go the way of Blockbuster, Motorola, Sears and Kodak, which were at the top of their game until their markets were disrupted, sending them toward oblivion.
Companies now have to be on a war footing. They need to learn about technology advances and see themselves as a technology startup in Silicon Valley would: as a juicy target for disruption. They have to realize that the threat may arise in any industry, with any new technology. Companies need all hands on board — with all divisions working together employing bold new thinking to find ways to reinvent themselves and defend themselves from the onslaught of new competition.
The choice that leaders face is to disrupt themselves—or to be disrupted.
Big data has the opportunity to end discrimination.
Everyone creates data. Whether it is your bank account information, credit card transactions or cell phone usage, data exists about anyone who is participating in society and the economy.
At Root, we use data for car insurance, an industry where rating variables such as education level or occupation are used directly to price the product. For a product that is legally mandated in 50 states, the consumer’s options are limited: give up driving and likely your ability to earn a living or pay a price based on factors out of your control.
Removing unfair factors such as education and occupation from pricing leaves room for variables within an individual’s control — namely: driving habits. In this way, data can level the playing field for all consumers and provide an affordable option for good drivers whom other companies are painting with a broad brush. In the lon term, everyone wins as roads become safer and driving becomes prohibitively expensive for irresponsible drivers.
This is just one example where understanding the consumer’s individual situation deeply allows for more precise — and more rational — decision making.
But we know that the opportunity of big data goes beyond the individual. For example, the unfair practice of naively blanketing entire countries, religions or races unfairly as “dangerous” is a major topic in the news. What happens if you apply the lens of big data to this policy?
With the increased availability of data, we are able to better understand the causal paths between data generation and an event. The more direct the causal path, the better predictions of future events (based on data) will perform.
Imagine having something as trivial as GPS location data from a smartphone on a suspected terrorist. Variables such as having frequent cell phone conversations with known terrorists or being located within five miles of the last 10 known terrorist attacks will allow us to move away from crude, unjust and discriminatory practices and toward a more just and rational future.
Ahmad Khan Rahami, who placed bombs in New York and New Jersey, was flagged in the FBI’s Guardian system two years earlier. The agency found there weren’t grounds to pursue an investigation — a failure that may have been averted if the FBI had better data capture and analysis capabilities. Rahami purchased bomb-making materials on eBay and had linked to terrorist-related videos online before his attempted attack. Dylann Roof’s activities showed similar patterns in the months leading up to his attack on the Emanuel AME Church in Charleston, SC.
The causal path between a hate-crime or terrorist attack and the actions of Dylann Roof and Ahmad Khan Rahami is much more direct than factors such as religion, race or skin color. Yet we naturally gravitate toward making blanket assumptions, particularly if we don’t understand how data provides a better, more just approach.
Today, this problem is more acute than ever. Discrimination is rampant — and the Trump administration’s ban on travel is unacceptable and unnecessary in the era of big data. For those unmoved by the moral argument, you should also know policies like the ban are hopelessly outdated. If we don’t begin to use data to make informed, intelligent decisions, we will not only continue to see backlash from discriminatory policies, but our decision making will be systematically compromised.
The Privacy Red Herring
Of course, if data falls into the wrong hands, harm could be done. However, modern techniques for analyzing and protecting data mitigate most of this risk. In our terrorism example, there is no need for a human to ever view GPS data. Instead, this data is collected, passed to a database and assessed using a machine learning algorithm. The output of the algorithm would then direct an individual’s screening process, all without the interference of a human. In this manner, we remove biased decision making from the process and the need for a “spy” to review the data.
This definitely provides a challenge for the U.S. intelligence community, but it is an imperative one to meet. If used responsibly, analytics can provide insights based on controllable and causal variables. The privacy risk is no longer a valid excuse to delay the implementation of technologies that can solve these problems in a manner that is consistent with our values.
This world can be made a much better and safer place through data. And we don’t have to sacrifice our privacy; we can have a fair world, a safe world and a world that preserves individual liberties. Let’s not make the mistake of believing we are stuck with an outdated and unjust choice.
The insurance industry has been historically slow to embrace technology, lagging behind even the banking sector. This attitude is understandable — the industry relies heavily on historical data, which is generally not available for new technology, and the industry is immensely risk-averse, as even one failure to live up to their commitments could be devastating to an insurer.
Technology is putting pressure on the insurance industry from three sides.
New customer demand
The first is customers, who have grown accustomed to an easy, Facebook-like experience in interacting with large service providers. Current insurance products are far too generalized and one-size-fits-all to appeal to a customer base that is expecting easily individualized products. Technology like usage-based insurance can make a provider significantly more appealing in this respect by making it possible to only pay the premium for risk actually taken; wearables can revolutionize the healthcare insurance market by allowing for truly personalized pricing.
The second source of pressure comes from competitors. Not only will consumers be more likely to give their business to a digital-native insurer, but entire new kinds of exposure are opening that will give a challenger an opportunity to strike. The cybersecurity market is growing everywhere, along with the pressure to contain and manage the risk better, yet traditional insurers are slow to make convincing offers to threatened customers. In addition, the blockchain is making a more decentralized market possible: While insurers could so far count on the immense need for capital as a barrier to entry, the blockchain could finally bring the transparency and reliability needed to make dynamic, small-scale insurance underwriting possible.
Lastly, technology provides new avenues to cut costs in internal processes and pricing products by making available huge sources of data and enabling its more efficient analysis. Insurers currently spend a lot of money on services that aren’t in their core specialty- processing claims, detecting fraud or manually assessing new risk. New algorithms for predicting risk, for example using machine learning, will allow for vast automation of the underwriting process, and managing contracts and identities with the blockchain will reduce the resources needed for fraud detection. New diagnostic technology, like wearables for healthcare or GPS trackers for cars, is bringing a new wealth of data that may balance the lack of historical data that is currently keeping insurers in as-is mode.
With those pressures, however, come a number of important opportunities in three areas: underwriting automation, connected devices and cybersecurity
Automation in the insurance industry can make underwriting both more efficient and more precise, with different lines offering different opportunities for automation.
Insurers are currently using automation primarily to support underwriters and aid in triage, with only a fifth saying their primary objective is to fully automate the process. What kind of automation is possible varies between business lines, but even in the most advanced segment, personal lines, only 42% of insurers say they have “mastered or almost mastered” automation. At the bottom end, life insurance, 80% of insurers say they are struggling or just getting started with automation.
Insurers are focusing on personal lines and small and mid-market commercial to expand their automated underwriting capacities, with more than 40% saying they will increase their spending in each field. However, in line with being late adopters, it is estimated that only 10% of insurers will have an algorithmic business strategy in 2019 that makes use of more advanced techniques like machine learning, which could make automation viable for more involved lines like health.
For most policies in motor, home and life, an underwriter reviews between eight and 15 factors. Current automation systems for life insurance have similarly small data requirements, with around half the systems drilling down into no more than 10 questions, and a third of them asking as many as 60 follow-up questions. Most systems incorporate lab data and prescriptions databases. These amounts of data are small compared with what a sophisticated automated system could use to assess risk.
As a naturally data- and analysis-heavy industry, insurance stands to profit from advances both in the sophistication of automation and in its affordability. As an industry that is also conservative and late to adopt technology, it faces the risk of being outflanked by a less risk-averse challenger that’s willing to bet on automation skills.
Insurers have for more than 25 years used primitive systems to fully automate small-scale risk in simple lines (for example, travel insurance), or to aid their underwriters by more effectively triaging requests and directing them to the underwriter that’s best suited for them, or to do some preliminary analysis. These systems generally rely on simple rules and are seen as supporting underwriters. As automation products become easier and cheaper to implement, and new decentralized technologies like blockchain make small-scale underwriting more transparent and available, we can expect their share to increase incrementally.
More importantly, insurers are also facing a new wealth of data both for historical risk research and for better assessment of new risk that could fundamentally change the way risk is priced. However, traditional systems are not equipped to deal with these amounts of data, and few insurers are ready to implement the machine learning technology that would be. The problem is that modern machine-learning can produce results but cannot generally explain them. Policy underwriters are naturally skeptical of underwriting risk based on a technology that provides no justification for a pricing beyond the rigor of its setup and the vastness of the data it has been trained on. However, insurers already use fundamentally similar systems for assessing their underwriters’ competence—if a junior underwriter repeatedly prices a risk outside of the usual range the same way a more senior underwriter would, the junior underwriter will be allowed to price those risks without supervision. If insurers can learn to trust this approach with technology, too, they will embrace machine learning.
Underwriting automation will become a significant field of innovation around both reducing staffing and coping with the new amounts of data, with each business line requiring its proper automation technology. As risk assessment algorithms become more reliable and executives more confident in them, they will be able to make low-level underwriting both cheaper and more consistent. As new sources of data for risk analysis become available, insurers will have to use machine learning algorithms to be able to make sense of the vast amounts of data.
Connected devices in insurance describes the network of smartphones, wearables, home diagnostics and other internet-connected devices that form one of the fastest growing spaces within insurtech. This stands to make available a new wealth of data for insurers to handle better pricing and encouragement of risk-decreasing customer behavior.
Wearables and Diagnostics
87.7 million U.S. adults, or about 38%, are expected to be using a wearable device in 2019, a growth mainly fueled by smartwatches and wristbands. VCs invested around $3 billion in IoT startups worldwide in 2015, and 38 million European and North American households are expected to have a smart thermostat in 2018, with two-thirds of those lying in North America. Nearly two-thirds of consumers already own or plan to purchase an in-home IoT device in the next five years.
Only 3% of insurers are already making use of wearable devices, and less than a fourth are developing a strategy for them, even though 60% of insurance executives believe that wearable technologies will be adopted broadly by the industry.
Telematics in cars allow insurers to track driving patterns of their customers. The advent of cheap GPS devices has made this technology ready for widespread adoption with usage-based insurance (UBI) and dynamically adjusted premiums. More than 15% of the U.K. car insurance market is usage-based, and Progressive alone has more than 4 million UBI customers in the U.K. In the U.S., there are around 5 million UBI policies in effect, and approximately 70% of all auto insurance carriers in the U.S. are expected to use UBI by 2020, with more than 26% of all motor policies being usage-based. Usage-based programs on average lead to a 57% decrease in total claims cost.
Health insurance tech startups raised more than $1.2 billion in venture funding in 2015, more than twice as much as in 2014, and making up almost half of the $2.6 billion in venture funding that was raised by insurance tech startups overall. Insurers themselves have committed more than $1 billion to investments in startups, and many of them have established their own in-house venture capital funds to exploit IoT and ready themselves for new markets.
58% percent of smartphone users in the U.S. have downloaded a health-related app, and around 41% have more than five health-related apps, generating data that insurance providers could use to fine-tune their individual premium pricing and encourage low-risk customer behavior. The first insurance company to offer discounts to customers using technology aids for better living was John Hancock in 2015. Other companies in the U.S. and elsewhere have since followed suit, offering as much as a 15% premium discount.
The number of connected devices is projected to grow by 35% each year over the coming years. This creates a new wealth of data, which insurers see as important but do not know how to tackle. To understand how insurers can approach the issue, we must look at the health insurance industry, which is at the forefront of integrating wearable tech and makes up for about half of all insurance tech investment.
Most of the efforts to integrate technology by insurers are simple and mainly designed as promotions, like awarding credits for a number of steps taken: this is a far cry from what big data could do for adaptive premium pricing based on comprehensive health data for each customer. The problem is likely a skepticism toward new technology for which no historical experience is available.
The other major industry using connected devices is car insurance. Here discounts are given to customers who drive less and more safely than others, and the benefits so far have been clear: a 57% reduction in claims. It remains to be seen how much of this reduction will turn out be a temporary Hawthorne effect, but it is sizable enough to pique interest everywhere. The major problem is that so far insurers do not penalize worse-than-average drivers, and it is unclear to what extent customer will accept self-tracking as mandatory or de facto mandatory by pricing. The same issues will also have to be faced by other insurance industries moving to integrate IoT.
Insurers agree that the Internet of Things and wearables will play a major role for the industry but have so far only used them in often-gimmicky promotional efforts, hindered by the fact that they cannot penalize customers for risk-increasing behavior. The health insurance market is the main point of investment for insurance tech, but the rise of smart devices everywhere makes innovation possible in all parts of life. The first insurer to overcome the regulatory hurdles and offer truly adaptive and responsive insurance that is not limited to one or two factors but embraces big data will have a strong first-mover advantage.
The cyber insurance market grows each year both in size and import but is insufficiently understood and served by insurance providers, who so far have few technological options to contain, predict and address cyber risk.
Risk levels and market size
Estimates for the yearly cost of cybercrime vary from €330 billion to €506 billion. The cost will increase as businesses and their supply chains become more digitally integrated. In the past three years, the average economic impact of cybercrime per organization in the U.S. has risen from $11.6 million to $15.4 million. The biggest share of this impact comes from the cost of business disruption. The global market for cyber insurance is estimated to rise to $20 billion in premiums by 2025.
Customer awareness and adoption
Businesses are insufficiently insured and informed around cyber risk. Around 40% of Fortune 500 businesses currently have insurance against cyber incidents, but generally not enough to cover their full exposure. In the U.S., 24% of all business have some form of cyber insurance. 48% of enterprise customers say they lack the necessary understanding of the complexity of cyber risks to better prepare against them.
Available products and expertise
Of the 10 largest insurers, only five offer standalone cyber coverage. While 90% of all insurance underwriters offer cyber insurance as an add-on to other products, more than 50% do not have any dedicated underwriters for cyber risk and rely on underwriters for other lines. Consequently, 70% of insurance brokers claim there is little to no clarity about what is covered in cyber products.
Cyber insurance is a major challenge for insurers as there is little historical data to inform the correct insurance pricing, and there is great variation from year to year in the kind of cyber attacks and damages that businesses face most. Technological solutions to better protect against cyber threats or at least contain the risk are unsatisfying. As a consequence, the traditionally conservative, risk-averse and technologically skeptical insurers are failing to live up to their role as protectors of businesses against new, existentially threatening cyber risks.
While adapting rapidly, the strength of protection against cyber crime is unlikely to proportionally increase with the strength of the attacks, so defenses against cyber attacks are usually about one generation behind, with new types of attacks emerging each year. Businesses and their supply chain are digitally integrating to an ever larger extent, so both the target size and sophistication of cyber attacks will lead to rising risk and damage from cyber incidents, creating more exposure for businesses everywhere.
These businesses are by and large aware of this threat but find themselves insufficiently informed about how to protect themselves because insurers fail to provide the much-needed expertise. The damage to different developed countries’ GDP from cyber crime ranges from 0.5% to 1.5%. As this share increases, we can expect regulatory pressure, which already represents a big liability risk for cyber incidents, to lead to an even higher demand for comprehensive cyber insurance.
At the moment, insurers are still unsure about how to best underwrite cyber risk and often go the safe route of not offering dedicated cyber products at all, or only offering very limited products. As cyber insurance becomes more of a business necessity, insurers that cannot provide expertise on it will seem unreliable and unfit to support a business and see their market share suffer in other lines as well, and hence this area becomes an important space for further investment.
Cyber risk is a major, growing risk to insurance providers, including banks, and businesses looking for insurance, both because of liability exposure and the threat of business interruption that could run into substantial unplanned-for costs. Even though awareness is increasing among business leaders, insurers are struggling with offering the right products with relevant features and pricing because of their lack of experience. An insurer that knows how and is willing to underwrite this new type of risk will quickly capture a sizable market share. There is a level playing field for insurers and new players as there is no historical data available for both — agility and willingness to use new sources of data could be a competitive edge for new insurtech players.
You can find the full report from which this article is excerpted here.
This is Part 3 of a four-part series. Part 1 can be found here. Part 2 can be found here.
What if Leonardo Da Vinci had been alive to witness the digital revolution? Perhaps he would have been a sought-after consultant and speaker (after his start-up had gone public and his paintings were selling for millions)! Da Vinci was, according to historian Will Durant:
“The most fascinating figure of the Renaissance… [He] took fondly to mathematics, music and drawing. In order to draw well, he studied all things in nature with curiosity. Science and art, so remarkably united in his mind, had one origin — detailed observation.”
According to Da Vinci, a scientist should look at experience and observation before applying reason to any experiment. He uniquely had both a right brain and left brain perspective, the art and the science view, that looked at facts but then creatively used them to innovate — highlighting the power of observation. And Da Vinci’s observations are still with us today.
For insurers, the power of observation is no less important than it was during the Renaissance. In fact, observation’s power for change and growth, using nearly any measurement (e.g. dollars, longevity, capacity for change, lowered risk) would certainly far exceed its Renaissance power. Insurance’s pervasiveness and necessity (it underpins economies to enable them to grow) make it globally and individually life-altering.
If insurers wish to tap into the power of observation, in which direction should they look?
The simple answer is that they should look at trends. But to fully explore trends, it will help us to split them into subcategories, such as purchase trends, lifestyle trends, customer preferences and commercial/industrial trends.
Observing Purchase Trends
This is the most obvious of the trends, yet it may be one of the most overlooked trends. How do people buy? What differences are there between segments such as millennials, baby boomers and small business owners? This goes beyond, “Well, they seem to be using the internet and mobile phones.” Observing purchase trends takes everything into consideration — Where are people when they are using their mobile phone or other mobile device? Where are people when they realize they have the time, need and inclination to purchase insurance? Is there a cosmic moment when the right offer at the right time with the right channel yields a magical response?
This kind of observation can certainly be informed by trends and disruption within other industries. For a quick example, consider how iTunes created a profitable shortcut in the music purchase process (as well as dispensing with a physical product, all of its delivery methods and costs). Then think about how Spotify, Amazon Music, YouTube, Pandora and SoundCloud have all dented iTunes demand and caused its prices to look exorbitant. The lesson for insurers is twofold: 1. Capitalize on opportunities to be in the right place at the right time with market targets, and 2. Be vigilant in price response, service response and capitalizing on the next idea.
Now that insurance is changing, it won’t stop. Perpetual observation, along with incubation and concept testing, will provide a foundation of market safety — if the organization is committed to acting on what it learns. This means continuous incubation and market testing of innovative products and services, likely outside of the normal insurance operations and systems structure — being creative and acting like a start-up.
Observing Lifestyle Trends
Insurance is so tightly bound to lives and lifestyles that it is imperative that insurers keep tabs on how lifestyles are changing. For example, in 2014, single adults in the U.S. began to outnumber married adults. How does that affect insurers with products that may seem to reward families with discounts and lower rates (i.e for multiple vehicles)? The sharing economy is also becoming mainstream, not only with services like Uber and Lyft, but also with shared office spaces, shared living arrangements and shared vacation residences growing in popularity. The sharing economy is all about the sharing of assets rather than ownership of them. Is it time for insurers to start thinking less in terms of insuring property owned or mortality and instead begin thinking in terms of insuring life experiences that may occur over short spaces of time, rather than for years? The rider in the Uber and the vacationer in the Airbnb may feel far more comfortable if they have the insurance for that specific time and need — knowing that no matter where they are, and no matter what happens, they have access to insurance.
Once again, this requires direct observation and then using the observations to creatively rethink insurance. Demographic studies that account for the next three, five and 10 years can even help insurers predict lifestyle patterns before they become mainstream, capturing the opportunity early and gaining market share.
Observing Customer Preferences
Many newspapers are losing money or are fading away. Bookstores are closing. Large department stores are somewhat outmoded. Bricks and mortar retail outlets are struggling to stay relevant. Purchases of used goods have never been higher. Online purchases have never been higher. What does this tell us about consumer buying preferences? What does it mean to insurers?
The digital transformation of buying that is playing out is unprecedented. But does it mean agent sales aren’t the future or that un-tailored, high-volume products are no longer needed? The answer is no. In many cases, the answer is to increase an understanding of preferences at both a high level (market trending) and an individual level (preference trending). Preferences change frequently, so market analysis and segmentation underpinned by data and analytics play an important role in understanding where reality is at any one point in time. For observant insurers that care about growing their business, building an excellent customer experience and acting on a real knowledge of market trends and individual preferences will strengthen customer satisfaction and retention. It will also build loyalty among market segments that are changing or traditionally hard to keep.
What do Samsung clothes dryers, FitBits and connected cars have in common? All of them have IoT sensors, all of them have digital connectivity to mobile devices and … they are all relevant to insurers.
When skateboarders started using GoPros (and posting videos to YouTube) and iPhones started locking themselves in cases of theft, insurers should have started paying attention. Drone technology, camera technology, GPS tracking, step measurement — all of these advances will play a role in insurer offerings, capabilities and services. But technological advancements are only the beginning of commercial trends that insurers can use. As commerce changes and as processes and products adapt, informed insurers will be able to support the changing needs of organizations. Start-up businesses and small businesses will be looking for ways to insure venture capital and other investments against loss. Drone and unmanned aircraft insurance needs will grow. Data protection and cyber security insurance needs will continue to grow.
The insurance Renaissance will change the needs of companies and individuals as they embrace new market trends, technologies and as they reshape their preferences. This will likely mean a decrease in demand for some traditional products such as auto insurance or individual life insurance. But, at the same time, it opens the door for new products that embrace the changes. Just look at companies like John Hancock with its Vitality product, as well as insurers providing risk avoidance services using IoT in their homes or those offering shared transportation insurance. For observant insurers that grasp the way financial and business models are changing, there will be excellent opportunities to supply innovative products and risk preventive services. The key will be in the observation.
Insurance is the economic foundation for economies, businesses, families and individuals, enabling them to operate or live life fully and with confidence. Our responsibility as an industry is to continually observe the changes that are happening inside and outside of the industries we serve, adapt to those changes with innovative products and services that meet changing customer needs, and do it with speed, capturing the opportunities unfolding before our eyes.
In my next post on the insurance Renaissance, we’ll see how re-envisioning financial and business models may be one of the ways that insurers can prepare for a new era of progress and success.
For centuries, people have lived in a world where data was largely proprietary, creating asymmetry. Some had it. Others did not. Information was a currency. Some organizations held it, and profited from it. We are now entering an era of tremendous data balance — a period of data symmetry that will rewrite how companies differentiate themselves.
The factors that move the world toward data symmetry are time, markets, investment and disruption.
Consider maps and the data they contained. Not long ago, paper maps, travel books and documentaries offered the very best views of geographic locations. Today, Google allows us to cruise nearly any street in America and get a 360° view of homes, businesses and scenery. Electronic devices guide us along the roadways and calculate our ETA. A long-established map company such as Rand McNally now has to compete with GPS up-and-comers, selling “simple apps” with the same information. They all have access to the same data. When it comes to the symmetry of geographic data, the Earth is once again flat.
Data symmetry is rewriting business rules across industries and markets every day. Insurance is just one industry where it is on the rise. For insurers to overcome the new equality of data access, they will need to understand both how data is becoming symmetrical and how they can re-envision their uniqueness in the market.
It will be helpful to first understand how data is moving from asymmetrical to symmetrical.
Let’s use claims as an example. Until now, the insurer’s best claims data was found in its own stockpile of claims history and demographics. An insurer that was adept at managing this data and applied actuarial science would find itself in a better position to assess risk. Competitively, it could rise to the top of the pack by pricing appropriately and acquiring appropriately.
Today, all of that information is still very relevant. However, in the absence of that information, an insurer could also rely upon a flood of data streams coming from other sources. Risk assessment is no longer confined to historical data, nor is it confined to answers to questions and personal reports. Risk data can be found in areas as simple as cell phone location data — an example of digital exhaust.
Digital exhaust as a source of symmetry
Digital exhaust is the data trail that all of us leave on the digital landscape. Recently, the New York City Housing Authority wished to determine if the “named” renter was the one actually living in a rent-controlled apartment. A search of cell phone tower location records, cross-referenced to a renter’s information, was able to establish the validity of renter occupation. That is just one example of digital exhaust data being used as a verification tool.
Another example can be found in Google’s Waze app. Because I use Waze, Google now holds my complete driving history — a telematics treasure trove of locations, habits, schedules and preferences. The permissions language allows Waze to access my calendars and contacts. With all of this, in conjunction with other Google data sets, Google can create a fairly complete picture of me. This, too, is digital exhaust. As auto insurers are proving each day, cell phone data may be more informative to proper pricing than previous claims history. How long is it until auto insurers begin to look at location risk, such as too much time spent in a bar or frequent driving through high-crime ZIP codes? If ZIP codes matter for where a car is parked each night, why wouldn’t they matter for where it spends the day?
Data aggregators as a source of symmetry
In addition to digital exhaust, data aggregators and scoring are also flattening the market and bringing data symmetry to markets. Mortgage lenders are a good example from outside the industry. Most mortgage lenders pay far more attention to comprehensive credit scores than an individual’s performance within their own lending operation. The outside data matters more than the inside data, because the outside data gives a more complete picture of the risk, compiled from a greater number of sources.
Within insurance, we can find a dozen or more ways that data acquisition, consolidation and scoring is bringing data symmetry to the industry. Quest Diagnostics supplies scored medical histories and pharmaceutical data to life insurers — any of whom wish to pay for it. RMS, AIR Worldwide, EQECAT and others turn meteorological and geographical data into shared risk models for P&C insurers.
That kind of data transformation can happen in nearly any stream of data. Motor vehicle records are scored by several agencies. Health data streams could also be scored for life and health insurers. Combined scores could be automatically evaluated and placed into overall scores. Insurers could simply dial up or dial down their acceptance based on their risk tolerance and pricing. Data doesn’t seem to stay hidden. It has value. It wants to be collected, sold and used.
Consider all the data sources I will soon be able to tap into without asking any questions. (This assumes I have permissions, and barring changes in regulation.)
Real-time driving behavior.
Retail purchases and preferences.
Exercise or motion metrics.
Household or company (internal) data coming from connected devices.
Household or company (external) data coming from geographic databases.
These data doors, once opened, will be opened for all. They are opening on personal lines first, but they will open on commercial lines, as well.
Now that we have established that data symmetry is real, and we see how it will place pressure upon insurers, it makes sense to look at how insurers will use data and other devices to differentiate themselves. In Part 2 of this blog, we’ll look at how this shift in data symmetry is forcing insurers to ask new questions. Are there ways they can expand their use of current data? Are there additional data streams that may be untapped? What does the organization have or do that is unique? The goal is for insurers to innovate around areas of differentiation. This will help them rise above the symmetry, embracing data’s availability to re-envision their uniqueness.