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Cyber Insurance Needs Automated Security

Hackers, malware, viruses, ransomware and phishing emails are becoming a normal part of increased connectivity, and their impact on everyday life is growing. The result is a profound increase in the demand for cyberinsurance. The downside? Cyberinsurance is hard to price as risk potential is not well understood, and losses can enter into the millions of dollars. Moreover, businesses with cyberinsurance may be lulled into complacency by their coverage. They shouldn’t be. Just reimbursing the costs of damage after a cyberattack isn’t smart business—smart businesses seek to prevent the cyberattack from occurring.

Enterprises do this at great expense, with costly, complex tools and teams beyond the reach of small and medium-sized enterprises (SME). SMEs need automated cybersecurity for cost-effective, full protection. That’s because cyberninsurance is insufficient to protect a business: It isn’t a substitute for good business practices that work in concert with cybersecurity. In short, cyber insurance and cybersecurity must complement each other to provide what businesses really want: peace of mind at predictable costs.

Cyber Safety Is as Essential as Fire Safety

Think of it like this: You wouldn’t protect a business from a fire simply by buying a fire insurance policy. Best practice fire safety includes smoke alarms, fire extinguishers, fire-retardant building materials, a designated gathering spot and regular fire drills. On the other side of the coin, governments have adopted fire safety building codes, and insurers don’t sell fire insurance without verifying fire safety compliance: Fire extinguishers, smoke detectors and sprinklers must be installed and properly maintained.

See also: Cybersecurity Holes in Connected Cars  

Similar businesses practices are necessary for cyber protection. But the technology has not caught up with business needs. Many cyber insurance policies are written without accurately measuring the risks that make a business vulnerable to a cyber attack. A one-time snapshot of the number and type of data records, or even a more full-fledged review of internal and external systems, is inadequate to assess risk. Technology evolves too quickly for these snapshots or scores to be valid over time. The moment a system needs upgrading, data may be at risk. The moment a new virus begins to spread, businesses are vulnerable. As long as a patch is not applied, systems and data are exposed. These big changes to risk affect the underwriting assumptions. It’s a shifting landscape, one that requires that businesses remain constantly vigilant. Automated cybersecurity technology is more effective than people at monitoring and addressing threats. In short, cyber insurance without automated cybersecurity is like fire insurance without smoke detectors.

Cyber Risk Models Need Much More Data

Automated cybersecurity platforms that detect and protect against cyber attacks are also useful to measure risk over time. Telematics let auto insurers such as Progressive and Metromile more accurately measure risk—and price accordingly. We need new “cyber-telematics” that allow underwriters to more accurately measure cyber risk. They provide risk insights about the insured, enabling the development of rich aggregate risk models. Cyber-telematics also helps underwriters develop risk models from the measurements correlated with cyber risk—and see the red herrings that aren’t. Cyber-telematics answers industry concerns noted in a March 2017 Property Casualty 360 article that “the insurance industry faces a rampant reporting bias that is hard to translate into policies.”

Without a thorough understanding of the profound risk being underwritten, losses are unpredictable—and potentially catastrophic. Insurers have long understood the impact of underestimating exposure aggregation with respect to natural disasters and other correlated losses like terrorism or asbestos claims. Of these, Towers Watson wrote, “The difference is that the terrorist attack is a single event and not a decades-long process, and the losses will be recognized and paid much more quickly.” The same, or worse, should be expected of large-scale single cyber events.

Technology is essential to collecting the data for, then understanding, mitigating and accurately modeling cyber risk.

Large enterprises have massive budgets, and most create a custom cybersecurity system using expensive experts and tools from multiple vendors. This has made it much harder to penetrate their defenses. As a result, hackers have moved down the food chain, making small and medium-sized businesses especially vulnerable. These businesses face the potential of a business-ending event in the face of a cyber attack.

Automation is the right answer when people and systems aren’t available or affordable. SMEs need automated cybersecurity to reduce risk and reduce cost. Current solutions are simply too expensive in terms of staffing and too complex in terms of tool integration. With automated cybersecurity, SMEs receive the benefit of robust machine learning coupled with economies of scale that take advantage of the cost efficiencies introduced by automation. For insurers, automation enables data gathering that informs robust risk management models, providing key insights to identify and mitigate loss potential.

See also: How to Eliminate Cybersecurity Clutter  

According to Hiscox data, 60% of smaller companies in the U.S. reported one attack or more in the last 12 months—and 72% of larger companies. In the U.S., the average estimated cost of an organization’s largest cyber incident was $35,967 for 99 or fewer employees and $102,314 for 1,000 or more employees. However, a November 2017 Property Casualty 360 article reports that “in the aftermath of an incident, SMBs spent an average of $879,582 due to damage or theft of IT assets; additionally, disruption to normal operations cost an average of $955,429.” This wide variance in the reported cost of cyber incidents reflects uncertainty among insurers.

The Hiscox report further observes, “While big firms incur the highest costs in nominal terms, the financial impact of cyberattacks is disproportionately high for the very smallest companies.” Because these “smallest companies” can least afford effective cybersecurity, they need automated solutions. Let the machines do the work.

Peace of Mind

Cyberinsurance complemented by automated cybersecurity is key to modern business—neither is sufficient on its own. SMEs are better protected with the complement of these tools. A simple metaphor is the modern automobile. Today’s cars don’t simply provide airbags to react to accidents, they include technologies to avoid accidents: anti-lock braking systems (ABS), blind spot monitoring, lane departure warnings and more. Modern cybersecurity and cyber insurance are similar complements: Airbags cushion the blow, much as a rapid response can limit the losses from a cyberattack, and automated cybersecurity monitors networks and protects SMEs, much as accident prevention systems protect drivers.

Modern technology demands the next evolution of cyber insurance and cybersecurity measures, similar to the evolution of fire insurance and car safety technology. Effective, automated cybersecurity technologies, coupled with comprehensive cyber insurance, are needed for real peace of mind against cyber attacks.​

Health Startups Go After 3 Pain Points

In my last post, I outlined the four dimensions that are defining the opportunities for health insurtech innovation: the health of the American people, marketplace trends, the role of regulation and the players.

Incumbent health insurers are pursuing legacy tactics to compete in the ACA world. There are big M&A approved (Centene/Healthnet) and facing regulatory challenges (both Aetna/Humana and Anthem/Cigna). Many are increasing premiums. Others are leaving the public exchanges (notably, United Healthcare withdrew earlier this year, and Aetna just announced its withdrawal from 11 of the 15 exchanges).

Innovators addressing the root of user pain points can influence how plans are selected and healthcare is consumed. The levers are not easy to move. Success requires compliant ways of combining big data analytics and personalization with user-centric digital experiences.

The headline of a recently published New York Times article, Cost, Not Choice, Is Top Concern of Health Insurance Customers, would seem to state the obvious. Yet insurers have expressed surprise at the policy mix and which plans are proving to be most popular. Carriers participating in the public exchanges report poorer actual performance than anticipated in premiums (lower) and claims (higher). Users are gravitating toward lower-cost plan options and show a trend to self-select into higher-cost plans when they know a big health care expense looms.

This is not just an issue for incumbents. Oscar, among the most visible innovators in the US health insurance marketplace, reported a $105 million loss in 2015. Lack of scale is a challenge, but the company has also been affected by the user decision-making dynamics affecting established carriers.

See also: Matching Game for InsurTech, Insurers  

The results suggest (at least) three pain points:

1. People don’t see value because they don’t understand what they are buying.

  • When people think something is too expensive, it is because either they cannot afford it (i.e., it really is too expensive) or the perception of value does not justify the price.
  • Reportedly one in seven employees do not understand the benefits being offered by employers, of which health insurance is by far the biggest piece.

2. People are being held accountable for health decisions that they are not equipped to handle.

  • Faced with a complex set of choices and opaque information, it is no surprise that many go for the easy option: saving money now.

3. People don’t always make rational decisions.

  • A basic primer in behavioral economics will tell you that emotion, bias and other limitations — not rational analysis — drive decisions and that people discount perceived upside relative to downside. There is not enough upside to pay more in the short term.

Players who manage to affect these behavioral drivers stand to gain. Here are examples of companies working the issues.

Connecting disparate sources of data

PokitDok creates “APIs that power every health care transaction.” They aim to enable data connectivity across the silos that in today’s world require manual navigation. They define an ecosystem including Private Label Marketplaces, Insurance Connectivity, Payment Optimization and Identity Management. The company closed a $35 million B round last year. PokitDok is a pure technology play. Achieving their vision could be the “holy grail”: better economics and better patient experiences and outcomes without owning underwriting risk.

Helping employers

It hasn’t been lost on the startup world that 150 million employees purchase health care via employers, which is why many companies are focused on improving the benefits buying experience and promising to help employers lower costs. The ACA requires that all companies with more than 50 employees offer health insurance. This aspect of the regulation, coupled with the fact that health benefits expense has risen steadily, provides a specific and large innovation space.

Competitors include:

Lumity, who reported raising $14 million last fall, acting as an insurance broker. The company claims to be “the world’s first data-driven benefits platform for growing businesses” promising to simplify benefits selection for employers and employees. Employees are asked to provide health data, which are compared with aggregate profiles using proprietary algorithms. The big question: Will employees see enough benefit to share potentially sensitive information?

Zenefits, recovering from widely publicized regulatory issues, has new leadership. The company acts a broker, and focuses on small businesses.

Collective Health is targeting a wide range of businesses via “ready-to-go,” “configurable,” and “advanced” solutions. The employee experience components of the offering are aimed at helping users make better-informed decisions with less hassle.

SimplyInsured aggregates health insurance plan options for small businesses to make comparisons easier, and aims to automate processes presumably essential to creating a viable cost structure for serving this segment.

See also: InsurTech Need Not Be a Zero-Sum Game  

A number of benefits consultants including Aon and Towers Watson (the latter via their acquisition of Liazon in 2013) offer larger employers private exchange capabilities – these include portals for employee benefits enrollment enabled by data analytics and a friendly user interface. They act as or engage brokers to create benefits plans tailored to employers’ goals. Such portals can be helpful to employees, and check a box for employers seeking to improve the benefits experience, not just reduce expenses.

Health Advocate, founded in 2002, is the largest example of a relatively new industry positioned to help patients navigate an increasingly complex system towards the right care and reimbursement. The question being raised around these solutions – although as the de facto advocate within my own family I’d love to have a professional advocate to whom I could outsource – is whether they are a workaround adding yet another layer of expense to an industry that earns among the worst customer satisfaction scores of any. As an employer, however, it’s a benefits option that could be valuable given the stress of managing the health care process many employees undergo.

Motivating people to adopt healthier habits

Vitality, reported on in an earlier post, is a cobranded platform offering deals and rewards designed to motivate people who take steps towards better health. Hancock offers the HumanaVitality program, integrating Vitality’s rewards program into the insurance relationship. If people see near-term benefit to behavior change this could be a good use case upon which to build.

Facilitating patient payments to providers

Patientco is a “payments hub” supporting “every payment type,” “every payment method,” “every payment location.” Focus is on efficiently increasing revenue for providers, secondarily to improve the payments experience for patients. The company provides the ability to integrate its solution with other health technology solutions.

Providing better experience capabilities to carriers

Zipari is a customer experience and CRM platform providing a product suite including enrollment, billing, and a 360-view of members across engagement channels. The company targets is product line at insurers, both direct-to-consumer and group or employer channels.

See also: Be Afraid of These 4 Startups

The multiple miracles that would have to occur for a quick fix make it unlikely that we will see a simple, logical health insurance experience any time soon. We are relatively early in what is likely to be a long game. But, insurtech innovators and even more mature companies operating within and around the sector are demonstrating the capacity to go after the possibilities that data, technology and the ability to see creative solutions offer to mitigate the pain.

Survey: Predictive Modeling Lifts Profits

The breadth and depth of predictive modeling applications have grown, but, of equal importance, the percentage of participants reporting a positive impact on profitability has dramatically increased, Towers Watson’s most recent predictive modeling survey finds.

Our 2014 Predictive Modeling Benchmarking Survey indicates the use of predictive modeling in risk selection and rating has increased significantly for all lines of business over the last year, continuing a long-term trend. For instance, in the personal auto business, 97% of participants said that in 2014 they used predictive modeling in underwriting/risk selection or rating/pricing, compared with 80% in 2013, a 17-percentage-point increase. For standard commercial property/commercial multiperil (CMP)/business-owner peril (BOP), the number jumped 19 percentage points, to 51%, during the same time period (Figure 1). In fact, the percentage of participants that currently use predictive modeling increased for every line of business covered in the survey.

Figure 1. The use of predictive modeling in risk selection/rating has increased significantly for all lines of business over the last year

Does your company group currently use or plan to use predictive modeling in underwriting/risk selection or rating/pricing for the following lines of business?

Sophisticated risk selection and rating techniques are particularly important in personal lines, where models have now penetrated most of the market. An overwhelming 92% of survey participants cited these techniques as essential drivers of performance or success. To a significant degree, this was also true for small to mid-sized commercial carriers, with 44% citing sophisticated risk selection and rating techniques as essential and another 42% identifying them as very important.

Even as the use of predictive modeling extends to more lines of business, there is an increasing depth in its use. Predictive modeling applications are increasingly being deployed by insurance companies more broadly across their organizations as their confidence in modeling increases. For example, 57% of survey participants currently use predictive modeling techniques for underwriting and risk selection, and another 33% have plans to use them over the next two years. Although a more modest 28% currently use predictive modeling to evaluate fraud potential, a sizable additional 36% anticipate using it for this purpose over the next two years. Survey participants report plans to deploy predictive modeling applications in areas including claim triage, evaluation of litigation potential, target marketing and agency management. These applications will favorably affect loss costs, expenses and premium growth.


Eighty-seven percent of our survey participants report that predictive modeling improved profitability last year, an increase of eight percentage points over 2013 (Figure 2). The increase continues a pattern of growth over several years.

Figure 2. Companies implementing predictive models have increasingly seen favorable profitability impacts over time

What impact has predictive modeling had in the following areas?

Slide 9 of Executive Summary

A positive impact on rate accuracy helps explain the improvement. In fact, the percentage of carriers citing a positive impact on rate accuracy has increased every year since 2010, when 70% cited a positive impact. In three of the past four years, the percentage-point increase in carriers citing a positive impact has hovered around 10%. In this year’s survey, nearly all (98%) of the respondents reported that predictive modeling has improved their rate accuracy. Improved rate accuracy has both top- and bottom-line benefits: It boosts revenue because it enables insurers to price more effectively in very competitive markets, retaining existing customers and attracting potential customers with rates that accurately reflect their level of risk. At the same time, rate accuracy drives profit because it also helps carriers identify and write more profitable business,and not focus solely on market share and price.

More accurate rates also improve loss ratios, which have improved in parallel, according to our survey participants. In 2014, 91% of survey participants cited the favorable impact of predictive modeling on loss ratios, an increase of 14 percentage points over 2013. When premiums more accurately reflect risk, losses are more likely to be properly funded.


The bottom-line fundamentals — profitability, rate accuracy and loss ratio improvement — identified in our survey are complemented by top-line benefits. Positive impacts were registered on renewal retention (55%), underwriting appetite (46%) and market share (41%).


Sophisticated risk selection and rating are cited as essential by many of our participants, but our survey indicates that, despite favorable trends, insurers are still far from leveraging sophisticated modeling techniques to their fullest, even in pricing. Two-thirds of participants aren’t currently using price integration (the overlay of customer behavior and loss cost models to create metrics that measure different rate scenarios) for any products. A few are past price integration and are currently implementing price optimization (harnessing a mathematical search algorithm to a price integration framework to maximize profit, volume and other business metrics) for some products.

The disparity between what is viewed as the optimal use of modeling techniques and the current level of implementation needs to be bridged if insurers want to leverage predictive modeling as a competitive advantage to identify and capture profitable business. Increasingly, insurers are making greater use of analytics including by peril rating (which replaces rating at the broad, line-of-business level with specific rating by coverage), proprietary symbol (customizing vehicle classifications for personal automobile policies) and territorial and credit analysis.

Those insurance companies that can’t employ sophisticated risk identification and management tools face the possibility of losing profitable business and adverse selection.


Profitability is hard-earned in the current competitive property/casualty market, and predictive modeling is recognized by a steadily growing number of companies as an invaluable tool to improve both top- and bottom-line performance that ultimately reflects in earnings growth. Our survey suggests that insurers are increasingly comfortable with predictive modeling and are using it in a growing number of capacities. However, participant responses also indicate that there are still many benefits offered by predictive modeling and other more sophisticated analytical tools that have not been achieved, such as treating data as an asset and more effectively using predictive modeling applications to improve claim and other functional results. Improving performance on these issues alone could make a significant difference in the profitability of insurance companies and offers all the more reason to explore new ways to benefit from data-driven analytics and predictive modeling.


Towers Watson conducted a web-based survey of U.S. and Canadian property/casualty insurance executives from Sept. 3 through Oct. 22, 2014. The results discussed in this article represent the views of 52 U.S. insurance executives. Responding companies represent a significant share of the U.S. property/casualty insurance market for both personal lines carriers (17%) and commercial lines carriers (22%).

A Wakeup Call for Benefits Brokers

More news from the technology front: Aetna acquires bswift. , shortly after Hodges-Mace announced the purchase of SmartBen. Last year, it was Towers Watson buying Liazon. Next year, it will be someone else. Is this just beginning of the dance where everyone in employee benefits needs to choose a partner? What does this mean for the benefits market and the benefits broker?

For some, the Aetna acquisition of bswfit may be strange. Aetna buys a company that provides technology that is used by its competitors and that handles enrollment for many employers that don’t have Aetna insurance. Similarly, Towers Watson bought a company whose products and services are distributed by its competitors, other brokers.

What most people aren’t realizing is that the world has changed. If you view this acquisition in the old world, where competitors don’t work together, you may see it one way, but in a new world it may look a little different — in many industries, companies that compete in one segment may be partners in another.

My message to brokers on this is to start thinking differently. Those who don’t will get left behind. The rules of the game are changing, and you don’t get to make all the rules.

I have been fortunate to have worked in some capacity with Mark Bertolini, CEO of Aetna, and Rich Gallun, CEO of bswift. Both are outside-the-box thinkers. Aetna has invested billions in technology preparing for what it views as a consumer-centric healthcare model. Aetna wants to reinvent the patient experience. To quote Bertolini, “We’re going to begin to change the healthcare industry by giving people tools they can put in the palm of their hand.”

Here is another quote from Bertolini that would make brokers pause. When asked about the future of healthcare, Bertolini responded: “There wouldn’t be plan designs. You wouldn’t need them. What you would do is invest in all those things that are necessary to keep people healthy.” You can see a full overview of the Aetna model by viewing this presentation from its 2013 investor conference.

Some may see the bswift acquisition as a benefits enrollment platform for Aetna. But I see this as another step by Aetna to execute on a plan to compete effectively in a new healthcare world. A world where consumers are in more control. Where provider systems are engaged in a patient’s wellness and not just proving treatment after the fact. Where health information and communication is moved via Web and mobile.

Bswift made a strategic move into the consumer-centric world through private exchange technology, with individual rating and decision support tools. Now it has paid off. This made bswift attractive to Aetna. Congratulations to bswift for a job well done.

So what does this mean for benefits brokers?

A few weeks ago, I wrote an article titled “Does Apple’s HealthKit signal the end of employer-based insurance?” Some may not relate Apple’s investment to the Aetna acquisition of bswift; however, I think they are related. Apple is clearly one of the top consumer technology vendors in the market. Aetna is driving consumer-centric healthcare. They are pieces of the same puzzle. It is a puzzle benefits brokers need to pay attention to because the market is changing around them. A carrier buying an enrollment vendor says one thing, Aetna’s and Apple’s investments mean something different.

The healthcare world is changing in a way that most brokers are not recognizing. Consumer-centric; mobile; doctors as wellness facilitators; employers out of the risk business? Maybe. So get ready.

UBI Market Doubles, Reaches a Milestone

A recent report from Towers Watson shows that the world is making steady progress toward usage-based insurance (UBI). That steady growth is poised to become explosive if insurers can move faster and deal with privacy concerns while delivering UBI via smartphone apps that consume little of the battery’s charge.

The report says market penetration has nearly doubled in less than a year and a half — reaching 8.5% of U.S. drivers in July, up from 4.5% in February 2013. UBI has reached a milestone, with all 50 states now having programs available.

Consumers want the discounts that they can get by having their usage quantified and verified. Consumers are more willing than ever to work with carriers that offer UBI programs — meaning they will leave carriers that don’t.

An effective UBI program may prove to be a once-in-a-career opportunity for auto-insurance executives to resegment the market and claim a bigger share. The last time there was a change even approaching this magnitude was in the 1990s, when insurers discovered the importance of credit ratings in assessing how risky a driver is.

It’s time that we stopped measuring with a 12,000-mile-long tape measure — that being the distance that old-school insurers assume someone drives each year — and started measuring with a ruler. The mileage bands used to determine risk need to become so small that a single tank of gas could put a consumer into a new one — and the consumer needs to know that in advance so she can make a fully informed decision about how much to drive.