Tag Archives: FitSense

An Eruption in Disruptive InsurTech?

I attended an InsurTech “boot camp” at the magnificent Christ Church, Spitalfields, U.K., my first such event, and I was intrigued to see what would be presented and how the audience would react.

The organizers billed the day’s theme as “Experience the Eruption.” Their website stated the aim was to “recognize the fast-paced appearance of insurance start-ups, which are creating seismic shifts behind the scenes that will lead to the emergence of a new identity within the insurance sector as we know it today. An ‘eruption,’ which will allow new disruptive entrants to break out into the mainstream and support an industry that needs to engage differently in a highly customer-centric and digital-friendly world.”

Was that lofty ambition that labors excessively on hyperbole, or did the afternoon live up to the hype?

The format of the afternoon was a series of Dragons’ Den (a U.K. TV show) style pitches (without the interrogation) for investment or partnerships from the incubated firms selected for Startupbootcamp’s program (which includes investment from them, meaning there is an element of self-interest that firms do well). The pitches were preceded by a fireside chat from the chief strategy officer of Knip, a Swiss-based app that acts as a portal and broker for insurance policies.

See Also: InsurTech Forces Industry to Rethink

Were any truly disruptive? My view is that they all fell into one of three broad camps of focus:

Distribution and Sales

I’d put four firms in this bracket: MassUp, Spixii, Buzzmove and MyFutureNow — but all had a different focus with different levels of potential disruption.

MassUp was all about making buying insurance for “stuff’ easier by making it an add-on for any purchase. The company had a good story and was slick, but it didn’t feel truly disruptive. Credit card companies have been offering similar protection for years, and MassUp will do well to distinguish itself from extended warranty products that savvier consumers tend to decline. That, perhaps, is the problem with the business model for me — while tech may make it easy for the consumer to purchase the insurance (and for sales companies to add it as an option), it doesn’t obviously increase the value for the customer.

BuzzMove is a successful online removals broker, a portal to help customers find a removal firm when they move houses. The company has added to its capability by recognizing that a key element of quoting for removals is an inventory of the things that need to be moved. Typically, individuals don’t do this when they take out contents insurance (or, indeed, don’t update it when they buy new things), so they run the risk of being under-insured. Linking the life event with an inventory that can be used to underpin an insurance quote is a smart way to add value to the customer — without additional effort. As such, it is effectively looking to take over the customer by owning the life event in the same way banks have looked to do — e.g. take out a mortgage, and they will try to convince you to re-visit your life insurance levels. As such, the concept is not disruptive, but the concept of the home inventory and the tech underlying how this is put together is something insurers (and others) will undoubtedly embrace, so it is therefore significant. I’ll return to this later, as the ownership of this data becomes key.

MyFutureNow has a reasonably simple proposition; it is an online portal for customers to manage disparate pension plans by consolidating them into a single plan that is offered through the site. On the surface, its proposition is attractive and is reinforced by a slick implementation of the website and the app — the economics are being driven by a percentage fee on the value of the pension fund when transferred in. The key to success will be to differentiate the consumer experience. However, as regulated financial advisers will tell you, this is a complex area, and the consolidation of old plans is not necessarily the appropriate outcome for all consumers. It is unclear to me the extent to which MyFutureNow has have thought through the compliance and advice issues. Again, the focus is to try and take over ownership of a particular part of a customer’s portfolio (in this case, pensions).

Spixii’s proposition was timely, what with Facebook’s recent announcement of the addition of “bots” to its Messenger app. Essentially, Spixii offers a message bot that sells insurance (currently just travel insurance, but the concept could obviously be extended quite easily.) Inevitably, all financial service providers will add bots as way of communicating and selling, as will the price comparison websites, so this is definitely an “on-trend” area to watch.

Customer Experience

Three firms fall into this category: RightIndem, Domotz and Quantifyle.

RightIndem looks to enhance claims management by allowing insurers to offer a self-service claims platform and by increasing the transparency of the claims process. Claims is an area consumers point to as frustrating, so any steps to enhance the offering will be hugely positive; it is an area we will see all insurers developing in the coming years.

Domotz is a little more difficult to classify as it is not strictly an insurance proposition. The company plays in the space of the Internet of Things and the smart home. The insurance angle is providing information to the customer that will help reduce claims through smart home management (e.g. the customer gets an alert if running water is detected and nobody is home). Insurers might therefore offer discounts to those who install such systems. As such, it is perhaps similar to the wave some years ago when insurers encouraged drivers to fit alarms and immobilizers in their cars before they were standard issue.

Quantifyle’s proposition is based on driving good customer behavior for wellness by motivating people to achieve fitness goals. Insurers have already played in this area — most noticeably Vitality, whose entire proposition is built around rewarding customers for their lifestyle.

Big Data

The last firm presenting is alone in this category, although others touched upon it.

Fitsense­ demonstrated how it can harness the data collected from wearable tech (such as fitness trackers and smartphones) and overlay that with environmental information to provide the insurer insight into a customer’s lifestyle and behavior. Undoubtedly, there is great insight to be had, but the key element here will be the willingness of consumers to adopt and provide that information to insurers. (Location-aware information was also touched on by Spixii, which speculated that its app could provide, for example, travel insurance options that depend on the travel profile of the individual.)

This leads us into the important area of privacy and ownership of that information, with consumers rightly being concerned about the erosion of their privacy. While the youngest generation of consumers are likely to be increasingly less concerned, the adoption will need to happen slowly to bring customers along. There is also the risk of consumer self-selection (similar to the current adoption of “driving standards” apps by motor insurers), and it raises the moral question of whether increasingly individualized risk pricing is at odds with the original insurance principle of pooling of risks.

So, What Was Missing?

Invariably, InsurTech “innovation” majors on the three areas highlighted above — they are usually the easiest to move elements of the insurance process forward into the digital world but, therefore, are not necessarily disruptive, instead shifting the margin of current offerings. Two areas of development were conspicuous by their absence:

Peer-to-Peer insurance

This is an area where there are a few start-ups dabbling, but they haven’t yet reached any critical mass. Key inhibitors are traditional barriers to entry to the world of insurance, namely regulation and, in particular, capital requirements. It is a fast-moving area and one where, potentially, blockchain technology will grow out of its hype to provide a compelling proposition that satisfies regulators. In particular, recent work suggests that using the Lloyd’s of London model as template and porting to a blockchain model could provide the tipping point.

Consumer-Owned Risk Assessment

While big data has been touted as a way for insurers to get rich, detail on their customers and individualized risk assessment (which, in and of itself is simply a further iteration of the traditional model with more data) leads to issues of privacy and the moral question of individual versus pooling of risk. There is a paradigm shift in the interaction of consumers with institutions in the digital age that isn’t reflected here — that in which the consumer has more power and takes ownership of his or her own data. As such, this could break the mold of the traditional insurance product silos and be truly disruptive. In the new age, the dynamic is reversed, and the richness of data and the assessment of risks an individual faces do not belong to the institution — instead, control is with the individual, who, in turn, get the insight that allows them the power to manage a risk profile.

See Also: A Mental Framework for InsurTech

This shift has started in wealth management, and it seems natural that insurance will follow. New players in this sector will not be the traditional insurer, as the focus will need to be on providing the value to the consumer with the ownership of the data and allowing the consumer to manage it. This sits more easily with the business model of companies such as Google or Facebook than with the incumbents in the insurance market.

Conclusion? 

Nothing I saw in these presentations made me believe this group of companies would be genuinely disruptive (or, indeed made me reach for the checkbook to invest). When compared with the broader FinTech spectrum or tech-centric events, the afternoon felt less slick and less innovative. InsurTech is still young, so there is still a lot of maturing to do, but there were one or two hints from these companies that may stimulate discussion, which, in turn, might lead to genuine innovation.

8 Start-ups Aiming to Revive Life Insurance

In my last post, I described the state of the life insurance industry, including the pain points where InsurTech entrants are poised for impact.

The life insurance industry is suffering from a dying (literally) distribution model, complex products and a flawed purchase funnel.

New entrants can transform the industry by bringing a clean-sheet approach to:

  • Putting the client at the center of the business
  • Prioritizing the direct-to-client experience, including simpler products and path-to-purchase
  • Launching businesses on a back-end that enables low-cost, fast issuance and personalized underwriting and offers
  • Creating business models that align carrier and client interests and flex beyond protection-after-the-fact to providing value through prevention services
  • Supporting multi-channel servicing and claims management that satisfy clients
  • Using data responsibly to be proactive, personalized, timely, cost-effective and relevant
  • Treating life insurance as part of the client’s broader financial plan, including the connection to anticipating one’s healthcare requirements and managing the drivers, to the extent these are controllable, of health problems
  • Aligning with the demographic trends (the boomer handoff to the millennial generation and the emergence of the new majority in the U.S.) and the technology trends (mobile as the main screen; the role of social media in the client experience; and the application of big data to change the experience and business model)
  • Disproving orthodoxies that have become barriers to innovation for the sector, i.e., “insurance is sold not bought,” “the agent is the customer,” et al.

As much as start-ups are emerging and being funded aiming at health, home and auto, much less attention is being paid to either life insurance or its sibling, long-term care.

One founder/CEO with whom I spoke this week had two possible explanations: (1) Life insurance is the stepchild of the sector, and (2) the “sold not bought” orthodoxy is embedded, even among start-ups, which are typically seen as better not only at casting aside such self-imposed obstacles but seizing upon them as open doors for disruption. These factors may be deflecting entrepreneurial energy and attention in other directions.

See Also: InsurTech Can Help Fix Drop in Life Insurance

Long-term care has been a challenging product for traditional carriers, with players either abandoning the product or re-pricing and reconfiguring their products as flaws in earlier underwriting have become clear. According to Consumer Reports, between 2007 and 2012, 10 of the 20 top long-term-care providers stopped selling the product, and those in the business began raising rates, some reportedly as much as 90%, to address high claims projections.

That said, there are new ventures worth watching, and the good news about the relatively low level of attention being paid to life insurance, for those who see ignored space as white space, is that there could be more opportunity to succeed for those who engage.

Here are a few start-ups focused on the valuable white spaces:

In stealth mode are three companies worth keeping an eye on:

  • Sureify Labs is focused on “bridging the gap between insurers and their current and future policyholders” through a B2B offering aimed at helping traditional carriers move into the new world. The company’s site states that the platform “starts with consumer web and mobile applications that drive engagement through device-integrated wellness, savings and rewards programs tied to a policy. Behind the scenes, we give you as the carrier all the tools necessary to engage, communicate and up-sell your policyholders through digital mediums.” This sounds as though it would be a dream come true for carriers that are serious about building client-centric businesses.
  • Ladder, formed just a year ago (see: CB Insights report) is reportedly starting with a mobile value proposition built around easier and faster access to term life insurance, using available, permissible data sources to improve the underwriting process. If, as the name suggests, the company is building a value proposition that redefines the traditional notion of an insurance ladder – a construct that lets you plan for extra coverage when you’ll need it the most and taper off coverage at other times – I would expect them to develop more dynamic, effective relationships with clients than those propagated by the traditional one-and-almost-always-done insurance sales model.
  • Human Condition Safety (HCS’ site is under construction) is an example of a start-up focused on expanding the value a life insurance carrier can provide by offering prevention services in addition to protection. AIG became a strategic investor in the company earlier this year. HCS is said to be “developing wearable devices, analytics and systems to improve worker safety.”

A number of start-ups are building capabilities to solve carrier problems improving on the traditional distribution and product models. An investor might ask if these are businesses or features:

  • Force Diagnostics is focused on “combining science and a customer-centric streamlined process” to transform health and wellness screening. The expense (to the carrier), hassle (to the applicant) and elapsed time (a burden to all) associated with today’s underwriting requirements for blood and urine samples are ripe for reinvention.
  • Insurance Social Media, part of Serious Social Media, is offering a “set it and forget it” capability to improve agent effectiveness on social media. Given the demographic profile of the average agent (57 years old, and accustomed to pushing product), kick-starting their social media presence and providing relevant content solve pain points for today’s distributors. Of course, two questions regarding any start-up aiming to mass-produce content are: first, can such content come across as authentic, and second, how does this model scale?
  • Insquik offers agents a white label solution to create their own online stores. The focus is on term life automatic issuance up to $350,000 face value, and, according to the company’s site, aims specifically to serve the sub-segment of agents who “have access to large populations of consumers i.e., focused on Worksite Employee Benefits, Affinity Groups, Unions, Groups and Associations.”
  • Fitsense is a start-up coming out of StartupBootcamp that is building a data analytics platform focused on enabling insurance companies to reduce premiums “for anyone with a smartphone or wearable device.”
  • Sure provides a digital front-end and a more real-time experience for an old idea – a micro-duration life insurance policy that provides coverage during air travel. (In the pre-digital era, this was simply called “per trip coverage”.) American Express is one company that for more than 30 years offered air flight life insurance policies at varying face amounts, as part of a portfolio of travel-related protection benefits.

The opportunity for Insurtech to expand efforts in the life insurance category is not simply the commercial potential of disrupting a model that has proven its limitations. It is also the prospect of addressing a societal need that has been neglected for decades. These are two compelling reasons to encourage more participation by investors and entrepreneurs, stimulating a bigger pipeline of entrants to take on the reinvention of the category.

fortune telling

Fortune Telling for Insurance Industry

In the world of InsurTech, there are distribution players and there are data players. The data players are essentially doing two things:

First, they are enabling and exploiting new sources of data, such as telematics, wearables and social listening.

Second, they are processing data in completely new ways by applying data science, machine learning, artificial intelligence and high-performance computing.

The result is that, for insurers, the InsurTechs are creating opportunities for the development of new products for new customers; improved underwriting and risk management; and radically enhanced customer engagement through the claims process.

Which is why, in my humble opinion, tech-driven innovation in insurance will be data-driven.

As a result, this week I feature an Israeli start-up called Atidot, a cloud-based predictive analytics platform for actuarial and risk management…aka, the next gen of data modeling and risk assessment!

I’ve recently Skyped with CEO Dror Katzav and his co-founder Barak Bercovitz. Both have a background in the Israeli military, where they were in the technological init of the intelligence corps. Both have a background in cyber security, data science and software development.

These are two very smart cookies!

And they have applied their minds to the world of insurance and, very specifically, to data. To change the way that data is cut and diced to provide multiple insights from very different perspectives has been their purpose.

Atidot
The result is Atidot, which in Hebrew means, “fortune telling.”

What’s the problem?

Dror explained it to me:

“Insurers (or rather, actuaries) are not doing all that they could with the data they have. And there are several reasons for this.

“First, they miss the point, Insurers look at data from a statistical perspective and miss out on the insights and perspectives that can be seen from different points of view.

“Next…, the traditional modeling tools that are still being used today are cumbersome, difficult to re-model and rely heavily on manual effort. With new sources of data now available, these tools are simply inadequate to handle them.

“And third, they’re too slow. The frequency of updating the models is too long, measured in weeks and months. This is because many of the current tools are limited in scale and flexibility, unable to cater for the huge volumes of data now available to them.”

How is work done today?

Today, insurers think about key questions to ask prospective policyholders. Do you smoke? Do you drink? Do you have diabetes? What is your gender? What is your location?

Insurers map the customer’s answers onto a statistical table. This linear modeling approach provides a risk rating of a certain outcome, such as the mortality rate for a life product.

But data science does not follow a linear model. It is different and varied. Data is modeled to show different correlations of risk to key variables.

This is what Atidot does.

It applies multiple approaches simultaneously to process a much larger set of data. This will include existing data that was previously ignored, such as the day of the month the salary is paid or frequency of ATM withdrawals, through to new sources of data, such as driving behavior or activity levels.

And while it is still very new for insurers to link, for example, increased levels of activity to mortality rates, there is enough evidence to suggest that it is just a matter of time before they do. You only have to look at the number of competitions on Kaggle to see that!

This shift gets to the crux of the insurer’s problem:

Quite simply, traditional models don’t have the ability to handle the new sources of data. Nor do they have the muscle to process it.

I’ve previously covered some brilliant InsurTechs in the data space, including Quantemplate and Analyze ReFitSense is a data aggregation platform that provides insurers with a new source of data to underwrite life risk differently. The platform collects data from all major fitness and activity tracking devices. The data is then normalized (to weed out differences in the way activity is tracked) and presents the underwriter with a common score to indicate activity patterns and levels (just as Wunelli enables a driver behavior score from telematics data).

However, the challenge for insurers is knowing what to do with this data and how to handle it.

Dror put this into context for me:

“Let me give you an example from a South African life company who were building two life products – accidental disability and severe infection disease. To test our platform, we ran their traditional method alongside ours.

“We found that they had a lot of data about their customers that they were not using or taking advantage of. And even if they tried to, the actuaries did not have the means to group this data and properly assess it in their models.

“Atidot were able to group the data differently using our tech and show them how they could significantly improve the accuracy of their forecast tables.

“We showed them how they could look at data in a different way.“

This all sounded great, so I pressed Dror for examples and we started to talk about a piece of data that seemed irrelevant to a life risk assessment – the day the premium is collected.

Dror showed me a sample of data from a live pilot the company ran for a U.S. life business on a 50,000-customer sample.

It showed that customers who paid their premiums on the 14th of the month had a 20% lower lifetime value than those who paid on the 1st.

Atidot graph
By enabling multiple data models to run simultaneously and picking the best model to better understand customers, Atidot drew a relationship between data that the actuary didn’t have before. Nor would the actuary have intuitively thought of it or arrived at it through a linear modeling approach.

So, is this enough to change the way insurers rate risk? Or change the risk selection criteria for an insurer?

To answer this I turned to Alberto Chierici, co-founder of Safer and an actuarial consultant with Deloitte. He told me:

“One issue to overcome for insurers is communication to the customer and regulators. For example, in some states it is compulsory to communicate to consumers why and how rating factors (gender, age, ZIP code) are used in pricing.

“That is making many insurers reluctant to adopt machine-learning-based risk rating and pricing. Think about the example you cited about people paying the 1st of the month versus people paying the 14th – how do you explain that to customers?”

Alberto pointed me to this discussion on Kaggle to illustrate the point.

One thing is clear, the InsurTech puck is heading Atidot’s way.

 

The original version of this article appeared here.

digital innovation

The 7 Colors of Digital Innovation

InsurTech is now established in a class of its own, no longer a sub category of Fintech. In 2015, $2.65 billion of venture capital was invested in InsurTech. We now have InsurTech-focused accelerators, with the excellent Startupbootcamp in London, the Global Insurance Accelerator in Des Moines, Iowa, (about to start its second cohort) and Mundi Lab announcing its start-ups for its insurance program in Madrid.

In the past year, I have interviewed more than 50 InsurTech start-ups, and I have seen the full spectrum of characteristics and common themes that run through these innovative digital insurance businesses, which i call:

From Distribution to Data, the Spectrum of InsurTech

Red – Distribution

Distribution is all about making insurance easier to buy, consume and understand. Innovators put the customer first and build their insurance proposition from the customer out (unlike incumbents, which organize their business around internal capabilities).

These start-ups are all about the customer, and their propositions are characterized by convenience, on-demand, personalization and transparency (and, of course, digital).

Examples include;

  • Bought by Many
  • Knip
  • Cuvva
  • Insquik
  • PolicyGenius
  • Moneymeets

Orange – Enterprise

Here we see a new breed of enterprise-class software providers. These are software as a service platforms running on the cloud. They have consumption-based pricing models that replace the traditional, million-dollar, up-front license fee and multi-year implementation.

In the main, these InsurTechs have taken hold of the small and mediums-sized business (SMB) space, but it is a matter of time before they prove themselves as genuine enterprise solutions for Tier 1 insurers.

Examples include:

  • Vlocity
  • Zenefits
  • Insly
  • Surely
  • Riskmatch

Yellow – Mutual 

New peer-to-peer business models return insurance to its roots of mutualization and community. The model relies on the notion that social grouping and affinity will change behavior and address moral hazard (thereby reducing claims payouts and premiums).

The question of scalability still hangs over P2P insurance, but, if it succeeds as a business model, it could form the foundation of a new breed of insurer. Just as kids call to their parents in their hour of need, customers will call to the insurer in theirs.

Examples include:

  • Friendsurance
  • Guevara
  • TongJuBao
  • Lemonade
  • Uvamo
  • Gaggel

Green – Consensus

Blockchain technology will fundamentally change the way the insurance industry works (as well as banking and society as a whole, IMHO).

The promise is huge although as yet unproven. From smart contracts to identity authentication, from fraud prevention to claims management, blockchain technology will provide the underlying technology foundations for a trustless consensus that is transparent to all parties.

Examples include:

  • Everledger
  • Tradle
  • SmartContract
  • Dynamis
  • Blockverify

Blue – Engagement

For me, this is the most significant of the characteristics from InsurTech in personal lines. The product becomes integrated in the customer’s lifestyle. It becomes sticky and overrides the annual buying exercise, where price is the key buying criterion. Digital natives are responding well to lifestyle apps that sit on top of the underlying insurance product.

Examples include:

  • Vitality
  • Trov
  • Oscar

Indigo – Experience

The true value of insurance is only realized when the customer makes a claim. New tech solutions that improve the customer journey through the claims process will not only improve the customer experience, they will also reduce the cost of claims and claims payouts.

Examples include:

  • 360Globalnet
  • RightIndem
  • Tractable
  • Vis.io
  • Roundcube

Violet – Data

This is all about new sources of data to rate and underwrite risk. This is about using data science, machine learning, artificial intelligence and high-performance computing to process data in completely new ways.

While distribution is vital to change the way customers interact with insurers, it is the data players that hold the key to fundamental change in the way insurance is manufactured, especially in personalisztion of insurance premiums and policies.

Examples include:

  • Quantemplate
  • Analyze Re
  • Meteo Protect
  • The Floow
  • Fitsense
  • Influmetrics
  • RiskGenius