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The Dazzling Journey for Insurance IoT

When Chloe steps out the door of her apartment on her way to work in the morning, her vehicle automatically unlocks its doors while the navigation system maps out the best route based on the latest weather and traffic conditions. Simultaneously, her home’s thermostat resets, and her security system arms.

During her commute, Chloe decides to stop at a name-brand franchise for a cup of coffee. In a moment of weakness, Chloe – a diabetic – elects to consume a fresh-baked pastry along with her java. Fortunately, Chloe’s smart glucose monitoring system sends her an alert quantifying the size of the impending spike, and she responds appropriately to avert any issues.

At her destination, Chloe’s car locks and arms when she walks away from it. As she makes her way indoors, Chloe’s workspace is simultaneously adjusting to her established lighting, temperature and activity levels. During the morning hours, Chloe elects to override two of the standing periods she’s selected for her daily routine.

In the afternoon, Chloe’s home heating system detects a part is on the verge of failure. It generates a signal that triggers an automated process and orders the needed part, contacts a service provider and schedules the repair.

Moments later, Chloe receives a notification of the impending breakdown as well as the day and time of the repair appointment, which she quickly confirms – via an app on her phone – and, using the same app, books a florist visit during the repair time frame to get some expert advice on an issue with her house plants.

In the evening, as she arrives home from work, Chloe’s proximity disarms the household alarm and adjusts HVAC accordingly. After a healthful meal and her nightly yoga routine, Chloe sits down to finish reviewing several mortgage offers for the home she’s buying.

Working on the mortgage causes Chloe to think about other ways to protect her family, so she clicks on a banner ad for a customized life insurance product. After staying up beyond her usual time, Chloe retires for the night.

The Insurance IoT Imperative

Today, most of us are familiar with basic forms of the electronic connectedness known as the Internet of Things (IoT). We obtain driving directions from our smartphone assistant, order pizza via smart speakers and control smart home devices with an app.

But Chloe’s game-changing level of automated, integrated and connected IoT will arrive sooner than many people realize.

As numerous consulting firms have discussed, businesses are becoming interdependent within and across categories. This will dissolve traditional industry boundaries and replace them with a set of distinctive and massive ecosystems clustered around fundamental human and business needs.

In this article, we’ll review the current state of the Insurance IoT, explore what’s needed for future success and provide an executive-level overview of the technology considerations required for gaining favorable outcomes in a connected world.

At the Starting Line

Although IoT is most common among insurtechs, industry-wide efforts to harness insurance IoT are in their infancy. Many insurers are still focused on modernizing their core systems. Most are still struggling with defining what it means to transform into a “digital insurer” to meet escalating user experience expectations.

As the accompanying overview graphic “Market Maturity” suggests, the majority of early insurance IoT initiatives have concentrated on one type of IoT, telematics, in personal and commercial auto lines. In the U.S., adoption is still minimal, with many initiatives having yet to realize a positive ROI. However, insurers have clearly grasped the larger potential as the traction and evaluation of new entrants, like Root, have captured the market’s attention and raised the sense of urgency.

We’ve also seen some property insurance IoT efforts around residential and commercial structures. There, the focus has been assessing the impacts of mitigating various risks. By and large, even the most advanced initiatives are in the piloting or developmental phases, as insurers conduct research on sensor types, analytics tools, management systems, human interaction layers and adoption barriers.

Progress among health insurers is similar to property. Early efforts range from offering fitness trackers to arming chronic obstructive pulmonary disease (COPD) inhalers with sensors for automatic tracking of medication use. Again, initiatives are in early phases, with real-world outcomes and profitability impacts yet unknown.

See also: Insurance and the Internet of Things  

Understanding the Real Value Proposition

Moving forward, there’s little doubt the insurance industry will accelerate its embrace of insurance IoT. The true winners will be those who understand the real value proposition of insurance IoT, which are the opportunities for value creation and sharing that ultimately boost an insurer’s bottom line.

To visualize how insurance IoT improves bottom lines, compared with traditional approaches, see the graphic portraying the respected “Insurance IoT Value Creation Framework.” This waterfall framework was created by the IoT Insurance Observatory, a think tank representing over 50 North American and European enterprises, including ValueMomentum. The Observatory also includes six of the top U.S. P&C insurance groups and four of the top seven global reinsurers.

Let’s review some examples drawn from Chloe’s life, which illustrate the framework’s building blocks and how insurers benefit.

First, Chloe’s renter’s insurance is a smart policy, offering more than monetary reimbursement when something bad has happened. Her insurer sold her a safety and security service for a monthly fee. Moreover, the insurer connected its systems to her smart home infrastructure and even added some water leakage sensors that were not previously present.

The insurer created and manages the automated process that gets triggered by the signal from the heating system, enabling the insurer to intervene. Further, Chloe’s insurer receives revenue from its preferred service providers, like the repair technician and the florist, who pay the insurer a fee for automatic access to fulfilling Chloe’s needs.

Although the policy Chloe selected permits her health insurer to raise her deductible for chronically engaging in risk-elevation activities, such as the contra-indicated pastries, reduced standing periods and sleep deprivation, Chloe chose this product because her transgressions are infrequent. As for the health insurer, it gains a self-selected, lower-risk policyholder.

Chloe’s health insurer is also involved in her hyper-connected day, providing the glucose monitoring system together with an app that supplies Chloe with 24/7 access to a network of nutritionists. Chloe also receives a preferred rate for the monitoring and coaching, which reduces the insurer’s claims costs.

Some of Chloe’s other activities also reduce risks and create value. These include automatically securing her home against intrusion and keeping indoor spaces the proper temperature to avoid infrastructure incidents and damage from frozen pipes, not to mention wearing her glucose monitoring system. Chloe’s insurers benefit from the reduced probability of Chloe submitting a claim.

As for Chloe’s morning stop, she obtained her coffee for free by redeeming a QR code from her auto insurer sent as a reward for driving a certain number of miles at low risk (no hard braking, speeding, phone distractions, etc.). Previously, Chloe’s auto insurer had negotiated a very favorable rate on the coffee because the chain would benefit from cross- and up-sells, like Chloe’s impulse purchase. Chloe’s insurer reduces the risks to its book of business with this inexpensive behavior-change mechanism.

In the evening, when working on her mortgage prompted Chloe to shop for life insurance, she opted in to permit the life insurer to obtain her health records, wellness activities from her mobile phone and the current contents of her refrigerator. In real time, the insurer calculated Chloe’s life score and created an exceptionally accurate quotation. Next, the insurer presented Chloe with a competitive quote based on her age, lifestyle and health history.

Due to all of the positives, Chloe now loves her insurers. However, before her life was hyper-connected, she felt insurance was more of a necessary expense than a beneficial experience. Not only was her risk exposure greater, but she never received any rewards from her insurers. What’s more, Chloe’s insurance premiums were over 20% higher.

By leveraging IoT data, Chloe’s insurers have created bottom-line value, a portion of which they share with her via discounted prices and other incentives. This value creation/value sharing model embodies insurance IoT’s transformational potential.

What’s Required to Get There

Once you’ve fully appreciated the business value embedded in the dozens of IoT data points your policyholders create every minute of every day, you can begin to acquire the appropriate technology capabilities for gathering, analyzing and acting on the IoT data in real time.

Although this journey will involve numerous steps, a good starting point is understanding the seven primary technology layers required for insurance IoT and the key considerations for assembling them into a complete solution. For a visualization of these layers, consider the graphic “Insurance IoT Architecture” framed by the IoT Insurance Observatory.

Technology Layer 1 – Sensors

Devices that collect IoT data can range from simple, purpose-built solutions, such as a water flow detector, to complex devices that incorporate multiple types of sensors, like a smartphone. Although there’s no single “correct” type of sensor to use for any given application, it’s vital to consider both what data a given sensor is, or is not, gathering and how the sensor is collecting the data as each significantly affects analytics abilities and outcomes.

Technology Layer 2 – IoT Data Collection and Data Sources Management

Upon collection, data must be transferred for storage to a location where it, and other collected information, can be properly processed, managed and accounted for. In the early days of telematics, industry-specific solutions handled this layer.
Now, as insurance IoT scales up to require data gathering from millions of policyholders, who are each generating thousands of different types of data points every nanosecond, this layer is quickly moving to mega-vendor platforms, like Microsoft Azure. Such platforms are purpose-built for fast transfer and management of vast amounts of information, plus they provide other services like device management, data security, resiliency, load balancing and ease of integration with other systems. All of these capabilities are vital to real-time insurance IoT.

Technology Layer 3 – Insurance-Specific Data Analysis and Preparation

From this layer forward, success depends on partnering with experienced solution providers that demonstrate a granular understanding of insurance nuances, ranging from rating requirements to loss specifics. Whether you’re developing a proprietary technology layer or adopting a purpose-built solution, partnering with experienced consultants and integrators is the most effective means to achieve your goals.

Within Layer 3, collected data from all real-time, non-real time, internal and external sources gets normalized, interpreted and prepared for insurance-related purposes. A simple homeowners’ example is a combination of real-time data from smart sensors, both real-time and historical climate data from external providers and policyholder data such as contact information and preferences. The most advanced solutions for this layer now included advance algorithms and machine learning capabilities, speeding the normalization, interpretation and preparation chores.

See also: Global Trend Map No. 7: Internet of Things  

Technology Layer 4 – Advanced Insurance Analytics

In this off-line layer, advanced analytics are performed on the data from the Layer 3 to create proprietary algorithms and models that are applied in subsequent two layers. A workers’ comp example is the probability of injury based on historical claims data combined with various external data sources. Or, in an automotive scenario, risk indicators that would predict a loss cost for a particular type of accident based on a particular type of vehicle on a specific type of roadway under a specific type of climatic conditions.

Technology Layer 5 – Smart Insurance Actions

Arguably, it’s within Layer 5, and its close cousin Layer 6, where the real-time “magic” of insurance IoT occurs. In other words, these layers translate the data and information from the forgoing layers into activities insurers can use for differentiating themselves and taking advantage of new opportunities to stay competitive. The technologies in both Layer 5 and Layer 6 can be made up of internal systems, cloud-based solutions or a hybrid.

Specifically, Layer 5 rapidly applies algorithms and data from the previous layers to result in smart actions related to traditional insurance activities such as underwriting decisions, pricing calculations, claims management and cross-selling.

Technology Layer 6 – Connected Insurance Ecosystems

This layer can be thought of as a neighbor to Layer 5, rather than a vertical step up. This layer contains the partnering services and all of the connections required for the use of those services, as illustrated by Chloe’s story. However, the possibilities go far beyond those we’ve presented, making innovative thinking key to competitive success.

Technology Layer 7 – User Experience

Naturally, any successful insurance IoT deployment will involve integrating all of the forgoing back-end processes and systems with the front-end experience presented to policyholders and prospects. Such experiences should be designed as a mixture of digital and physical interactions, as insurance IoT is characterized by combining automated processes, triggered by data, with human engagement.

Note that positive user experiences depend not only on the appropriateness of each interaction but also on appropriate timing. This ensures policyholders and prospects receive what they need and when they need it, rather than alienating users with distracting interactions that cause confusion or create interference.


Regardless of which of the scenarios we’ve presented apply to your business, or where on the connectivity spectrum your enterprise is today, it’s clear the opportunities inherent in the insurance IoT offer vast possibilities for improving your bottom line and becoming beloved by your policyholders. Given the rapid paradigm shifts already underway, the greatest risk to insurers is delay. In short, the time to start building and executing your insurance IoT strategy is now.

This article was first published on Carrier Management.

UBI Is Not Usage-Based–Sorry!

Usage-based pricing is a fascinating topic for insurers. A technology that allows persistent monitoring of risk exposure during the coverage period could potentially enable insurers to price each risk at the best rate.

The potential, however, is not the reality.

In 2017, 14 million policies sent telematics data to insurers around the world, of which 4.4 million were in the U.S market, based on an estimate by the IoT Insurance Observatory, an insurance think tank that has aggregated almost 50 insurers, reinsurers and tech players between North America and Europe. (In the U.S., there were a further 3.6 million policies that are still active and commonly defined as telematics but that in the past had a dongle only and didn’t send any data to insurers last year.)

However, less than 9% of the global insurance telematics policies were characterized by usage-based pricing, which is a mechanism that charges the policyholders for the current period of coverage based on how they behave (mileage or driving behavior) during this period.

Instead, the vast majority of the telematics policies bought by customers around the world today have a defined up-front price for the current policy term. Moreover, the telematics data registered during the policy period does not affect this price in any way, and is used only for proposing a renewal price at the end of the policy. So, these policies are not usage-based because at the beginning of each policy term the customers are sure about the amount they are going to pay for the policy, regardless of their behavior during the months of coverage.

These existing implementations of telematics-based pricing are somewhat validated from consumer perceptions toward insurance. In a survey of 1,046 U.S. consumers, the Casualty Actuarial Society Insurance On-Demand Working Party has addressed and demystified some of the behavioral economics assumptions on the insurance products. The research showed that only 32% of consumers reviewed their personal lines (auto and home) coverage more than once per year. Furthermore, 89% of consumers said they would rather pay a single, stable price per year compared with paying per usage without a certainty of total price. Usage-based auto insurance, across the entire on-demand category studied by the working group, is attractive to people penalized by traditional insurance products, that is, consumers with low usage who would otherwise have to pay for more coverage than they need.

Potential and Success Stories

The usage-based approach persistently monitors the policyholders and charges (potentially) each customer a rate commensurate with actual exposure, minimizing the premium leakage in each coverage period. The resulting minimized earning volatility from usage-based pricing allows insurers to increase the leverage and through this to improve investment return and the return on equity of the company. This approach also allows for increased retention of good risks, at any pricing level, which are penalized by competitors with less accurate pricing mechanisms. The quality of the portfolio is improved (with more profitable customers) at each renewal.

The resulting lower volatility from usage-based pricing and better quality of the portfolio over time would also enable insurers to negotiate lower reinsurance costs.

But while usage-based insurance could theoretically be a profitable option for insurers, the problem seems to be the lack of customer demand for an insurance product where there isn’t a defined up-front price for all the entire coverage period..

See also: Rethinking the Case for UBI in Auto

Newcomers to the insurance market are bringing a different perspective to the problem, recognizing that small clusters of drivers who have been heavily penalized by the current insurance rates—such as extremely low-mileage drivers, or extremely safe drivers without a credit score—could be enough to start a niche business. There are a few success stories of insurtech startups, such as Insure The Box and Metromile, which have been able to build portfolios around 100,000 policies and relevant company evaluations within six to seven years.

Driving Scores at the Underwriting Stage

One way to combat the lack of market fit that has affected the usage-based adoption could be to use a driving score at the underwriting stage. This way, insurers will make an up-front quotation by using—together with traditional data—the driving data.

The value created through this approach is clear and similar to experiences the sector has had integrating new risk factors (e.g. credit scoring) in pre-existing risk models. This telematics-enhanced risk model enables more accurate pricing. This, in turn, allows insurers to generate favorable selection by attracting the best risks for each pricing level (leaving the worst to the competitors). Through the creation of smaller and more homogeneous clusters of clients, this approach even reduces premium leakage, reducing the volatility. And, if the driving score is used at each renewal, there is a chance of improving portfolio quality over time (at any pricing level), with insurers using driving scores for underwriting, benefiting from retention of the most profitable customers–those who are penalized by competitors with less accurate pricing mechanisms.

The ROI of this approach is extremely positive, but the current scenario for obtaining the customer driving score seems very different from the scenario we have known for the credit score. The credit score (or the granular data necessary to calculate it) is available on the entire customer base and certified by reliable third parties, so each insurer can gather this data any time a customer requests a quotation via an agent, a broker, a call center or even online. Moreover, anyone who doesn’t have a credit score is considered a nonstandard risk. So, the concretization of the driving score dream requires the availability and reliability of third-party data for the insurers and, most importantly, the creation of frictionless purchasing processes for the clients.

Data exchanges, which bring OEM data to insurers, have been present in the U.S. customer market for a few years, but because there are many points of friction throughout OEM funnels, they still represent only 2% of the U.S. telematics insurance portfolio. This customer fatigue is due to the need to opt in to request a quotation. Eligibility for the opt-in comes in a moment when he is not shopping around for insurance coverage (a few months after the purchase of the new car). The quotations, which are done with anonymized data, are only indicative, so the customer needs to add data later to receive the real proposal.

Try Before You Buy

A different way to concretize the wish to access a driving score any time an insurance price quotation is calculated is by using a try-before-you-buy app. Given the current level of smartphone penetration, such an app likely provides an easier way to address a large part of the market than with the data exchanges and may also reduce customer frictions. As insurtech carrier Root is currently doing, an insurer can ask a prospect to download an app on his smartphone, calculate the driving score through collected data and, after a while, calculate the quotation incorporating the customer’s driving score. Using this approach, this less-than-two-year-old auto carrier startup wrote 1.5 times more premium than the more-talked-about carrier Lemonade. (Both are insurtech carriers, although Lemonade is writing renters insurance, and Root is writing auto). Root even entered in the insurtech unicorn club in August, thanks to a $100 million round of funding raising the valuation to $1 billion.

Tailored renewal price

As mentioned, 90% of the current global telematics policies only use the driving data for tailoring the renewal price to the customers after having monitored them for a few months (rollover approach) or for the entire coverage period (leave-in approach).

Are insurers achieving any economic value through this pricing approach?

They can increase the retention of the most profitable risks at each pricing level by providing a discount at renewal. However, this additional discount reduces the profitability of these policyholders. So the chance to create some value through this “discounted retention” is linked to the presence of a high-level churn rate. If surcharges to the worst risks at each pricing level are added, insurers will have the opportunity at renewal to partially reduce the premium leakage they have identified on these risks, or push some of them toward competitors.

The accompanying chart (right side) summarizes these pricing thoughts: The expected ROI of the “discount at renewal” is definitely lower than the driving score scenario—it structurally misses the ability to have a positive up-front selection by attracting the better risks at each pricing level—but it is positive if surcharges are added.

The IoT Insurance Observatory has found that a large portion of the policies using driving data for tailoring renewal prices have not resulted in any bad driver penalties.

So, are these telematics portfolios destroying value instead of creating it?

The reality is that there is value created on these portfolios, but the value is not tied to pricing. And some of the pricing approaches are even reducing that value.

First, there are many examples of the risk self-selection impact of all the telematics-based products around the world. Even if two customers seem to be equal based on their characteristics, the one who accepts the telematics product has a lower probability of generating a loss. The stronger the monitoring message on the product storytelling, the higher the self-selection effect. The most statistically robust study is on the Italian auto insurance market, where this risk self-selection effect has accounted for 20% of the claim frequency. In this market, telematics products currently represent more than one-fifth of the personal lines auto insurance business, and the storytelling of the product is hugely focused on monitoring and customer support at the moment of a crash.

Other than risk self-selection, three other telematics-based use cases have been exploited by insurers.

Some international insurers have reinvented their claims processes through telematics data: Their new paradigm is fact-based, digital and real-time. Insurers such as UnipolSai have introduced tools for their claim handlers that allow a quicker and more precise crash responsibility identification and have been providing precious insights to support the activity of all the actors involved in the claim supply chain (both loss adjusters and doctors).

See also: Is Usage-Based Insurance a Bubble?  

A second well-demonstrated telematics use case is the change of driver behavior. VitalityDrive introduced by the South African insurance company Discovery Insure is the first insurance telematics product entirely focused on promoting safer behavior. All the product features—from gas cash-back (up to 50% of fuel spending per month) to active rewards through the app (including coffee, smoothies and car wash vouchers)—are contributing to the risk reduction of the book of business and to increased retention of the best risks.

Both the Italian and South African experiences have even been characterized by the insurers’ ability of enhancing the insurance value proposition by adding telematics-based services bundled to the auto insurance coverage. The fees paid by customers for these services almost offset all the costs of the telematics services on the insurers’ income statements

Based on the experience of the IoT Insurance Observatory, global insurance telematics best practices have generated more value through these four use cases than through pricing as of today. So, the sum of the self-selection effect, the claim cost reduction and the economic impact of changes of behavior allows an insurer to provide an important up-front discount at the same level for all the new telematics-based policyholders.

This relevant level of up-front discount — 20% or more — has been able to drive the adoption (overcoming any eventual customer privacy skepticism) because it fits with the customer desire to save money, contrasting the low adoption rates generated for more than a decade in the U.S. where up-front discount offers are typically only 5%.

The discount should be maintained, on average, at the same level at the renewal stage. Moreover, an additional economic value can be generated—at each pricing level—by providing additional discounts to the best policyholders and reducing the discount to the worst ones.

This is what the international best practices are doing today.