The U.S. has seen an explosion in what is often referred to as the emerging “sharing economy” or “collaborative consumption.” In an increasingly connected society where most people have access to mobile communication devices, peer-to-peer services are springing up, based on mobile apps that consumers can use to access transportation services that historically have either not existed or were controlled by often highly regulated business or government entities. One might argue that this is not a new concept, given that hitchhiking has been around since not long after the wheel was invented and was quite common in the 1950s and 1960s until it fell out of vogue as its inherent dangers gained more attention from the media and increasing numbers of consumers owned or had access to automobiles or mass transit. But what we’re witnessing today is a relatively new phenomenon. Uber, Zimride, Lyft, ZipCar, Turo, GetAround, TaskRabbit, JollyWheels, RentMyCar, Zilok, CityCarShare, bla, bla, bla, bla, bla…. Which brings us to BlaBlaCar, the latest incarnation of car sharing. Founded in France in 2006, BlaBlaCar now claims to operate in about a dozen European countries and is exploring expanding into other countries, such as India and Brazil. BlaBlaCar bills itself as a “ride sharing” mechanism, as opposed to “car sharing.” That falls somewhere between fee-based hitchhiking and a somewhat irregular share-the-expense car pooling arrangement. Details on how the system operates can be found at the company's web site. BlaBlaCar currently does not operate in the U.S. There is some question as to whether it can be as successful in the U.S. as it claims to be in Europe. Owning and operating a vehicle in Europe is far more costly than it is in the U.S. There is also a perception that Europeans may be more trusting of, or accustomed to, riding with strangers than Americans are. In addition, there are social issues to consider in the U.S. For example, a BlaBlaCar driver can refuse to transport particular passengers. If such a driver is white and a declined passenger applicant is black, would there be civil rights issues that could be addressed by claims or suits for discrimination? The question addressed by this article is, if BlaBlaCar were to begin operations in the U.S., would the personal auto insurance policies of its drivers cover this type of activity? According to the terms and conditions on BlaBlaCar’s web site and media articles about their service, most auto insurance in Europe covers this exposure because there is no “profit” involved. The passenger fee is referred to as a way to share the cost of a trip. The terms and conditions include a stringent hold-harmless provision and a liability cap to protect BlaBlaCar. However, the company's position on how personal auto insurance responds in Europe would be immaterial if it were to commence operations in the U.S. Many, if not most, personal auto policies in the U.S. may exclude BlaBlaCar activities regardless of whether a “profit” is sought or made. The decision could depend on the facts of each situation and the exclusion wording in the policy. The first question is whether there can be assurance that a driver is not making a profit. Second, the policy language may not consider profit to be an issue. For example, these are the two most common exclusions found in U.S. personal auto policies:

• We do not provide liability coverage for any "insured"...for that "insured's" liability arising out of the ownership or operation of a vehicle while it is being used as a public or livery conveyance. This Exclusion (A.5.) does not apply to a share-the-expense car pool.

• We do not provide liability coverage for any person...for that person's liability arising out of the ownership or operation of a vehicle while it is being used to carry persons or property for a fee. This exclusion (A.5.) does not apply to a share-the-expense car pool.

This language is taken from two different edition dates of the “ISO-standard” personal auto policy. In the case of use as a “public or livery conveyance,” ISO’s filing memorandum stated that the intent of this exclusion is to preclude coverage for vehicles available for “hire” to the general public for the transportation of people or cargo (e.g., taxis, sightseeing vans and package delivery services). The exclusion is not contingent on the profitability of the person or enterprise holding their vehicle out to the general public for hire. In the case of a vehicle used to “carry persons or property for a fee,” there is no mention whatsoever of whether this fee generates a profit for the owner/driver. In one case, this exclusion was held to apply to someone who used his pickup truck to transport a friend’s son’s belongings to college in exchange for gas money. However, both exclusions admittedly exempt a “share-the-expense car pool.” So what is meant by a “car pool”? One dictionary definition describes it as: "an arrangement between people to make a regular journey in a single vehicle, typically with each person taking turns to drive the others." Note the reference to “regular” and alternating as drivers. On the other hand, Wikipedia’s discussion of the term “carpool” implies a potentially broader concept that could include how BlaBlaCar operates. This muddies the water to the point that no blanket statement can be made about how U.S. personal auto policies might respond to claims arising from BlaBlaCar and similar ride-sharing services. If this were to become a significant exposure, one might expect U.S. insurers to define “car pool” in a way that precludes coverage for these services. In the past year or two, we have seen various forms of “car sharing” exclusionary endorsements introduced by ISO and individual insurers, though many of them still do not fully address the “share-the-expense car pool” situation. The only conclusion we can reach at this point is that how a vehicle is being used and how that use fits with an insurance policy’s insuring agreements and exclusions are becoming much more important and more difficult to determine. The insurance industry is not known for its innovation nor its ability to respond quickly to emerging social changes. The usual reaction is to exclude an unanticipated exposure until the industry can reasonably measure and predict the risk of loss. The growth of car- and ride-sharing (not to mention home-sharing) is something that will need to be closely monitored by the industry.

“Know your customers” is the new data mantra for the 21^st century. Clean, high-quality customer data gives insurers powerful marketing and service advantages and prevents expensive headaches. A well-conceived data warehouse is a good place to start, but, as core insurance systems develop problems over time, data-quality issues grow undetected in the data warehouse. These problems usually only show up when reports are generated from the warehouse and business people question the validity of the data. By then it is too late, and correcting the problem will take much time and money. So how do you avoid this problem? The answer lies in searching for small data problems before they get bigger. And, once they’ve been found, fix them right away. The same principles for running a great data warehouse apply to property/casualty, life and health insurers. All have complex challenges, but health insurers, which deal with patients, providers, employers and brokers, may face the biggest data challenges. To avoid data integrity issues, carriers should consider establishing a simple yet effective four-step program. 1. Control totals The standard approach is to keep track of the number of records in the file and make sure that same number end up in the warehouse. That's a good start, but take this concept further and use it with individual fields that are important for the business. For example, while loading patient data, we can get the control counts for male/female and match them with the membership system. Another example would be to get the control count based on age bands and make sure they match the membership system. 2. Aggregate data and check for trends Your system should aggregate certain data to make sure that the percentage is as expected and lies within a trend. For example, in a typical month, 18% of members may have claims. If that number is suddenly showing up as 8% or 28%, you know you probably have a data problem. To track the change, calculate the percentage that matched upfront and store it in the aggregate table. Storing of the aggregated data helps identify problems with the data quickly if trends change. 3. Set up automatic alerts Your system should automatically issue alerts whenever it detects a problem: controls totals that do not match or a percentage that’s outside the range of expected results. 4. Build and empower data teams Build a team whose job is to identify the data-quality issues. This team has to be knowledgeable about the business and understand trends. Data-quality team members should include representatives of various business departments and IT. When any problems arise, the data-quality team will report them to the data steward/governance team. The latter team is empowered to take prompt corrective action. The key to making any data warehouse successful is to continually build trust and credibility in the data. Checking for data anomalies is not a one-time thing. It needs to be done continuously as part of a healthy data program. Having a set of strategies for automating data-quality checking helps maintain the trust in data over time. Building a support team that is vigilant about finding data-quality issues is a must for continuing data quality.

While the Internet of Things (IoT) accounts for approximately 1.9 billion devices today, it is expected to be more than 9 billion devices by 2018—roughly equal to the number of smartphones, smart TVs, tablets, wearable computers and PCs combined. But, for the IoT to scale beyond early adopters, it must overcome specific challenges within three main categories: technology, privacy/security and measurement. Following are 12 hurdles that are hampering the growth of the IoT: 1. Basic Infrastructure Immaturity IoT technology is still being explored, and the required infrastructure must be developed before it can gain widespread adoption. This is a broad topic, but advancement is needed across the board in sensors themselves, sensor interfaces, sensor-specific micro controllers, data management, communication protocols and targeted application tools, platforms and interfaces. The cost of sensors, especially more sophisticated multi-media sensors, also needs to shrink for usage to expand into mid-market companies. 2. Few Standards Connections between platforms are now only starting to emerge. (E.g., I want to turn my lights on when I walk in the house and turn down the temperature, turn on some music and lock all my doors – that’s four different ecosystems, from four different manufacturers.) Competing protocols will create demand for bridge devices. Some progress is emerging in the connected home with Apple and Google announcements, but the same must happen in the enterprise space. 3. Security Immaturity Many products are built by smaller companies or leverage open source environments that do not have the resources or time to implement the proper security models. A recent study shows that 70% of consumer-oriented IoT devices are vulnerable to hacking. No IoT-specific security framework exists yet; however, the PCI Data Security Standard may find applicability with IoT, or the National Institute of Standards and Technology (NIST) Risk Management Guide for ITS may. 4. Physical Security Tampering IoT endpoints are often physically accessible by the very people who would want to meddle with their results: customers interfering with their smart meter, for example, to reduce their energy bill or re-enable a terminated supply. 5. Privacy Pitfalls Privacy risks will arise as data is collected and aggregated. The collation of multiple points of data can swiftly become personal information as events are reviewed in the context of location, time, recurrence, etc. 6. Data Islands If you thought big data was big, you haven’t see anything yet. The real value of the IoT is when you overlay data from different things -- but right now you can’t because devices are operating on different platforms (see #2). Consider that the connected house generates more than 200 megabytes of data a day, and that it’s all contained within data silos. 7. Information, but Not Insights All the data processed will create information, eventually intelligence – but we aren’t there yet. Big data tools will be used to collect, store, analyze and distribute these large data sets to generate valuable insights, create new products and services, optimize scenarios and so on. Sensing data accurately and in timely ways is only half of the battle. Data needs to be funneled into existing back-end systems, fused with other data sources, analytics and mobile devices and made available to partners, customers and employees. 8. Power Consumption and Batteries 50 billion things are expected to be connected to the Internet by 2020 – how will all of it be powered? Battery life and consumption of energy to power sensors and actuators needs to be managed more effectively. Wireless protocols and technologies optimized for low data rates and low power consumption are important. Three categories of wireless networking technologies are either available or under development that are better suited for IoT, including personal area networks, longer-range sensors and mesh networks and application-specific networks. 9. New Platforms with New Languages and Technologies Many companies lack the skills to capitalize on the IoT. IoT requires a loosely coupled, modular software environment based on application programming interfaces (APIs) to enable endpoint data collection and interaction. Emerging Web platforms using RESTful APIs can simplify programming, deliver event-driven processes in real time, provide a common set of patterns and abstractions and enable scale. New tools, search engines and APIs are emerging to facilitate rapid prototyping and development of IoT applications. 10. Enterprise Network Incompatibility Many IoT devices aren’t manageable as part of the enterprise network infrastructure. Enterprise-class network management will need to extend into the IoT-connected endpoints to understand basic availability of the devices as well as manage software and security updates. While we don’t need the same level of management access as we do to more sophisticated servers, we do need basic, reliable ways to observe, manage and troubleshoot. Right now, we have to deal with manual and runaway software updates. Either there’s limited or no automated software updates or there are automatic updates with no way to stop them. 11. Device Overload Another issue is scale. Enterprises are used to managing networks of hundreds or thousands of devices. The IoT has the potential to increase these numbers exponentially. So the ways we currently procure, monitor, manage and maintain will need to be revisited. 12. New Communications and Data Architectures To preserve power consumption and drive down overall cost, IoT endpoints are often limited in storage, processing and communications capabilities. Endpoints that push raw data to the cloud allow for additional processing as well as richer analytics by aggregating data across several endpoints. In the cloud, a "context computer" can combine endpoint data with data from other services via APIs to smartly update, reconfigure and expand the capabilities of IoT devices. The IoT will be a multi-trillion industry by 2020. But entrepreneurs need to clear the hurdles that threaten to keep the IoT from reaching its full potential. This article was co-written with Daniel Eckert. The article draws on PwC's 6th Annual Data IQ Survey. The article first appeared on LinkedIn.

A Lyft passenger was killed over the weekend near Sacramento, an incident that underscores inadequacies in California's insurance laws. As reported in Forbes, the Lyft driver saw a stalled Kia on Interstate 80 and swerved to the right, hitting a tree. One passenger died. The second passenger and the Lyft driver were injured. An incident like this causes one to ponder the arbitrary rules governing life, death and the arbitrary rules governing uninsured and underinsured motorist coverage (UM/UIM) in California. For example, the news article asserts that “if the death had been caused by a hit-and-run driver, the accident would be covered under Lyft’s $1 million uninsured/underinsured motorist policy.” Not so. Lyft does not disclose on its web site the provisions of its UM/UIM coverage, so let’s assume it tracks California Ins. Code sec. 11580.2 governing UM/UIM coverage. Subsection (b)(1) requires “physical contact” with the UM/UIM vehicle. If the Lyft driver hit a tree rather than the car, there is no physical contact with the UM/UIM vehicle. The nimble driver who avoids the pile-up but comes to grief on a tree receives no UM/UIM coverage. Neither do the passengers.  It makes no difference whether a convention of 20 bishops saw the hit-and-run vehicle, or even if the accident were recorded on video. Many jurisdictions (about half) find this restriction unnecessary, so why should California leave the passengers and driver unprotected? Assume the hit-and-run driver is discovered and the driver carries $1 million in liability coverage (highly improbable). The pile-up included a number of cars, plus a big rig, and caused numerous injuries. By the time this $1 million is spread around, there may only be leftovers for the occupants of the Lyft car. Because the coverage is inadequate to compensate their injuries, they can, then, call on Lyft’s $1 million UM/UIM coverage, right?. Not so. UM/UIM coverage is triggered only when the underinsured party’s limits are “less than the uninsured motorist limits carried on the motor vehicle of the injured person [the Lyft car in this case].” Subsection (p)(2). It makes no difference that the responsible party is grossly underinsured with respect to the damages. Again, this is a restriction many states (e.g., Arkansas) find unnecessary. Now assume the responsible driver has a $50,000 limit, and a gravely injured Lyft passenger accepts $49,500 in settlement with the underinsured driver. Now the passenger may look to Lyft’s $1 million UM/UIM policy for compensation, right?. Not so. By failing to collect the remaining $500, the passenger has forfeited any claim to the $1 million UM/UIM coverage. Subsection (p)(3) provides that UIM coverage does not apply “until the limits of bodily injury liability  policies applicable to all insured motor vehicles causing the injury have been exhausted by payments of  judgments or settlement . . . .” Thus, the passenger must go to trial against the intransigent party to collect the remaining $500. Once again, many states (e.g., Nevada, Idaho) do not follow this restriction. Once a gravely injured Lyft passenger has collected the $50,000 limit from the responsible party, the passenger may recover any remaining damages up to the $1 million limit of Lyft’s UM/UIM policy, right? Not so. California allows the UM/UIM carrier to subtract from its limits any recovery from other parties regardless of the extent of the passenger’s injuries, according to Subsection (p)(4)(A). Yes, once again, many states (e.g., Nevada, Utah) do not endorse this setoff rule. California’s UM/UIM rules fall well below best practices in other states. If Arkansas can have better UM/UIM coverage, why not California? California should follow the lead of other states and scrap these arbitrary restrictions. Even if California's rules were tolerable with respect to private automobile insurance, in commercial settings the public is entitled to more protection (otherwise, why must Lyft carry $1 million bodily injury and $1 million UM/UIM coverage when only $15,000/$30,00 is required for private auto, and UM/UIM is optional?) Changes could be accomplished either by legislation or possibly by the California Public Utilities Commission's specifying the commercial UM/UIM coverage requirements for charter party carriers. After all, if Uber, Lyft and others are to operate in other states, they must purchase UM/UIM policies that do not have these restrictions.

There is an old line in Silicon Valley: “Never confuse a clear view with a short distance.” We should keep that in mind as we think about wearable devices such as the Apple watch that are designed, among other things, to help us monitor and improve our health. The view is crystal clear, but we’re still a long way from getting to the destination.

The vision is idyllic: Some day, a wearable device will monitor all our vital signs and relay the information second by second to a healthcare provider, where some combination of computers and doctors will monitor it. We’ll know two weeks ahead of time that we’re about to have a heart attack and will be able to head it off. Doctors, who currently spend only about seven minutes a year with the average patient, will mine the stream of information, spot chronic issues in more people and get them treatment for, say, high blood pressure. Our knowledge about health will increase exponentially because so many aspects of so many people will be tracked, and in real time.

Researchers say the change in health will be like what has happened with cars. We used to wait until we saw steam coming out from under the hood, then fix whatever was wrong. Those cars lasted 60,000 or 70,000 miles. Now we have sensors all over the place in cars, learn about problems before they become acute and gather voluminous data on what works and what doesn’t, so cars can keep getting better. As a result, many cars last more than 200,000 miles. With people, once we can get those sensors “under the hood,” we should also see huge improvements in health and life expectancy – engine performance, too.

But three major things have to happen before we achieve that idyllic vision, and only one is even close to reality.

The one change that could at least plausibly happen soon is that people adopt wearables en masse. No more of this buy a Fitbit, wear it for a couple of months and then set it aside. At least millions of people, and maybe tens of millions, will have to buy wearable devices and keep them on 24/7 for basically forever, just to really get the movement started. That sort of adoption will require smaller and better-designed wearables and far better battery life – the early line on the Apple watch is that it won’t even go a full day on a charge. Makers of wearables will also have to agree on standards so that all health data can be integrated into any software and analyzed by any healthcare provider. At the moment, every wearable maker wants to own the standard, and standards fights can take years to sort out, but with Apple working its magic on consumers and with Microsoft introducing a well-regarded device, it’s at least possible to imagine mass adoption within a few years.

That’s the easiest problem.

The most severe problem is that wearables aren’t yet close to collecting the really useful information. Wearables can monitor your pulse and provide a reasonable estimate of how many steps you take, but that’s not the good stuff, as far as medicine is concerned.

I got a tutorial on this almost 15 years ago from Astro Teller, who cofounded Body Media, a pioneer in the wearables field. He said the data he really needed was blood pressure and information from blood tests. Astro is a seriously smart fellow – the grandson of the principal developer of the hydrogen bomb, Edward Teller, Astro has since 2010 been directing the Google X laboratory, meaning he has the Google Glass, driverless car and many other cutting-edge projects reporting to him – but Body Media never cracked the code before being acquired by Jawbone for $110 million, principally for its patents, in 2013. While there are glimmerings of progress all over, no breakthrough seems especially close.

Google, for one, has a project in the Google X lab that puts sensors in contact lenses that can measure blood sugar and send a constant, wireless signal to a wearable device, giving diabetics a noninvasive way to monitor themselves. But the technology must now be calibrated for different conditions. What if the wearer is crying? What if the weather is dry? What if it’s raining? It’s not clear how close to market the technology is.

Others talk about having people swallow sensors that would roam the bloodstream and report on all kinds of conditions, including watching out for cancer, but those are far enough out that they still read like science fiction.

A company has a prototype of a device that would measure blood pressure constantly, but, even if that proves workable, the device needs to go through multiple iterations and become tiny enough that it can fit into a general-use device – people may wear one health-related device on an arm, but they won’t wear two or three or four.

The final hurdle that has to be cleared is doctors and other practitioners. When I talk to doctors about the idyllic vision for the future of healthcare, they look at me like I have two heads. They’re feeling swamped just trying to keep up in a world where they see the average patient a few minutes a year, and their problems will only get worse if talk of a physician shortage proves true. Now we want them to go from seven minutes a year to 525,600 (the number of minutes in a year) for each patient? Yeah, right.

Even if doctors and other practitioners sign up for this new world of healthcare, every support system will have to change. Computer systems will have to be set up to do the vast majority of monitoring. Software will have to be written. A new class of data analysts will have to be developed. Health practices will have to reshape themselves around data streams. Insurers will have to adjust coverage. Courts will have to sort out where liability for mistakes falls – with a programmer, a doctor, someone else?

You could start the clock now on all these changes in medical practices, and they’d still take years to sort out.

The key issue to monitor in the progress of wearables is the sensors. Once someone can easily capture blood pressure information or conduct some important blood test without breaking the skin, well, then we’re talking. At that point, consumer adoption will be a solvable problem. So will adoption by medical professionals, though that will be a long slog.

In the meantime, we will soon be able to buy our Apple watches, and we’ll have fun with them. We might even get a little healthier if we keep wearing the things and somehow feel the need to walk a bit more. But that shiny vision of a world where care, insurance and everything else about health changes because of wearables? That’s still a long way out there.

In my experience, there are a number of traps that organizations fall into when they are identifying the risks they face. The traps make it very difficult to manage the risks. #1 – The Broad Statement Some organizations fall into the trap of capturing “risks” that are broad statements as opposed to events or incidents. Examples include: • Reputation damage; • Compliance failure; • Fraud • Environment damage These terms tell us nothing and cannot be managed – even at a strategic level. Knowing that you might face, say, reputation damage doesn't help you understand what might hurt your reputation or how you prevent those incidents from happening. #2 – Causes as Risk The most common issue I see with risk registers is that many organizations fall into the trap of capturing “risks” that are actually causes as opposed to events/incidents. The wording that indicates a cause as opposed to a risk include: • Lack of …. (trained staff; funding; policy direction; maintenance; planning; communication). • Ineffective …. (staff training; internal audit; policy implementation; contract management; communication). • Insufficient …. (time allocated for planning; resources applied). • Inefficient …. (use of resources; procedures). • Inadequate …. (training; procedures). • Failure to…. (disclose conflicts; follow procedures; understand requirements). • Poor….. (project management; inventory management; procurement practices). • Excessive …. (reporting requirements; administration; oversight). • Inaccurate…. (records; recording of outcomes). These "risks" also tell us very little and, once again, cannot be managed. Knowing that you might face a lack of training, for instance, doesn't tell you what incidents might occur as a result or help you prevent them. #3 – Consequences as Risk Another trap that organizations fall into when identifying risk is capturing “risks” that are actually consequences as opposed to events or incidents. Examples include: • Project does not meet schedule; • Department does not meet its stated objectives • Overspending Once again – these are not able to be managed. Having a project not meet schedule is the result of a series of problems, but understanding the potential result doesn't help you prevent it. So, if these are the traps that organizations fall into, then what should our list of risks look like? The answer is simple – they need to be events. I look at it this way – when something goes wrong like a plane crash, a train derailment, a food poisoning outbreak, major fraud .etc. it is always an event. After the event, there is analysis to determine what happened, why it happened, what could have stopped it from happening and what can be done to try to keep it from happening in the future. Risk management is no different – we are just trying to anticipate and stop the incident before it happens. The table below shows the similarities between risk management and post-event analysis: To that end, risk analysis can be viewed as post-event analysis before the event's occurring. The rule of thumb I use is that if the risk in your register could not have a post-event analysis conducted on it if it happened – then it is not a risk! If you apply this approach to your list of risks events, you will: • Reduce the number of risks in your risk register considerably; and (more importantly) • Make it a lot easier to manage those risks. Try it with your risk register and see what results you get. A Risk Is a Risk Commonly, people talk of different types of risk: strategic risk, operational risk, security risk, safety risk, project risk, etc.  Segregating these risks and managing them separately can actually diminish your risk-management efforts. What you need to understand about risk and risk management is that a risk is a risk is a risk -- the only thing that differs is the context within which you manage that risk. All risks are events, and each has a range of consequences that need to be identified and analyzed to gain a full understanding. For example; You have a group identifying hazard risks, isolated from the risk-management team (a common occurrence), and they tend to look at possible consequences in one dimension only – the harm that may be caused. Decisions on how to handle the risk will be made based on this assessment. What hasn’t been done, however, is to assess the consequence against all of the organizational impact areas that you find in your consequence matrix.  As a result, the assessment of that risk may not be correct; for instance, there may be significant consequences in terms of compliance that don't show up as an issue in terms of safety. If you only look at risk in one dimension, you may make a decision that creates a downstream risk that is worse than the event you're trying to prevent. For instance, you may mitigate a safety-related risk but create an even greater security risk. The moral of the story: Managing risk in silos will diminish risk management within your organization. In about 80% of cases, you can’t do anything about the consequences of the event; what you are trying to do is stop the event from happening in the first place.

There is an old line in Silicon Valley: “Never confuse a clear view with a short distance.” We should keep that in mind as we think about wearable devices such as the Apple watch that are designed, among other things, to help us monitor and improve our health. The view is crystal clear, but we’re still a long way from getting to the destination. The vision is idyllic: Some day, a wearable device will monitor all our vital signs and relay the information second by second to a healthcare provider, where some combination of computers and doctors will monitor it. We’ll know two weeks ahead of time that we’re about to have a heart attack and will be able to head it off. Doctors, who currently spend only about seven minutes a year with the average patient, will mine the stream of information, spot chronic issues in more people and get them treatment for, say, high blood pressure. Our knowledge about health will increase exponentially because so many aspects of so many people will be tracked, and in real time. Researchers say the change in health will be like what has happened with cars. We used to wait until we saw steam coming out from under the hood, then fix whatever was wrong. Those cars lasted 60,000 or 70,000 miles. Now we have sensors all over the place in cars, learn about problems before they become acute and gather voluminous data on what works and what doesn’t, so cars can keep getting better. As a result, many cars last more than 200,000 miles. With people, once we can get those sensors “under the hood,” we should also see huge improvements in health and life expectancy – engine performance, too. But three major things have to happen before we achieve that idyllic vision, and only one is even close to reality. The one change that could at least plausibly happen soon is that people adopt wearables en masse. No more of this buy a Fitbit, wear it for a couple of months and then set it aside. At least millions of people, and maybe tens of millions, will have to buy wearable devices and keep them on 24/7 for basically forever, just to really get the movement started. That sort of adoption will require smaller and better-designed wearables and far better battery life – the early line on the Apple watch is that it won’t even go a full day on a charge. Makers of wearables will also have to agree on standards so that all health data can be integrated into any software and analyzed by any healthcare provider. At the moment, every wearable maker wants to own the standard, and standards fights can take years to sort out, but with Apple working its magic on consumers and with Microsoft introducing a well-regarded device, it’s at least possible to imagine mass adoption within a few years. That’s the easiest problem. The most severe problem is that wearables aren’t yet close to collecting the really useful information. Wearables can monitor your pulse and provide a reasonable estimate of how many steps you take, but that’s not the good stuff, as far as medicine is concerned. I got a tutorial on this almost 15 years ago from Astro Teller, who cofounded Body Media, a pioneer in the wearables field. He said the data he really needed was blood pressure and information from blood tests. Astro is a seriously smart fellow – the grandson of the principal developer of the hydrogen bomb, Edward Teller, Astro has since 2010 been directing the Google X laboratory, meaning he has the Google Glass, driverless car and many other cutting-edge projects reporting to him – but Body Media never cracked the code before being acquired by Jawbone for $110 million, principally for its patents, in 2013. While there are glimmerings of progress all over, no breakthrough seems especially close. Google, for one, has a project in the Google X lab that puts sensors in contact lenses that can measure blood sugar and send a constant, wireless signal to a wearable device, giving diabetics a noninvasive way to monitor themselves. But the technology must now be calibrated for different conditions. What if the wearer is crying? What if the weather is dry? What if it’s raining? It’s not clear how close to market the technology is. Others talk about having people swallow sensors that would roam the bloodstream and report on all kinds of conditions, including watching out for cancer, but those are far enough out that they still read like science fiction. A company has a prototype of a device that would measure blood pressure constantly, but, even if that proves workable, the device needs to go through multiple iterations and become tiny enough that it can fit into a general-use device – people may wear one health-related device on an arm, but they won’t wear two or three or four. The final hurdle that has to be cleared is doctors and other practitioners. When I talk to doctors about the idyllic vision for the future of healthcare, they look at me like I have two heads. They’re feeling swamped just trying to keep up in a world where they see the average patient a few minutes a year, and their problems will only get worse if talk of a physician shortage proves true. Now we want them to go from seven minutes a year to 525,600 (the number of minutes in a year) for each patient? Yeah, right. Even if doctors and other practitioners sign up for this new world of healthcare, every support system will have to change. Computer systems will have to be set up to do the vast majority of monitoring. Software will have to be written. A new class of data analysts will have to be developed. Health practices will have to reshape themselves around data streams. Insurers will have to adjust coverage. Courts will have to sort out where liability for mistakes falls – with a programmer, a doctor, someone else? You could start the clock now on all these changes in medical practices, and they’d still take years to sort out. The key issue to monitor in the progress of wearables is the sensors. Once someone can easily capture blood pressure information or conduct some important blood test without breaking the skin, well, then we’re talking. At that point, consumer adoption will be a solvable problem. So will adoption by medical professionals, though that will be a long slog. In the meantime, we will soon be able to buy our Apple watches, and we’ll have fun with them. We might even get a little healthier if we keep wearing the things and somehow feel the need to walk a bit more. But that shiny vision of a world where care, insurance and everything else about health changes because of wearables? That’s still a long way out there.

Commercial lines insurance is tricky. On the one hand, it’s one of the last bastions of niche underwriting expertise -- there are specialist writers who know their markets better than anyone else and enjoy customer loyalty and consistently superior profits as a result. On the other hand, it’s still simply insurance — and it’s experiencing many of the same symptoms that the rest of the P&C insurance industry is facing:

• Increasingly sophisticated underwriting and pricing models, opening the door to adverse selection
• An ever-shrinking number of market-dominating insurers
• An evolving marketplace, with technology companies entering insurance and a customer base increasingly made up of Millennials

These three issues are forcing fundamental changes in the way insurers operate, creating a dynamic in which there will be clear winners and losers. It’s important to recognize the changing conditions to be successful both today and in the future. New players entering insurance A look at recent headlines reveals some well-recognized brands that are now turning an eye toward insurance. Tech companies like Google and Facebook, e-commerce giants like Amazon and Overstock and retailers such as Walmart and IKEA are all making waves. It’s also not just personal lines that’s being affected; Overstock’s new insurance agency offers business insurance, including workers' compensation. Why is there a sudden interest from new players? These companies oftentimes see opportunities for profit and growth in industries when there are fundamental inefficiencies that can be exploited, and generally start by employing data-driven strategies to compete on customer acquisition. Insurance executives understand this threat, as evidenced in a recent survey by the Economist that identified distribution (i.e., acquisition of customers) as the No. 1 vulnerability for disruption. What’s particularly troubling is that half the respondents in the survey were unsure of how well prepared the industry is for the changes coming in the next five years. With competition from non-traditional companies that are technologically savvy, are trusted by consumers and have money to burn, insurers simply cannot afford to be uncertain about their future. Catering to a new generation Numbering 76.6 million in the U.S., Millennials are the largest population in the country. Unfortunately for the industry, this group also happens to be the demographic most dissatisfied with insurance products and services. The U.S. Census Bureau notes that Millennials represent $1.68 trillion in annual purchasing power, which means insurance has no choice but to adapt to the needs of their future customers, business owners and employees. The industry tends to categorize Millennials as a personal lines concern (homeowners and personal auto, specifically), but commercial lines will fall behind and be caught off guard without a new mindset. Although insurance doesn’t carry the best reputation among Millennials, there is an opportunity to educate them and win their loyalty. According to a poll by the Griffith Insurance Education Foundation, Millennials know very little about the insurance industry -- 80% of students answered that they didn’t know anything about the industry at all (and only 5% claimed to be very knowledgeable. This is likely why Millennials tend to purchase insurance from companies with easy-to-navigate websites and don’t always base decisions based on price alone, according to a 2014 J.D. Power survey. If insurers don’t make the Millennial generation a priority, the likelihood of them buying insurance from other companies with better name recognition grows more and more likely, even if that company isn’t a traditional insurer. The risk of adverse selection grows as insurers get smarter The increased use of advanced technologies among insurers spells trouble for those that are still playing catch up -- the risk of adverse selection grows as competitors leverage predictive modeling techniques and gain access to new data sources that provide a broader view of the market. As analytics becomes more pervasive in commercial lines underwriting, leading insurers will become more adept at increasing their market share. Insurers without advanced analytical tools are more likely to bind higher-risk policyholders at inadequate rates and less likely to bind lower-risk policies by failing to match their competitor’s lower-price offering. Adverse selection is an insidious threat, because it’s completely invisible until well after it has infected a portfolio. Accurate pricing and superior risk selection are key, and predictive techniques have served as an important competitive advantage, going beyond traditional heuristics alone. How winners gain their advantage Workers’ compensation is, in many ways, a leading example for the rest of commercial lines. Combined ratios continue to decrease, making it more attractive for companies (both traditional and non-traditional) to come in. This crowds the market, forcing the industry to adopt more sophisticated competitive strategies. When properly developed and implemented, the use of predictive models is a recipe for success. It helps to achieve the pricing precision needed to stay ahead of the competition while avoiding adverse selection in the marketplace. Insurers without the ability to identify and react to adverse selection will lose their competitive advantage and simply not survive. As we’ll share in Valen’s annual outlook report for commercial lines this week, the benefit to using the latest analytical tools are huge, and the cost of doing nothing is equally as significant. Working the hard and soft market cycles no longer brings competitive differentiation -- the competition is fierce in target market segments ,where superior risk selection and pricing clearly determines who the winners and losers are. This article first appeared on WorkCompWire.

From Ben Franklin (inventor of the lightning rod and bifocals) to Thomas Edison (the phonograph and popular form of the incandescent light bulb) to Tim Berners-Lee (the World Wide Web)/Internet, inventions have spawned new generations of ideas and disrupted, transformed and created businesses. That process continues. . . but at a much faster pace, with smart phones, social media, the Internet of Things and much more. Today, there is the shared economy movement, built around crowdsourcing and open innovation. There is no turning back … only moving forward. Now, we instantly interact with businesses and individuals on different mobile devices. Collaboration via the Internet has quickly become mainstream in our daily lives, both personally and professionally. Mass collaboration is rising to new heights via crowdsourcing and open innovation, creating transformative outcomes. Crowdsourcing enables companies to tap into the power of the masses and communities, while open innovation helps identify, develop and market new ideas, products, services and more within these communities. Together, the combination obliterates the traditional internal, hierarchical or linear thinking and development approaches and creates an entirely new playing field that accelerates the execution of innovation within organizations. Just a few years ago, crowdsourcing was viewed as a method for ideation – a method to have people collaborate online, in an open forum, to develop the best ideas. But today crowdsourcing has moved well past this to new, more sophisticated and disruptive levels. Crowdsourcing is eliminating the traditional boundaries between companies, creating a porous environment to engage the rest of the world, whether customers, partners or others. It is fueling open innovation and the development of new businesses at an unprecedented pace that, in turn, fuels change in traditional businesses. It is reshaping business and the economy, creating a major new outside industry trend – the shared economy. According to a Forbes article, “Airbnb And The Unstoppable Rise Of The Share Economy,” in January 2013, for 2013 the revenue flowing through the shared economy directly into peoples’ wallets surpassed $3.5 billion, with growth exceeding 25%. It was noted that this rate of peer-to-peer sharing was moving beyond being an income boost to becoming a disruptive economic force. Fueling this trend are the Millennials, a large and influential economic group. Strapped with high college loan debt that limits their ability to purchase homes, cars or other high-value items, they are trending toward subscribing instead of buying music, movies or TV shows (thanks to the likes of Pandora, Netflix and others). They prefer to access news from Twitter, Facebook or Flipboard, or to buy used goods from eBay or Craigslist. Millennials have grown up with the technology that enables sharing, accessing or subscribing as an acceptable alternative to owning. The shared economy empowers people to become co-creators, funders and customers of new businesses that are disrupting traditional industries. Just consider Airbnb and Uber, two companies viewed as leaders in the shared economy that are reportedly worth billions, rivaling in value their traditional counterparts, taxis and hotels. In this new shared economy, traditional companies – like insurers – must quickly adapt to be relevant. Insurers need to begin to ask themselves these questions: What new products and services can we provide to these new business models? How will the new models reshape discounts or the bundling of insurance products? How could we partner with some of these new businesses? How will we need to rethink the customer relationship? The shared economy is empowering individuals and businesses to access specialized skills, resources, goods or services from anyone, anywhere, anytime. New business models are challenging decades of business assumptions that were based on ownership rather than short-term access or subscription. As a result, the fundamentals of insurance are being redefined, from risk models to pricing, products and services. While many insurers may see crowdsourcing and open innovation as risky, other industries are experiencing the transformative power. They are fueling the intensity and raising customer expectations that will affect insurance. Collaboration must happen both within and outside the insurance industry because the challenges and opportunities have become much bigger and broader. Insurers must reorient their business practices from product development to services aimed at creating more value and a deeper customer experience. There is an unparalleled opportunity for any company, in any industry, to ignite a new future that is powered by the human imagination through crowdsourcing and open innovation. The overriding and most critical question for insurers is not if, but how will they embrace the shared economy, crowdsourcing and open innovation – first to get in the game, then to influence change, and ultimately to win.

Loss expenses are on the rise, at an alarming rate, according to California's Workers' Compensation Insurance Rating Bureau (WCIRB). The California Workers Compensation -- Aon Advisory Bulletin (July 2014) indicates that “allocated costs (mostly attorney payments) increased 7.3% in 2013. Unallocated costs increased 10.3%.” Given that legal costs are on the rise, here are nine ways that risk managers can more closely manage legal services: Have in-house counsel monitor outside counsel (and adjuster performance). Litigation costs must be properly managed because overzealous defense counsel and untrained (or cooperating adjusters) can prolong litigation, increase costs for the employer and wreak havoc on the lives of injured workers. Review outside counsel financial arrangements -- consider capped fees, flat fees or “invoice paid upon file completion.” Paying at the end allows outside counsel to defend the claim but discourages unnecessary hearings and runaway fees and lets risk management easily review the ultimate fee rather than numerous monthly bills. Excessive fees are more noticeable and easier to compare against other files and law firms. Attorneys who are milking the claim become more visible. An “invoice paid upon file completion” is a good approach if you use the same attorney frequently. However, this approach should not be used when the defense counsel only has one file. You could end up with an excessive bill, with little recourse other than to fight with your own chosen counsel over the amount. Conduct an independent audit to assess whether defense counsel was needed in the first place, or whether she was just assigned the case to do work the adjuster, assigned too many cases, was too busy to do. A favorite ploy of overworked adjusters (and lazy adjusters) is to allow the defense counsel to handle the claim. Legal counsel should not be paid to do the adjuster’s job, including gathering medical reports, state board records and ISO reports, arranging independent medical exams (IMEs), etc. An independent claims audit of your files will tell you whether you are paying legal fees for the work the adjuster should be doing. Review hearing rulings. Review whether the same attorneys are requesting hearings on the same issue repeatedly or requesting hearings on issues they are likely to lose. For example, if benefits are terminated but reinstated at the hearing, and this happens repeatedly, it is an indication that benefits are being terminated without sufficient cause, thereby creating unnecessary legal expense. In insurance speak, this is called “churning” files. Churning is any unnecessary activity undertaken by defense counsel for the sole purpose of increasing the legal services bill. It can be unnecessary research on a subject the attorney should know, unnecessary motions, unnecessary discovery, having another attorney in the firm review the case, having a paralegal or junior partner undertake an unnecessary action, etc. Before any preparation by defense counsel for the hearing, the adjuster should phone the defense attorney and discuss the need for the hearing and what the probable outcome will be. If you know going into the hearing that you are going to lose, have counsel resolve the issue with the opposing counsel. It will save both legal fees and unnecessary claim costs (indemnity and medical costs continue while you wait for the hearing). By removing the unnecessary hearings, you move the file faster, with less overall claim cost, to the final resolution. Review whether opportunities for agreement between counsel are ignored. Defense counsel may avoid agreement because it is more profitable to have a junior attorney attend hearings and collect a large fee. For example, in Connecticut, a claimant’s doctor can be changed, with agreement of counsel, but defense counsel rarely agree even though knowledgeable counsel will know which doctors have reputations for overtreating and overrating disability, which doctors are known for unbiased treatment and ratings and which doctors have a reputation for being conservative in their treatment and ratings. Review whether defense counsel makes unfounded accusations against claimant of misbehavior or wrongdoing (e.g. claimant is not credible or is trying to game the system) on every claim to obfuscate the issues and prolong the litigation. If defense counsel is not totally objective in his assessment of both the claim and the claimant, it is time to immediately identify new defense counsel. Look at whether the attorney charges for lots of research, on many files. Very little research is necessary except in unusual claims with issues of law, so files with legal research should be reviewed very carefully. Adjusters -- with sufficient authority -- should attend all hearings with defense counsel. Sometimes, there are opportunities to settle litigation during hearings. These opportunities should be considered while someone with the requisite authority is present. In many cases, seasoned adjusters are capable of attending hearings without defense counsel. (This is not allowed in some jurisdictions.) Risk managers (or the company human resources manager or the workers’ compensation coordinator) should attend all hearings to be available to testify about the job requirements and efforts to provide transitional duty and to show interest in the injured worker’s well-being. Specify this procedure in the account handling instructions. To verify you are controlling your legal fees, a two-pronged approach is needed. A litigation management review by an independent claims auditor will determine the effectiveness of your adjusters in controlling legal expenses. This should be combined with an audit of the legal invoices by an experienced legal bill auditor.