How close is the auto insurance end game? Implications of ADAS and autonomous cars on the re/insurance industry

Anand Rao, Scott Fullman, Balaji Jayakumar, Spencer Allee, 04 Feb 2015

'Fasten your seatbelts – Google’s driverless car is worth trillions [1]'. 'A Scenario: The end of auto insurance [2]'. How the Autonomous Car with Change the World and Upend Auto Insurance [17]'. All are recent press headlines. Are they for real – can we believe the hype?

This paper suggests that while it may take a couple of decades for the impact of automation to flow through the system, the reduction in premiums and the broader implications to the auto insurance sector are significant. Forward thinking insurance carriers and auto manufacturers will create new opportunities to thrive in this automated environment, while others will see a significant erosion of revenues.

In this paper, we will examine the different levels of driving automation, the technologies that enable automated driver assistance and autonomous driving, and the impact that these technologies may have on the frequency and severity of accidents. We then project the adoption of these technologies across the entire car fleet to determine the likely reductions in claims and expected resulting impact on insurance premiums.

Automated Driver Assistance Systems (ADAS) and autonomous car technologies

The US National Highway Traffic Safety Administration (NHTSA) recently distinguished five levels of maturity for automated driver assistance3. The five levels provide a useful framework for examining the different types of technologies:

•    Level 0: No automation
•    Level 1: Function-specific automation eg, cruise control, automatic braking, and lane keeping
•    Level 2: Combined function automation eg, adaptive cruise control and lane centering
•    Level 3: Limited self-driving automation where the driver cedes control of all safety critical functions under certain traffic or environmental conditions
•    Level 4: Full self-driving automation where the car performs all safety critical functions under all conditions

Most current vehicles are at Level 1. A number of manufactures are introducing Level 2 vehicles with a few experimental Level 3 vehicles. Level 4 vehicles are being operated in test tracks and are not allowed to operate on normal roads.

Vehicle manufacturers and traffic authorities are currently using three main categories of technologies that will enable moving up the five levels:

  • 1. Vehicle automation – In order to assist drivers, various auto manufacturers are deploying a number of in-car technologies, such as forward collision warning, drowsy driver detection, adaptive headlights, lane departure sensing, blind spot assistance, parking assistance and adaptive cruise control. When motorists use some of these automated driver assistance technologies in tandem, self-driving automation can reach Level 2 or even Level 3

  • 2. Vehicle to Infrastructure Communications – This includes not only vehicle automation, but also automating the road infrastructure through road monitoring, smart signals that communicate with cars, and sensors that can detect rain and snow. Such combined automation could lead to more Level 3 automation.

  • 3. Vehicle to Vehicle Communications – Considering the number of cars that are already on the road and the time it will take to replace the existing fleet, automated or partially automated cars will have to co-exist with human drivers. Vehicle to vehicle communications using either in-car technologies or smartphone technologies can help facilitate this transition.

The impact of automated driver assistance technologies on the frequency and severity of accidents

Automated driver assistance technologies’ impact on auto claims and (eventually) premiums depends on a number of factors:

  • Technology impact – Different auto manufacturers implement technologies such as forward collision warning, drowsy driver detection, and adaptive headlights in different ways. As a result, reductions in collision damage could vary depending on the effectiveness of respective implementations.

  • Availability – Depending on manufacturer cost technology, regulatory requirements, and customer adoption, automated technologies may be available as standard or optional features. In addition, auto manufacturers may deploy these technologies on just a few higher end models or on all of their models.

  • Usage – A human driver has to activate some of these technologies, like adaptive cruise control and parking assistance. As a result, even if vehicles feature them, drivers may not use some or even all of the technology at their disposal.

  • Regulatory intervention – Given these technologies’ potential benefits to safety, congestion, and greenhouse gas emissions, the government is likely to mandate their eventual, standard usage. However, it is also likely that it will want to rigorously test them under all conditions before they approve widespread deployment. This could either speed up or slow down adoption of automated technologies. 

  • Penetration – The availability and use of automated technologies will change over time as they mature and drivers become more comfortable using them. The percentage of new cars with these technologies will be a critical factor in their overall impact.

We have modelled the overall impact of these technologies over a 20-year time horizon in three steps:

  • Step 1: Technology impact analysis – In this step, we use the Highway Loss Data Institute’s detailed research2 to estimate the impact of the following automated driver assistance technologies on the frequency and severity of five types of claims: bodily injury liability, collision, personal injury protection, comprehensive claims and property damage liability.

    -    Forward collision warning
    -    Drowsy driver detection and warning
    -    Adaptive headlight
    -    Lane departure
    -    Blind spot assist
    -    Voice activated systems
    -    Adaptive cruise control
    -    Parking assistance
    -    Back-up protection
    -    Curve assist
    -    Night vision

  • Step 2: Adoption projection – Having determined the impact of the specific technologies on the four types of claims we estimate their adoption – which includes the availability and usage – over the next 20 years.

  • Step 3: Loss reduction estimation – In order to determine the net reduction of losses across the five major categories of claims, we use the assumptions of the adoption of automated technologies and how long it will take to replace older vehicles.

The model is sensitive to the assumptions we make along these three steps. In particular, the key assumptions that drive the overall results are:

  • 1. Technology impact – Based on our analysis of all the technologies we describe above, the reduction in losses include bodily injury (-15%), collision (-6%), comprehensive (0%), property damage and protection (-14%), and personal injury protection (-10%). This is an average we base on different technologies that different auto manufacturers deploy4, 5, 6, 7, 89. As technologies improve and manufacturers learn from their own and other manufacturers’ experiences, these impacts are very likely to change.

  • 2. Availability and adoption – Historical analysis shows a fifteen-year span between initial introduction of a new technology and 95 percent new vehicle availability. In addition, we assume that it takes an additional 15 years (or 30 years total) to reach 95 percent of all vehicle availability. If some of these technologies demonstrably prove to contribute to passenger safety10, 11, 12, , then regulatory mandate could accelerate their widespread availability. In addition, the adoption is likely to come in multiple waves as the different technologies are piloted, tested, deployed as optional and finally available as standard16. Frost & Sullivan estimates around 3.2 million semi-automated, highly-automated, and fully-automated new vehicles in North America and around 3 million in Europe as part of the third wave of shipments16.

  • 3. Baseline – We assume a linear projection of losses for the baseline based on projecting vehicle miles driven and loss experience from 2009-2013. This projection suggests that total losses would grow to USD 83 billion by 2025 and USD 101 billion by 2035 if new technologies have no impact.

Based on the overall assumptions and the analysis, we estimate a reduction of losses of around 10 percent for the US auto market by 2025 and 20 percent by 2035. We estimate the net baseline projected losses without driver assistance technologies to be USD 83 billion by 2025, and USD 76 billion with driver assistance technologies. By 2035 we project losses without driver assistance at USD 101 billion, and USD 80 billion with driver assist technologies. Figure 1 shows the total projected losses for US auto insurance by 2025, with and without automated driver assistance technologies.

US+Projected+Losses+-+Auto+Insurance+

We believe that these estimates are conservative, and if we relax some of our assumptions of the rate of availability, pace of adoption, and impact of technology on losses, then we estimate even greater loss reductions. For example, Thatcham research20 estimates that 80 percent of all crashes in UK occur at a speed of less than 25 km/h. ADAS focused on safety systems at lower speeds (eg, forward collision with automatic breaking, emergency brake assistance) can result in 208,000 fewer crashes, 158,000 fewer injuries, and 52,000 mitigated injuries, to reduce repaid and whiplash compensation to EUR 1.8 billion.

Changes to regulations are already underway. The 1968 Vienna Convention on Road Traffic requires a human driver to be present in a moving vehicle and to have control over the vehicle at all times is being amended18, 19. The Working Party on Road Traffic Safety of the UN Economic Commission for Europe is working on the draft amendments to the Vienna Convention that is expected to be passed and adopted by all nations over the next couple of years. That would remove some of the regulatory barriers and hasten the adoption of ADAS.

Future scenarios

Although current technology and legislation are still very much works in progress, there are strong forces that do stand to reshape the sector, including shifts to new types of coverage, alternative distribution channels, and redefined customer segments.

We envision four highly possible evolutionary changes to the personal auto insurance industry's products, distribution, and customers, as well as one more truly transformative change that would significantly affect the shape and size of the industry as we know it.

  • Risk shifting – Automated driver assistance technologies, such as forward collision warning and drowsy driver warning, will increasingly shift the risk of driver error to the risk of mechanical malfunction15. This would shift liability to manufacturers and result in a new form of auto insurance that could be packaged with cars that rely on these technologies. In turn, this would shift the key buyer from the end-consumer to the manufacturer, and fundamentally change the entire value chain, from product definition to pricing, marketing, distribution, underwriting, service, and claims. If carriers decide to market this coverage to consumers, then they would do so either at the point of sale, or perhaps try to increase market share by co-marketing with the manufacturer and/or dealer. Risk shifting offers opportunities for auto insurers to gain market share by doing deals with auto manufacturers, but also a threat to companies that fail to capitalize on these opportunities.

  • Risk sharing – Smartphone apps and social networking have already started playing a role in collision reduction. In addition, the dramatic rise in social networking has enabled individuals to develop new affinities wherein people with similar attitudes, interests, and behaviors can pool resources to share risk and lower overall costs. For example, there are new carriers that combine social networking with insurance by connecting customers to form insurance networks that promise significantly lower premiums. These carriers claim that their models allow insurers to access new customers virally, decrease process costs, and reduce claim ratios. While this represents the potential for lower rates for more people, it also could make insurance more affordable for many and therefore lead to premium growth.

  • Risk slicing: Self-driving mode – In the next five to ten years we are likely to see more cars with a self-driving mode. Drivers will be shifting between hands-on and hands-off-driving depending on conditions. This will result in different risk profiles for a single trip and also different liabilities – driver liability in the hands-on mode and product liability in the self-driving or hands-off mode. This type of risk slicing offers a number of interesting pricing options for auto insurers. Similar to usage based or mileage based insurance that telematics- driven auto insurers offer, we could see insurance premiums priced differently based on the mode of driving.

  • Risk slicing: Car sharing – An alternative type of risk slicing occurs with the growing trend of car sharing. Urban living and the increasing availability of automotive time-sharing suggests a future in which premiums move from 24-hour asset coverage to a pay-per-use model. Over 80 percent of the U.S. and over 50 percent of the global population is considered urban; understandably, car sharing is rapidly growing17. According to a Frost & Sullivan research estimate that Forbes13 reported in March 2012, the global car sharing market could exceed USD10 billion by 2020, and the North American car sharing market alone could surpass 4.4 million members and USD 3 billion by 2016. In Europe the number of member will rise to 15 million by 202014. As a result, an increasing number of low-frequency drivers is likely to mean at least some reduction in individual premiums. However, this scenario does not necessarily represent only lost premiums. Most of the people who do not choose to own cars will need to rent them at least occasionally; accordingly, car sharing can expand the market for alternative buyers of insurance.

  • Risk reduction and elimination – Unlike the above scenarios that represent significant change but not necessarily extreme disruption to the insurance industry, driverless cars or autonomous cars (Level 4) equipped with the latest awareness technologies could completely change the industry as we know it. Google, Inc.'s auto research investments are hastening the eventual, widespread availability of driverless cars. Google’s driverless, laser-equipped vehicles have logged over 700,000 miles without an accident; moreover, the company has begun investing in the research and development that initially sets and then drives down the costs of new technologies17. Driverless cars are now legal in California, Nevada, Michigan and Florida in US. Google estimates that the technology can reduce traffic accidents by 90 percent, reduce number of cars by 90 percent, and reduce wasted commute time and energy by 90 percent resulting in savings of USD 2 trillion per year to the US economy1.

Implications

The widespread adoption of ADAS and eventually autonomous cars is not a matter of if it will happen but when. On the road to fully autonomous cars are a number of risks and opportunities for auto insurers. Ignoring them or not taking decisive actions could prove fatal. Some of the ways to turn ADAS adoption into an opportunity include:

  • Product innovation – Usage-based, driving mode-based, and trip-based insurance using telematics devices and ADAS offers insurers new product innovation opportunities. Insurers who are able to unbundle auto insurance and re-bundle it in new ways to target emerging urban and casual drivers, as well as self-driving car drivers.

  • Distribution innovation – Rise of affinity groups, car sharing groups, and vehicle manufacturers who want to package auto insurance with autonomous vehicles can offer new distribution channels to auto insurers. Disruptive players who focus primarily these segments can adopt a B2B distribution channel directly with auto manufacturers or their dealers. These players would have a fundamentally different business model and could progressively capture market share as the proportion of autonomous cars increase.

  • Service innovation – As the need for protection decreases, insurers can play the central role of aggregating information and entertainment needs. Auto manufacturers, online or mobile service providers, telecommunication providers, and information providers are all vying for leadership in in-car infotainment services. Insurers with trusted brands can re-orient themselves as service providers.

  • Claims innovation – The biggest impact of ADAS and autonomous cars will be on safety and the prevention or reduction of accidents. Insurers who approach insureds with these types of cars as a separate segment and handle claims based on on-board diagnostics and analytics will use fundamentally different economics for claims handling and the legal expenses associated with claims. Such auto claims settlement will increase claims satisfaction and reduce litigation costs.

Whatever the future holds, the automotive insurance business is going to change. Despite some doomsday predictions for the industry, there are opportunities for insurers to develop innovative new products, alternative distribution approaches, and new customer segments which can help them thrive, not just survive. The carriers that can think creatively about new markets and potentially drastic changes to automotive technology and ownership will be the ones who are most likely to successfully navigate the path to the future.

Bibliography

1. Fasten your Seatbelts: Google’s Driverless Car is Worth Trillions (Part 1). Chunka Mui. January 22, 2013.
2. A Scenario: The end of auto insurance – What happens when there are (almost) no car accidents? Donald Light, Celent Report. May 8, 2012
3. Preliminary statement of policy concerning automated vehicles. National Highway Traffic Safety Administration, 2013.
4. Mercedes-Benz collision avoidance features: initial results. Highway Loss Data Institute Bulletin. Vol. 29, No. 7: April 2012.
5. Honda Accord collision avoidance features: initial results. Highway Loss Data Institute Bulletin. Vol. 31, No. 2: April 2014.
6. Volvo collision avoidance features: initial results. Highway Loss Data Institute Bulletin. Vol. 29, No. 5: April 2012.
7. Acura collision avoidance features: initial results. Highway Loss Data Institute Bulletin. Vol. 28, No. 21: December 2011.
8. Buick collision avoidance features: initial results. Highway Loss Data Institute Bulletin. Vol. 28, No. 22: December 2011.
9. Mazda collision avoidance features: initial results. Highway Loss Data Institute Bulletin. Vol. 28, No. 13: December 2011.
10. Predicted availability of safety features on registered vehicles – an update. Highway Loss Data Institute Bulletin. Vol. 31, No. 15: September 2014.
11. Estimated time of arrival: New safety features take 3 decades to spread through vehicle fleet. Insurance Institute for Highway Safety Loss Data Institute Bulletin. Vol. 47, No. 1: January 2012.
12. V2V: Cars Communicating to Prevent Crashes, Deaths, Injuries.  DOT : February 2014.
13. Zipcar fuelled up for $22 Run as Business Model Matures. Forbes. March 20, 2012.
14. Growing Awareness of Peer-to-Peer Carsharing will Boost Carsharing Rentals in Less Populated Areas in Europe. Frost & Sullivan. August 22, 2012.
15. The Reshaping of Auto Insurance. Marik Brockman and Anand Rao. Insurance and Technology. January 8, 2013.
16. From Vehicle Automation to Autonomous Driving: The Big Leap. Prana Natarajan, Frost & Sullivan. Proceedings of the The Autonomous Car: Risks and Opportunities for the Re/Insurance Industry. September 2014.
17. How the Autonomous Car will Change the World and Upend Auto Insurance. Brad Templeton, Singularity University. Proceedings of the The Autonomous Car: Risks and Opportunities for the Re/Insurance Industry. September 2014.
18. Regulatory Challenges for the Introduction of Automated Driving: Road Traffic Law. Miodrag Pesut. UN Economic Commission for Europe. Proceedings of the The Autonomous Car: Risks and Opportunities for the Re/Insurance Industry. September 2014.
19. The Legal Framework for Autonomous Driving: Data Privacy and Product Liability. Eric Hilgendorf. Proceedings of the The Autonomous Car: Risks and Opportunities for the Re/Insurance Industry. September 2014.
20. Understanding Technological Advances in Vehicle Safety to Reduce Claims Costs in the Future. Matthew Avery. Thatcham Research. 2014.

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1.       Baseline We assume a linear projection of losses for the baseline based on projecting vehicle miles driven and loss experience from 2009-2013. This projection suggests that total losses would grow to USD83 billion by 2025 and USD101 billion by 2035 if new technologies have no impact.

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Authors

Anand Rao

Leader Insurance Analytics and Partner, Pricewaterhouse Coopers

Anand Rao is a Partner in PwC’s Advisory practice, with over 25 years of experience in the industry. He leads the Insurance Analytics practice and is the Innovation Lead for the US firm’s Analytics Group. He is also the co-lead for the global Project Blue, the Future of Insurance research. In these roles, Mr Rao is responsible for a team of practitioners who work with C-level executives at some of the world’s largest organisations, advising them on a range of topics including global growth strategies, marketing, sales, distribution and digital strategies, behavioural economics and customer experience, and statistical and computational analytics. As the Innovation Lead for the Analytics Group he is responsible for research and commercial relationships with academic institutions and start-ups focused on new and innovative big data and analytic techniques. With his PhD and research career in computer science and artificial intelligence and his subsequent experience in management consulting, he brings business domain knowledge, statistical, and computational analytics to generate unique insights into the theory and practice of ‘data science’.

Scott Fullman

PwC Insurance Advisory

Scott Fullman is a Manager in PwC’s Advisory practice, and member of the Analytics Innovation group. With more than seven years of consulting experience, he has focused primarily on projects helping clients understand and unlock value from data.  Through his work with the Analytics Innovation team, he focuses on exploring and proving the commercial and technical feasibility of emerging concepts and techniques. Mr Fullman has also led a number of international work streams, with experience on four continents. Previous to working as a consultant, he was a Product Manager with a software development firm focused on mobile applications, and also worked for a leading mobile device manufacturer in their research and development group. Mr Fullman holds a Bachelor of Science in Engineering from the University of Illinois Urbana-Champaign.

Balaji Jayakumar

PwC Insurance Advisory

Balaji Jayakumar is a Manager in PwC’s Insurance Advisory practice with over 12 years of experience. He specializes in advising financial services clients (P&C, Life and Reinsurance) on their business and technology transformation. Over his career, he has consistently provided insights on how carriers can get the maximum leverage from their technology investments by aligning the business and technology solutions. Mr Jayakumar has frequently done this in areas with carriers that operate in complex multi-line and multi-channel business environments. He has worked in strategy through execution of large core systems transformation programs involving Policy, Billing and Claims. He brings a unique blend of technology and business skills that maximizes value from major business and technology transformations. Mr Jayakumar holds a bachelor in electrical engineering. He also holds CPCU and FLMI Designations.

Spencer Allee

PwC Analytics Innovation

Spencer Allee is a Senior Associate in PwC’s Analytics practice, specializing in Analytics Innovation and Insurance Analytics. He has experience applying simulation modeling, machine learning, and network analysis to business problems across the value chain, including marketing, sales, distribution, service, and support. He helps manage PwC’s Analytics Innovation Accelerator, an incubator-style group within PwC Analytics that rapidly builds and tests prototypes of new analytics techniques, and he helps coordinate PwC’s Insurance Information Management and Analytics group. Prior to PwC, Mr Allee received a BA of Economics with Distinction from Yale University.

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