The impact of connected automated vehicles on the insurance sector: a comprehensive analysis of legal and risk factors

The introduction of connected automated vehicles (CAV) offers significant societal benefits and economic opportunities while similarly posing major challenges to society, businesses, research, and regulatory bodies. In addition to directly affected markets, such as the automobile manufacturing and transportation sectors, insurance is one of the core downstream sectors acutely sensitive to the adoption of this emerging technology. In fact, the adoption of CAV technology has the potential to profoundly affect existing business models of insurers with key triggers arising from changes to liability frameworks, a changing risk landscape and changes of customer interfaces and market structure induced by a shift of societal mobility approaches. Due to the facilitating role of insurers for the introduction of new technology, the strategic implications for this stakeholder have to be understood holistically and proactively to ensure a seamless introduction. Given that insurance as a subject of academic research is interdisciplinary by character and since the strategic implications emerging with CAV technology originate from both legal and risk factors, this thesis provides a multidimensional research approach linking different research disciplines and research methods. Using the current German liability and insurance framework as a case study, this thesis confirms that the methodology to allocate liability based on the strict liability of the vehicle owner is generally compatible with peculiarities of automated driving. However, adjustments to the existing framework are necessary to maintain an adequate level of claimant protection for accidents caused by automated vehicles. In addition, this thesis highlights that an adequate ultimate allocation of liability costs is potentially inhibited because of several barriers that hinder the shift of liability costs to the manufacturer side. This is particularly because the ability and motivation of motor insurers to conduct subrogation claims is negatively affected by a lack of required technical and engineering know-how and because market-wide conduction of subrogation claims would erode the business volume of motor insurance. In addition to legal challenges arising from existing liability and insurance frameworks, this thesis analyses data-driven use cases to present the access to in-vehicle data as another core CAV-related legal question from an insurance-perspective. Finding a status quo where OEMs begin to leverage their superior access to in-vehicle data for the expansion of their own business models, the analysis underlines that the increasing interconnection of modern automobile vehicles will have a significant strategic impact on insurance-related service offerings. However, by analysing this status quo from a business ecosystem perspective, it becomes apparent that taking the role of a physical dominator to extract maximum short-term value might be an obvious but not necessarily successful approach for OEMs on long-term. This is because the shift from a goods-dominant supply-chain perspective to a service-dominant perspective will also need a profound redefinition of OEMs´ supply-chain relationships. This finding supports the resolution of contrasting positions of OEMs and third-party providers and enables an unbiased and ix farsighted approach of regulatory bodies to prevent that inadequate advantages of single actors result in market failure to the detriment of customers. For analysing the potential impacts of CAV technology on insurance-relevant risk-factors, this thesis provides qualitative and semi-quantitative analyses of relevant drivers for motor insurance and automotive product recall risk. Referring to CAV technology´s impact on motor insurance risk exposure, the research concludes that automated driving vehicles indeed have the potential to significantly decrease the number of road accidents caused by human-error. However, as there is insufficient data available about the reliability of highly automated driving systems in real-world applications, reliable quantification of future accident risk exposure is inhibited. Therefore, assumptions of a sharply decreasing accident risk exposure are by no means straightforward nor statistically proven, especially as the provided analysis reveals risk-relevant peculiarities of every single level of automation. In addition, new risks such as the risk of automotive cyber-attacks are likely to emerge with the penetration of CAV technology which, in turn, introduce potential sources of yet unknown catastrophe-alike risk exposures to MTPL insurance. For the analysis of automotive product recall risk, this thesis couples the qualitative assessment of CAV-induced risk drivers from legal and technology-related sources with an analysis of historical product recall data from different product recall databases. With this approach, this thesis finds an increasing risk of product recalls induced by CAV technology, which is triggered by the increasing complexity of vehicle hardware and software and by an increasing legal and reputational risk in the case that CAV technology fails. With the provided multidimensional research approach, this thesis contributes to an improved understanding of legal frameworks regulating CAV technology´s introduction and enables regulatory bodies for a proactive and farsighted adaption of existing legislation. Particularly referring to the improved understanding of liability frameworks, the contribution to existing literature results from the fact that the provided in-depth analysis not only extends on liability law on a detached basis but considers important interdependencies resulting from motor insurance law and from motor insurers´ central role within the liability settlement process. In addition to the contribution to an improved understanding of legal factors, the analysis of CAV technology´s impacts on motor insurance risk characteristics contributes to an improved understanding of CAV technology´s inherent risk-factors. This is particularly useful as existing research and public expectations often seem to be biased and not sufficiently granular in the analysis of idiosyncrasies of single levels of automation. Furthermore, the presented research on CAV technology´s implications to product recall risk contributes to a comprehensive academic discussion of relevant risk-factors and serves as a cornerstone for academic research on a largely unaddressed aspect. From a business perspective, the findings of this thesis not only provide an holistic assessment of the impacts of CAV technology on the insurance sector enabling insurance entities to take proactive strategic measures for adapting existing business models to a probably changing business environment but also support stakeholders on the CAV technology supply side in implementing adequate risk management frameworks to cope with emerging risks exposures such as product liability and product recall.