Editorial: The personalisation of insurance: Data,behaviour and innovation

Data, behaviour and innovation in insurance practice (on selling Lemonade)

To make the stakes clearer, consider how one of the better known insurtech brands positions the differences between its business model and those of traditional insurers. The US-based company Lemonade Inc. offers home, renters and more recently, pet insurance and is one of a handful of insurtech disrupters to have achieved unicorn status. The company was launched on the stock market with an Initial Public Offering (IPO) in July 2020. ⁵ In the publicity surrounding the IPO, the founders made much of the well-rehearsed claim that the tech industries’ facility with data has now far surpassed that of insurers, allowing insurtech companies to take a completely different approach. In a promotional video released on YouTube just before the launch, co-founder Daniel Schreiber characterised this as a microscopic approach to risk:

At twenty data points much of humanity looks alike so you price and underwrite large numbers of people as if they are a uniform, monolithic group but we found that the data we were gathering, that hundred X digital zoom we have, it showed us that groups our competitors seemed to consider to be monolithic were actually made up of predictable subgroups with over 600 percent variation in their likelihood to file a claim. ⁶

Since the 19th century the profitability of insurance has derived substantially from investment rather than premium income (Alborn, 2002; Ericson et al., 2000; McFall, 2014; Van der Heide, 2019a). The pattern shifts from sector to sector and over time but until relatively recently insurers were effectively cushioned from variations in premium income by fixed asset investments (Lex, 2020). The decline in investment returns since the 1990s has made insurance a more competitive market and increased the importance of premium income and pricing. This has incentivised firms to search for

more sophisticated BDA-driven pricing models in order to optimize the profits with the help of the new possibilities offered by technological developments and new data sources [and] enabled a more granular segmentation of risks, increasing the effectiveness of risk selection, and allowing more risk-based pricing. (EIOPA, 2019: 29)

First, there is little clarity about what sorts of data, and what sorts of behaviour are in play in discussions of how BDA permits more granular, personalised underwriting. There is a particular tendency to focus, almost exclusively, on telematic self-tracking data, perhaps because it is both very new and very visible. But it is only one of a number of Big Data sources that insurance undertakings use. According to a report by the UK’s Financial Conduct Authority (FCA, 2016), these sources also include proprietary data (e.g. data from connected companies such as personal data of products purchased or loyalty cards); third party data (e.g. aggregated search engine data including credit checks, license details, claims discount databases, price comparison website quote); social media data and connected device data. This means that insurers have a far, far greater range of ‘behavioural’ data sources than connected devices to draw upon and even connected device data is more nuanced in their application than the idea of behavioural pricing suggests (see Jeanningros and McFall, 2020; Meyers, 2018; Meyers and Hoyweghen, 2020). Credit information, for example, has been used to price motor and home insurance for several years despite its potential for proxy discrimination (Kiviat, 2019; Prince and Schwarcz, 2020). The scope for discrimination has increased dramatically over the last decade with the incorporation of ‘alternate’ or ‘external’ customer data, to profile price sensitivity and risk attitude and has attracted scant public attention. This includes a vast and novel range of unstructured data, as Prince and Schwarcz explain:

Traditionally, firms differentiated among customers, employees, and others based on a limited amount of data that they directly collected. In recent years, however, firms have increasingly come to rely on data secured from a broad number of external sources. These data frequently involve online actions, such as “transactions, email, video, images, clickstream, logs, search queries health records, and social networking interactions ….”⁵⁴ But firms rely on data that increasingly also extends to actions in the physical world, which are measured by “sensors deployed in infrastructure such as communications networks, electric grids, global positioning satellites, roads and bridges, as well as in homes, clothing, and mobile phones.” (2020: 1273)

BDA, IoT connected devices and AI alter the range of what it is technically possible for insurers to do. The emergence of telematics and self-tracking product features in the motor, health and life sectors described in this issue, showcase the role of behavioural data in personalising insurance. They also demonstrate that while technological innovation may stretch, expand and transform the technical classification of risk, this has to be accommodated within the commercial and regulatory practices of insurance. For insurance to be commercial, risk has to be transformed in an ongoing orchestration of legally and technically calculable processes and sentimentally appealing products (McFall, 2014).

Lemonade Inc. provides a nice worked example of what that involves. Lemonade’s choice of brand name is itself revealing. Historically, insurance companies have chosen names to help solve marketing problems. Initially, that meant reassuring customers that however speculative the insurance business seemed, it was financially solid, safe and respectable with names like the Prudential, Equitable, Provident, Perpetual and Amicable. After decades of consolidation, the giant surviving companies chose initials and abstract portmanteaus such as AXA, AVIVA, Manulife or AIG, in an attempt to retain something of their histories in more pronounceable and memorable names. Insurtechs have adopted distinct naming conventions calculated to break into the low brand differentiation of the major incumbent insurers with quirkier, friendlier and more human names, for example Oscar, Brolly, Ladder, Clover and Rhino. ‘Lemonade’ fits the trend but adds an additional knowing wink to George Akerlof’s classic formulation of adverse selection, the dynamic whereby high-risk individuals (‘lemons’) are motivated to buy more insurance than low-risk individuals (‘peaches’) when confronted with the same premium (Akerlof, 1970). Adverse selection, or the lemon problem, means that insurance companies cannot easily differentiate between low-risk and high-risk buyers:

Some insurance buyers are low-risk “peaches” and other insurance buyers are high-risk “lemons.” In many cases the insurance buyers have at least some sense of whether they are lemons or peaches. If the insurance company can tell the difference between lemons and peaches, it will charge the peaches a peach price and the lemons a lemon price consistent with actuarial fairness, and the market will work efficiently … If insurance companies are not able to tell the difference between lemons and peaches, however, or if they are prevented from charging different prices, then they will have to charge all of the buyers the same price. (Baker, 2010: 1609–1610)

Too many data controversies in all the wrong places

To paraphrase Donna Haraway (2016), it matters what matters we make trouble with. One of the problems with the attention paid to self-tracking is that it makes insurance look more straightforward and more precise than it is. It encourages us to continue to neglect the more confusing and difficult prospects raised by datafication in an industry that was poorly understood anyway. Insurance’s unobtrusiveness means that now, when the data controversies surrounding surveillance and extraction have started to touch it, we don’t have a specific vocabulary or grammar to assess what’s happening. Instead we have formulations made from the perspective of data controversies in general. Cathy O’Neil’s summoning of insurance as an instance of the socially destructive force of data science fits this mould precisely.

Now, with the evolution of data science and network computers, insurance is facing fundamental change. With ever more information available – including the data from our genomes, the patterns of our sleep, exercise, and diet, and the proficiency of our driving – insurers will increasingly calculate risk for the individual and free themselves from the generalities of the larger pool. (O’Neil, 2016: 164)

Confusion is exacerbated by the way actors involved in the insurance technology and allied industries talk about themselves and the way critics respond. In the overlapping languages of consultancy, research, trend forecasting, investment and funding, scenarios of intensely surveillant, personalised insurance use cases are presented as the ubiquitous, nearly present, already unavoidable, now. There is an economy to this of course – insurtech actors have a vested interest in disruption narratives. Curiously, insurtech’s drummers and its critics often talk in rhythm and draw on the same sources to document how the tech works. Cory Doctorow (2020) recently remarked that the critique of surveillance capitalism presumes that ‘everything Big Tech says about itself is probably a lie’ but makes an exception for the claims that tech makes about itself in its sales literature. There is something about surveillance tech in particular that seems to command both a reasonable dread and a disavowed fascination with being seen (cf. Ruckenstein and Granroth, 2020). Public concern about BDA is not well served by inflating the extent and value of its uses on the basis of tech industry claims while neglecting the hard empirical graft of figuring out what is happening in what Ball and Webster (2020: 2) have called ‘the mid-range of BDA – the mesh of organisations which mediate between the end consumer, the organisational and societal context, and the marketer of products’.

In a different but not entirely unrelated context, Christopher Kelty (2017) describes how new media and internet technologies almost immediately raised questions about ‘participation’, about whether it could be caused, cured, enhanced or damaged by technological development. These troublesome questions, he argues, are endlessly, fruitlessly deliberated since:

we do not really know what participation is that it could be caused or cured by technological development. On one day, participation is the solution to our most practical concerns or even an ethical calling; on the next day it is a containment strategy designed to keep us chillingly in place or to extract data and money from us at every turn. (Kelty, 2017: s77)

We suspect the word insurance suffers from a similar kind of trouble. Insurance is a malleable political rationality, a calculative epistemology, a means of social solidarity, a fantasy of replaceable totality. It is so very mundane and yet so drunkenly various that we find it hard to parse its grammar. At this moment when the proliferation of data and techniques is finally turning public attention towards insurance, we have not got an adequate shared vocabulary or grammar for a publicly framed discussion of what insurance is and how it works. Can systems trained on external data sources classify, cost and price risk more precisely? Does this vary by sector, in motor, property, health, life? If insurance risk is to be granularised is it still insurance or some other kind of financial instrument, a derivative, a future that is being traded? If so, in a pandemic climate and in the face of the millions globally who lack adequate health coverage should we not be talking urgently about alternative mechanisms for healthcare payment, for social, even interspecies, solidarity (Hendrickx and Van Hoyweghen, 2018; Prainsack and Van Hoyweghen, 2020)?

The contributions

There is no single discipline in which scholars of insurance are primarily concentrated. Instead research from across the social sciences, the humanities, law, business and mathematics combine in transdisciplinary insurance studies dedicated to a disparate range of practices. The articles in this special theme reflect that in the varied theoretical perspectives, methods and tools they bring to interrogating the role of data, behaviour and innovation in contemporary insurance. Together, they work towards establishing a ‘grammar’ for the growing public debate on the future of insurance by placing critical questions in the practical context of insurance innovation, insurtech and regulation.

We begin this task with Maiju Tanninen’s account of how contested the technology of behaviour-based insurance is. Tanninen meticulously reviews the way recent developments in behaviour-based insurance have featured in two main streams of literature, Critical Data Studies (CDS) and the social and technical studies (STS) of insurance. She shows how CDS presents personalisation in insurance as an extreme, and yet still exemplary, case of datafication practices anticipating immense and unwanted consequences. STS-inspired studies, on the other hand, focus their attention on how concrete practices of datafication take place and enact practices of personalisation. Tanninen acknowledges the importance of critical voices on insurance, but she questions the shallowness of the portrayal of insurance arguing that CDS relies ‘on the same understandings as the dominant Big Data enthusiastic discourses, ultimately taking insurers’ and tech companies’ visions on the “digital disruption” seriously’ (Tanninen, 2020: 10). She makes the case that more nuanced, substantive and empirically grounded research on both how insurers approach personalisation, and how users experience the products, is required to mitigate the hype.

The next two articles explore how insurers assess risk and translate it into the products and prices offered to consumers in the two sectors most closely associated with personalisation: health insurance and motor insurance. In ‘The Value of Sharing’, Hugo Jeanningros and Liz McFall describe how the South African company, Discovery International, built their Vitality brand explicitly around the idea of supporting healthy behaviour. The Vitality case, they argue, demonstrates the intricate connections between the technical practices and sentimental appeals at work in how insurance companies value risk and how their customers value insurance policies. Behaviour is Vitality’s core brand value and its policies provide incentives to customers to meet behavioural targets, share their data with the company and share their progress on social media. ‘The value of sharing’ for Discovery is in choreographing the perpetual dance between the technical valuation of risk, the commercial performance of the business and sentimental appeal of the brand.

Parallel twists and turns can be seen in the case study of experimental behaviour-based personalisation practices in car insurance presented by Gert Meyers and Ine Van Hoyweghen. They show how the regulatory requirements imposed on insurance companies create a Catch-22 situation. Currently, in order to be allowed to use BDA, insurers have to provide actuarial evidence on the relation between, for example, driving style and the experience of loss. Yet without collecting these data in real-time situations, it is impossible to build this evidence base. This is why insurers currently rely on the use of ‘economic experiments’ (Tironi and Laurent, 2014). Based on interviews and documentary analyses, Meyers and Van Hoyweghen outline how the in-vivo experiment was set-up, which interventions and manipulations were imposed to make the experiment successful, and how the study was evaluated by the actors. Following Austin (1962), they argue that economic experimentation ‘in the wild’ should not (only) be evaluated by its capacity to produce truth statements, but also on its (un)intended positive (happy) or negative (unhappy) consequences.

The final two articles of this theme issue reflect, in rather different ways, on how BDA are transforming what Francois Ewald (1991, 2012) called ‘insurantial imaginaries’. Laurence Barry and Arthur Charpentier (2020) consider how pricing practices in motor insurance are changing with the use of personalised analytics. They start by raising the question of whether we are indeed in the midst of a ‘new epistemological turn in data analysis’. Such a turn, they argue, would shift insurance knowledge away from its basis in statistics to create homogeneity through the classification of people into risk groups, towards a basis in analytics to generate differences (cf. Mau, 2019). This endangers the principle of the mutualisation of risk which is the historical and epistemological core of insurance technique. Barry and Charpentier explain that as most actuarial scientists recommend the addition of new variables to existing (statistical) risk models, the epistemological break has not – at least not yet – transformed risk practices across the industry. For them, although risk practices are becoming more granular over time the underlying mechanisms of insurance will continue to be based on the constitution of statistical risk groups. There is an important distinction to be drawn between imaginaries and sentiments mobilised around personalisation, whether by insurance actors or critics, and the calculative devices, the techniques, currently used to assess risks.

In a related vein, Alberto Cevolino and Elena Esposito (2020) elaborate on the social consequences of algorithmic prediction in insurance that might ensue in a move from statistical risk pools to predictive personalised profiles. They assemble a corpus of actuarial, scientific and commercial literature and use it to distil a nuanced imaginary of what a fully realised BDA-driven insurance might involve. They focus specifically on the changes in conceptions on fairness and discrimination that would be entailed in a reconfiguration of risk sharing from ‘pool to profile’. Discrimination based on profiles can seem fairer, or more palatable, than discrimination based on membership of a social group with characteristics that may be predictive but are subject to protections. In a ‘new alliance between actuarial techniques and digital technologies’, Cevolino and Esposito conclude, discrimination may be packaged as a consequence of individual life-style but it would generate classification situations that could affect individual life-chances in ways that are unpredictable.

The papers all share this concern with the far-reaching consequences on social solidarity represented by the steady epistemological turn away from risk pooling. Together, they consider how difficult the tendency towards a ‘risk pool of one’ is for insurance, whether privately or publicly organised, to put into practice. BDA has been positioned as a means of reversing the information asymmetries that structure insurance, allowing the insurance provider to know more about their policyholder as individual risks. But having access to more data is not the same as knowing individual risk. Instead, the pattern and character of asymmetries – the ‘market for lemons’ – is constantly transformed by subtle changes in insurance practice and in the socio-economic, political and regulatory context it takes place within. ¹¹ BDA reshapes the kinds of moral hazards and patterns of adverse selection that are produced in insurance relationships. Tracking the consequences of the market experiments that are happening backstage in the insurance-technology industries requires that researchers pay attention not just to the idea of surveillance but to the practices of insurance.