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- Convenors:
-
Turo-Kimmo Lehtonen
(Tampere University)
Maiju Tanninen
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- Discussants:
-
Tamar Sharon
(Radboud University)
Gert Meyers
- Format:
- Traditional Open Panel
Short Abstract:
Insurance institutions form a crucial infrastructure in contemporary societies. It is often claimed that artificial intelligence will fundamentally transform the insurance industry. This panel examines to what extent and how new datafication is actually changing insurance practices.
Long Abstract:
Insurance institutions form a crucial infrastructure in contemporary societies, as they pool and distribute risk, produce welfare, and build up trust that backs up economic activity. Within the insurance industry, it has been widely assumed that big data and artificial intelligence will disrupt the business at every operational scale. Through techniques such as data analytics, machine learning, and automated decision-making, insurance is datafied in a new way, which could have far-reaching societal consequences. Yet, the industry’s discourse is often characterized by hype and loose promises – if not mere guesswork – about what might take place in the future. At the same time, social scientific analyses on the theme have often been based more on critical assumptions than knowledge of actual changes in the field. This panel examines to what extent and how digital technologies are actually changing insurance infrastructures and practices. By empirically examining real-world insurance activities, the research presented in the panel will offer a corrective to both over-optimistic industry views and superficial criticism. We are especially interested in work that investigates life insurance, car insurance, climate change-related (re)insurance or cyber insurance. Important societal questions concern the potential individualization of risk, new ways of distributing responsibilities, and, more generally, changes in the collective forms of managing uncertainty.
Accepted papers:
Session 1Bastien Presset (King's College London) Maiju Tanninen
Short abstract:
The implementation of self-tracking in insurance (STi) has ignited debates over solidarity models. Based on interviews with policyholders we reveal how the daily use of STi challenges people's moral stances by personalizing risk and blurring the boundaries of institutionalized solidarity.
Long abstract:
The implementation of behavioural data and algorithmic technologies in insurance has sparked scholarly debate regarding their capability to disrupt the solidarity models of insurance. While the new insurance technologies’ ability to individualize risk has limitations, their features might accentuate individual responsibility and obscure the reference groups that constitute the foundation of insurance solidarity, particularly for policyholders. Yet, little is known of how policyholders themselves experience these insurance schemes. Based on 21 interviews conducted with users of a Swiss self-tracking in insurance (STi) program, we analyse how policyholders enact responsibility and solidarity together with a STi technology. Our findings reveal tensions in users’ daily experiences, highlighting frictions between solidarity as it is currently implemented in health insurance regulation and practises and the form embedded in STi interventions. Some users enthusiastically adopt the individualizing rationale of the technology, valuing self-responsibility and justifying it with the promise of reduced medical costs for everyone. Others struggle between their conviction with the established forms of solidarity and their desire to benefit from the programs. The results suggest that, although STi encounters regulative barriers, its current forms of implementation in users’ daily lives lead to sociomaterial reconfigurations of morals at the microsocial level.
Hugo Jeanningros (Université de technologie de Troyes) Renaud Debailly (Sorbonne University)
Short abstract:
Former cancer patients encounter difficulties in accessing loan insurance. To remedy their insurability issues, France as developed two complementary and somehow contradictory strategies. What is the fairest way to insure former patients? More or less knowledge about their risk level?
Long abstract:
Medical progress regarding the treatment of cancer drastically increased patient’s survival rates. These improvements led to a growing population of living former cancer patients, highlighting new issues, among which insurability related issues.
To remedy their difficulties to access insurance, especially regarding loan insurance, France has developed two strategies. On one side, a reference grid to frame insurance practices with cutting edge epidemiological knowledge. On the basis of these actualized data, insurers commit to adapt their rates and exclusion policies. On the other side, since 2016, the Right to be Forgotten for former cancer patients allows people to not declare their cancer 5 years after the end of their treatments. In addition, the Lemoine act (2022) forbid the use of medical reviews for “short” loans insurance. While the first strategy consists in encouraging the production and use of knowledge, the second one, in an opposite move, lies on the reduction of information available to insurers.
With this proposal, we explore the rationales behind these two complementary/opposite strategies. How does actuarial fairness collide with the legitimacy of former cancer patients to not suffer what they call a “double penalty”? To do so, the paper will build on an ongoing investigation conducted in France. The fieldwork rests on 20 interviews conducted with former patients, and also interviews conducted with insurance professionals, regulators and patient organizations.
With the financial support of the French National CancerInstitute (INCA_15900)
Linda Huber (University of Michigan)
Short abstract:
This presentation shares insights from ongoing ethnographic research on the datafication of the healthcare industry. It argues that the real-time reporting of quality metrics empowers insurers to transfer more risk onto providers and exert greater control over the labor process of care.
Long abstract:
U.S. federal regulations are incrementally shifting healthcare business models away from paying for treatments and tests towards paying for health. However, paying for care outcomes requires intensive processes of datafication: new kinds of data tracking, analysis and reporting. Quality metrics, standardized measures of providers’ adherence to ‘best-practices’ (e.g. using a EMR, doing preventative screenings) and overall performance (e.g., readmissions, mortality rates) play a pivotal role in the shift towards “paying for care.” Increasingly, insurers provide financial incentives for provider organizations to demonstrate good performance on ‘quality metrics’ in an attempt to pay more directly for the health of an overall population, rather than paying for services.
Traditionally, quality metrics are summarized annually through labor-intensive sampling from electronic health records. Increasingly, standardized and granular health data is enabling automated, real-time reporting, transforming quality metrics from a “rear-view mirror” of provider practices to real-time surveillance mechanisms. This shift enables insurers to exert more direct control over providers' adherence to best practices. Drawing from ongoing ethnographic research into the datafication of the U.S. healthcare industry, this presentation explores how the shift towards real-time reporting of digital quality measures may empower insurance companies to exert more direct control over providers and provider organizations. I illustrate this as one example of how datafication is enabling healthcare insurers to transfer the risks of care increasingly onto healthcare providers. Through conversations with the other panelists, I am interested in comparing how this technique of risk-displacement-through-datafication compares to other industries and contexts.
Alexander Gamerdinger (Copenhagen Business School)
Short abstract:
This paper studies how 'data professionals' — data scientists and actuaries within a disability insurer — legitimize their work amidst public controversies of algorithms. It contributes to the classification literature by showing the critical role of professional morality in shaping market dynamics.
Long abstract:
AI has revolutionized the way markets classify and sort individuals, offering substantial economic gains while posing significant societal risks due to unexpected impacts on individuals' life chances. This paper studies how 'data professionals' — data scientists and actuaries within a disability insurer — legitimize their work amidst these challenges. Using an ethnographic observations and interviews, the study explores the moral judgments each professional groups makes in their distinct risk assessment system. Actuaries focus on calculating premiums through traditional risk assessments, while data scientists leverage machine learning to predict claims risk, highlighting a crucial shift toward technological solutions in claims prevention. The findings reveal a stark moral divergence between these groups. Data scientists are outspoken on 'data ethics’ and have designed internal ethical guidelines that stress the necessity for developing fair and accurate models aimed at helping customers in need. In contrast, actuaries remain rather silent on their ethical considerations but adhere to notions of 'actuarial fairness', favoring explainable, simplistic models that group individuals into risk categories, thereby supporting solidarity through pricing. By comparing these professional morals, the paper shows how different moral viewpoints inform the outcomes of market classifications – while data scientists produce personalized risk scores, actuarial work produces group-based classifications. This paper contributes to the classification literature in economic sociology by signaling the critical role of professional morality in shaping market dynamics and societal impacts. It points to the continuing relevance of the professional background of ‘data professionals’ instead of focusing exclusively on the morals of algorithms.
Olli Hasu (Tampere University)
Short abstract:
In the climate finance discourse, automated environmental sensing and machine learning-aided data processing are proposed as an infrastructural opportunity for developing low-cost adaptation instruments. I analyze how insurance companies use environmental data in climate change adaptation mechanisms
Long abstract:
Financial institutions play a central mediatory role in climate change governance. In the climate finance discourse, automated environmental sensing and machine learning aided data processing are proposed as an infrastructural opportunity for developing low-cost adaptation instruments. However, this vision depends on data service companies that develop technological possibilities according to market conditions. In my Ph.D. work, I analyze finance as an interface of climate change governance. I ask how insurance companies transform environmental data into socioeconomic coordination in climate change adaptation mechanisms. In the paper I propose for this panel, I analyze World Bank’s data-based resilience project that outlines data platform markets as a system for structuring the possibilities and limitations for climate risk insurance in the Global South.
To make sense of the relationship between environmental data and climate insurance, I use horizon as a conceptual framework that I separate to two analytical registers. First, horizon refers to the governance of future climate risks as a discursive space surrounding the development of financial instruments and risk management standards. The political economic model of the horizon balances socioeconomic costs against benefits. Second, the climate finance discourse attends to the infrastructural organization of the horizon by analyzing the technological requirements for constructing biophysical systems as an object of governance. The objectification of biophysical factors as governmental constraints and resources is conditioned by available data infrastructure services. Overall, I map out how the relationship between socioeconomic interests and biophysical principles is constructed technologically and economically in the design of financial mechanisms.