The uncertain element 189
them. For insurers, trust is a requirement for transactions, which are usually understood as an assumed aspect of the customer relationship. Our
research suggests, however, that rather than being a given, trust needs to
be continuously performed, situated, and embedded in everyday practices
(Lobo-Guerrero 2013; Tranter & Booth 2019). In the context of behavioural
insurance, it is particularly contested, as customers evaluate the degrees of
trust and the overall dependability of data practices; mistrust towards the
overall data ecosystem could affect the insurance policies’ perceived reliability (Steedman et al. 2020).
Behaviour-based insurance is voluntary and competes with regular products in the private insurance market. Thus, consumers can choose whether
to purchase a behavioural policy and submit themselves to data collection.
Unlike in the world of social media, for instance, where people have entered
into firm data relations, in the realm of insurance they are still considering
the harms and benefits of a possible data relationship now and in the future.
As Langdon Winner (1980, p. 127) argues, ‘the greatest latitude of choice
exists the very first time a particular instrument, system, or technique is
introduced.’ Below, we demonstrate the ongoing negotiations that people
participate in to make sense of the data relationship with the insurance
company, as it has not (yet) become intertwined with their lives; it is still
easier for most people to hesitate and refuse to give up their data.
In the following sections, we first introduce our research site and methodology. Then we discuss our findings in three sections: firstly, we analyse customers’ reasons for adopting and using a behaviour-based policy. Secondly,
we look at how people make sense of the policies’ trade-offs and what makes
a ‘good deal.’ Finally, we discuss the doubt, hesitation, and uncertainty that
new policies raise. We conclude by arguing that uncertainties related to
the behavioural policies’ data practices undermine their trustworthiness.
Insurers, thus, need to deal with this uncertainty if they want to include
‘lively’ digital data in their operations.
Research methodology
Research site and focus
Our case study examines two Finnish behaviour-based life insurance policies, introduced to the market in the latter part of the 2010s by insurers we
anonymise as Company X and Company Z. In Finland, citizens are provided universal health care at a very low cost and, if exposed to economic
vulnerability, a decent basic income. Thus, private health and life insurance policies are often seen as a form of ‘extra security’ that ‘supplement’
the structures provided by the welfare state (Lehtonen 2014; Lehtonen &
Liukko 2010). The Finnish insurance market is highly regulated as national
laws, the Finnish Financial Supervisory Authority (FIN-FSA), and EU
directives set limits for industry operations. Especially the GDPR restricts
them. For insurers, trust is a requirement for transactions, which are usually understood as an assumed aspect of the customer relationship. Our
research suggests, however, that rather than being a given, trust needs to
be continuously performed, situated, and embedded in everyday practices
(Lobo-Guerrero 2013; Tranter & Booth 2019). In the context of behavioural
insurance, it is particularly contested, as customers evaluate the degrees of
trust and the overall dependability of data practices; mistrust towards the
overall data ecosystem could affect the insurance policies’ perceived reliability (Steedman et al. 2020).
Behaviour-based insurance is voluntary and competes with regular products in the private insurance market. Thus, consumers can choose whether
to purchase a behavioural policy and submit themselves to data collection.
Unlike in the world of social media, for instance, where people have entered
into firm data relations, in the realm of insurance they are still considering
the harms and benefits of a possible data relationship now and in the future.
As Langdon Winner (1980, p. 127) argues, ‘the greatest latitude of choice
exists the very first time a particular instrument, system, or technique is
introduced.’ Below, we demonstrate the ongoing negotiations that people
participate in to make sense of the data relationship with the insurance
company, as it has not (yet) become intertwined with their lives; it is still
easier for most people to hesitate and refuse to give up their data.
In the following sections, we first introduce our research site and methodology. Then we discuss our findings in three sections: firstly, we analyse customers’ reasons for adopting and using a behaviour-based policy. Secondly,
we look at how people make sense of the policies’ trade-offs and what makes
a ‘good deal.’ Finally, we discuss the doubt, hesitation, and uncertainty that
new policies raise. We conclude by arguing that uncertainties related to
the behavioural policies’ data practices undermine their trustworthiness.
Insurers, thus, need to deal with this uncertainty if they want to include
‘lively’ digital data in their operations.
Research methodology
Research site and focus
Our case study examines two Finnish behaviour-based life insurance policies, introduced to the market in the latter part of the 2010s by insurers we
anonymise as Company X and Company Z. In Finland, citizens are provided universal health care at a very low cost and, if exposed to economic
vulnerability, a decent basic income. Thus, private health and life insurance policies are often seen as a form of ‘extra security’ that ‘supplement’
the structures provided by the welfare state (Lehtonen 2014; Lehtonen &
Liukko 2010). The Finnish insurance market is highly regulated as national
laws, the Finnish Financial Supervisory Authority (FIN-FSA), and EU
directives set limits for industry operations. Especially the GDPR restricts