300050 The Future of Insurance. Social Consequences of "Proactivity" in Future Insurance (S) (WiSe 2019/2020)

Insurance is a pillar of modern society. Its function is not to offer safety – insurers do not offer the usual protections of precautionary measures (such as seat belts for car drivers), and policyholders are not less exposed to the danger of suffering a damage than those who are not insured. On the insurance market, what is traded is rather the uncertainty that everyone faces. The latter is the true object of insurance contracts. This is why modern insurance takes off when people accept the odd idea that uncertainty can be ‘tamed’ through the aggregation of many cases (Daston 1983; Hacking 1990). On the large numbers, in fact, regularities emerge that can be calculated and are therefore to some extent predictable. With the calculus of probability and statistics, insurance companies can thus set the policy premium and have the (mathematical) expectation that, at the end of a certain period, the difference between collected premiums and the indemnity payments is to the advantage of the company – in short, that the company can make a profit.
Digital technologies are changing this situation. Over the last ten years, the insurance industry has undergone a ‘disruptive’ change that could forever transform the way insurance contracts are taken out (Ewald 2012). Typical cases are the use of telemetry-based packages (black boxes) to be installed on vehicles for third-party liability motor insurance, and wearable technologies (e.g. FitBit watch) for health insurance. These technologies produce a large amount of data which are processed by predictive algorithms to estimate the degree of riskiness of the individual policyholder. The insured, in short, receives a score (usually represented by a color), and on the basis of the achieved score, she may have some kind of reward such as a fuel cash-back or a premium discount upon renewal of the policy.
In this workshop, we will explore the social consequences of digital insurance (or connected insurance). We will focus on ‘proactivity’ and the possible effects of the reversal of the information asymmetry that always plagued insurance companies. What happens when the insurance company, instead of operating ‘reactively’ (that is, after an accident has occurred), interferes ‘proactively’ in the life of a policyholder, permanently monitoring her behavior and suggesting corrections for a safer lifestyle? What happens when the insurance company knows much more about the policyholder than the policyholder knows about herself? How could the insurance industry change if policy premiums were proportional to the riskiness of the individual policyholder, i.e., if they were set according to the individual profile rather than according to the average values that ensure mutuality in the pool?

• Participants are invited to prepare a presentation and to contact the teacher in advance for the overall planning of the meetings. This workshop takes place over three days. Bibliography and detailed program of the workshop will be announced at the end of August 2019.

Bibliography

Corlosquet-Habart, Marine/Janssen, Jacques (Eds.) (2018): Big Data for Insurance Companies. London: John Wiley & Sons.

Daston, Lorraine (1983): Rational Individual Versus Laws of Society: From Probability to Statistics. In: Michael Heidelberger et al. (Eds.), Probability Since 1800. Interdisciplinary Studies of Scientific Development. Bielefeld: B. K. Verlag, pp. 7–26.

Daston, Lorraine (1987): The Domestication of Risk: Mathematical Probability and Insurance, 1650-1830. In: Lorenz Krüger et al. (Eds.), The Probabilistic Revolution I. Cambridge (Mass.): The MIT Press, pp. 237–260.

Esposito, Elena (2007): Die Fiktion der wahrscheinlichen Realität. Frankfurt a.M.: Suhrkamp.

Ewald, François (2012): Assurance, prévention, prédiction...dans l’univers du Big Data. Paris: Rapport pour l’Institut Montparnasse.

Frezal, Sylvestre/Barry, Laurence (2019): Fairness in Uncertainty: Some Limits and Misinterpretations of Actuarial Fairness. Journal of Business Ethics, pp. 1–10.

Hacking, Ian (1990): The Taming of Chance. Cambridge: Cambridge University Press.

Luhmann, Niklas (1996): Das Risiko der Versicherung gegen Gefahren. Soziale Welt, 43(3), pp. 273–284.

O’Neil, Cathy (2016): Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy. New York: Broadway Books.

Siegelman, Peter (2014): Information & Equilibrium in Insurance Markets with Big Data. Connecticut Insurance Law Journal, 21(1), pp. 317–338.

Swedloff, Rick (2014): Risk Classification’s Big Data (R)evolution. Connecticut Insurance Law Journal, 21(1), pp. 339–373.