Genetics is a major emerging risk and technological trend. There is a social context out of which genetics have arisen and it is important to take that into observation. With rise of big data and rapid technical changes, we are facing a plethora of changing landscapes in insurance and risks are evolving. Genetics is a subset of this wider phenomenon.
The problem is that as emerging risk, there is little if any data available of genetics on policyholders for historical analysis that is usually done in actuarial modeling. Other prospective models like agent based modeling, network models and qualitative profiling of emerging risk might have to be adopted. Historically, since nineteenth century, we socially had the concept of an individual being responsible for all his success and failure. Over time with the rise of welfare state, this trend became one of universal rights and solidarity. With many economic collapses and rise in uncertainties and conflict, we are socially transforming into applying the precautionary principal. This is where data with advanced algorithms like those of machine learning are applied to gain actionable insights even though there are only probabilities and correlations, and no certainties and causation. This is what researchers term ‘data derivatives’ and aside from using genetics as one of the many factors under consideration, this also serves as a surveillance concept that indirectly strengthens the surveillance society where we have the feeling of being under observation all the time and everywhere (Michel Foucault).
Another risk is that risk factors like genetics are treated as objective criteria by insurers whereas they are living realities for the policyholders. As can be seen, the governmentality approach can be seen as very relevant to actuarial practices. It is argued that group differences created by historical processes of domination are demoralized by actuarial representations (as they are for instance in insurance premium setting) it becomes more difficult for disadvantaged groups to generate political power. This is because sensitive stratifications like genetics, gender, credit profiles and pricing elasticities etc are living realities of the people whereas in classification for actuarial ratemaking, they are stripped off their subjectivities and transformed into an objective formal reality. The moral charge carried by these forms of differences are eliminated and so actuarial ratemaking classification “with its de-centered subject, seems to eliminate, in advance, the possibility of identity, of critical self-consciousness and of intersubjectivity (cf. Habermas, 1979). Rather than making people up, actuarial practices unmake them.”
This is not something only relating to social factors. Liability catastrophe can arise from massive infringement of privacy rights of policyholders from holding their genetics data. On another side, consider the rise in cyber-attacks over recent years. A single cyber-attack on insurer leaking policyholders’ genetics data can result in manifestation of mass legal suits. Moreover, actuaries and insurers have faced backlashes from consumers regarding use of gender, pricing elasticity and credit profiles for pricing but genetics can face a worse backlash given how intimate and deeply personal this data is.
Insurers are usually not just risk-averse (just see their investment portfolio compositions!), research shows they are also ambiguity averse. Genetics is a highly evolving trend with little to no proper standardized route and so should it be excluded as consideration for pricing until a minimum standardization arises? Should actuaries, is request higher vigilance and data capturing for policyholders that have adverse genetic traits? Over time, can we hope that such data compilation will help clear the fog of uncertainty and hopefully show us a clearer bigger picture?
We cannot simply shirk off responsibility by implying that we handle genetics directly for risk analysis and are not concerned with its indirect uses and potential applications. Werther concludes that until and unless we consider intention—philosophy, cognitive system, bias, etc.—used in building data, models and expert’s analysis, and implications, we are missing the big picture already. Nietzsche’s point that “the decisive value of an action may lie precisely in what is unintentional in it. … The intention is only a sign and a symptom, something which still needs interpretation, and furthermore a sign which carries too many meanings and, thus, by itself alone means almost nothing” (emphasis added).
In reaching an acceptable framework, perhaps this ancient advise can come in handy:
Impact on risk due to genetics:
- Is it true?
- Is it necessary?
- Is it kind (fair)?
As always, different stakeholders will have different answers and perspectives for these three questions and clearly there is no easy solution and there are complex tradeoffs but that should not deter us for reaching an acceptable framework for a start.
This reflects the broader principles of the Genetics as influencing social policy should also be our working goal, and not just technical considerations.