Big data in insurance

Insurance companies show a keen interest in Big Data that transforms the industry. Researchers from the University of Zurich and the University of Applied Sciences of the Grisons in collaboration with the reinsurer Swiss Re have analysed the ethical and legal challenges of using Big Data in insurance.

  • Portrait / project description (completed research project)

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    Within 30 months, an interdisciplinary team of researchers from the University of Zurich and the University of Applied Sciences of the Grisons – in collaboration with experts from Swiss Re – investigated the ethical, legal and societal aspects of using Big Data in private insurance. The team of experts in ethics, law, management science, psychology and sociology created recommendations that directly emerge from the team’s research. In this way, they aim to complement existing codices with proposals that are of relevance for the insurance sector and that have a grounding in empirical and theoretical insights gained in this project.

  • Background

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    Since the inception of the insurance industry, accurate and relevant data has been crucial for risk-based calculations. Insurance companies already use many applications of Big Data analytics, such as mobility mining in car insurance, personal profiling for fraud risk rating, or self-measurement in health insurance. Meanwhile, insurance companies aim to balance solidarity, namely the idea of sharing risks individuals may face due to their personal background and lifestyle, with fair insurance policies for the individual. The insurance industry is thus a paradigmatic case for understanding the societal acceptability of Big Data, and for analysing how privacy and insurance laws are balanced with the ad-vantages of many Big Data applications.

  • Aim

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    The goals of the project were (1) to identify the ethical and legal challenges of Big Data applications in the insurance industry, (2) to detect which values customers see as being threatened by digital exposure with a focus on privacy, fairness and solidarity, (3) to assess to what extent the designers of Big Data applications are sensitive towards these issues, and (4) to propose recommendations to meet these challenges.

  • Relevance/application

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    The findings allow policymakers and authorities to ad-dress the Big Data challenges facing insurers. Our recommendations could help to develop a form of self-regulation such that the insurance community can recognise ethically sensitive applications early on and either adapt them accordingly or opt not to develop them. The project’s bottom line is to pave the way for Big Data innovations, which are both ethically justified and le-gally foreseeable.

  • Results

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    Using literature reviews, expert interviews, media analysis, comparative legal analysis (Switzerland and California), surveys and stakeholder workshops, the following results have been achieved:

    • The media analysis revealed that the overall public discourse on Big Data is opportunity-oriented while simultaneously considering the risks of the applications; in Switzerland more than in the USA.
    • The comparative legal analysis revealed that Swiss insurance law does not limit the personalisation of private insurance and that data protection law is not the suitable body of law to determine if and to what extent insurance companies should be allowed to personalise their offers.
    • The ethical analysis revealed that the debate in ethics is shifting away from privacy-related aspects of collecting Big Data to the use of such data in machine learning. Frameworks for “fairness by design” may be required to decrease reputation risks when using machine learning in insurance.
    • The survey indicated that people show resistance to data use in insurance products as soon as the data seems to be unrelated to the object of insurance; this resistance is higher when the values fairness, privacy and solidarity are considered important for the people.
  • Original title

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    Between Solidarity and Personalization – Dealing with Ethical and Legal Big Data Challenges in the Insurance Industry