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Actuarial Data Science

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Abstract

Insurance companies have always been dependent on reliable projections and hence on data-driven decisions. As digitalization is progressing in almost every industry, insurance companies may benefit in particular, because they already possess valuable historical data. Not only is process automation, for example in the settlement of claims or the distribution of insurance contracts, worth considering, but the traditional fields of work of actuaries, for example, pricing, reserving, or investment, also offer various applications for machine learning algorithms. This chapter gives an overview of actuarial data science with promising use cases where existing models can be enhanced or even replaced and presents the important prerequisites that need to be taken into account.

Keywords

  • Data Science
  • Actuarial Science
  • Actuary
  • Actuarial Models
  • Fraud
  • Lapse
  • Pricing
  • Reserving
  • Risk Management
  • Solvency II

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  • DOI: 10.1007/978-3-030-78814-8_8
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Notes

  1. 1.

    Machine learning techniques for investment or asset portfolio management are not addressed in this chapter as this has a much wider range of application and is not restricted to actuarial practice.

  2. 2.

    An interesting combination of RPA and machine learning can be found in (RPA—Use Case SPPI see [Gabriel 2021]).

  3. 3.

    https://www.nhcaa.org/resources/health-care-anti-fraud-resources/the-challenge-of-health-care-fraud/.

  4. 4.

    Property and casualty insurance.

  5. 5.

    In GLMs the response variable comes from an exponential family distribution, for example Normal, Exponential, Binomial or Poisson distribution.

  6. 6.

    The ML approaches applied were Extreme Gradient Boosting and Support Vector Machines.

  7. 7.

    Adverse selection is defined as the information asymmetry between the policyholder and the insurance company.

  8. 8.

    See, for example, Society of Actuaries (SOA): https://mort.soa.org/ or the Social Security Administration (SSA): https://www.ssa.gov/OACT/STATS/table4c6.html.

  9. 9.

    For example, EIOPA has established an Expert Group on Digital Ethics in Insurance: https://www.eiopa.europa.eu/content/eiopa-establishes-consultative-expert-group-digital-ethics-insurance_en.

  10. 10.

    For example, the European Actuary Association (EEA) recently hosted the Data Science & Data Ethics e-Conference in June 2020: https://actuarial-academy.com/seminars/seminar?No=E0192.

  11. 11.

    https://inventory.algorithmwatch.org/.

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Correspondence to Susanne Brindöpke .

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Brindöpke, S. (2021). Actuarial Data Science. In: Liermann, V., Stegmann, C. (eds) The Digital Journey of Banking and Insurance, Volume I. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78814-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-78814-8_8

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  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-78813-1

  • Online ISBN: 978-3-030-78814-8

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