Actuarial Data Science

Susanne Brindöpke ()
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Susanne Brindöpke: ifb SE

A chapter in The Digital Journey of Banking and Insurance, Volume I, 2021, pp 119-136 from Springer

Abstract: Abstract Insurance companies have always been dependent on reliable projectionsProjections 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, pricingPricing, reservingReserving, 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 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78814-8_8

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

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