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The impact of artificial intelligence along the insurance value chain and on the insurability of risks
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Abstract Based on a data set of 91 papers and 22 industry studies, we analyse the impact of artificial intelligence on the insurance sector using Porter’s (1985) value chain and Berliner’s (1982) insurability criteria. Additionally, we present future research directions, from both the academic and practitioner points of view. The results illustrate that both cost efficiencies and new revenue streams can be realised, as the insurance business model will shift from loss compensation to loss prediction and prevention. Moreover, we identify two possible developments with respect to the insurability of risks. The first is that the application of artificial intelligence by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing one of the industry’s most inherent problems, namely asymmetric information. The second development is that artificial intelligence might change the risk landscape significantly by transforming some risks from low-severity/high-frequency to high-severity/low-frequency. This requires insurance companies to rethink traditional insurance coverage and design adequate insurance products.
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“Moreover, the application of AI by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing one of the industry's most inherent problems, namely asymmetric information (124).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [124]
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“See Table S2 for an extended version.”
Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions
Olawade, D. B., Wada, O. Z., Ige, A. O., Egbewole, B. I., Olojo, A. S., & Oladapo, B. I. (2024) · Hygiene and Environmental Health Advances · doi:10.1016/j.heha.2024.100114 · Cited by 418
AI-driven business model innovation: A systematic review and research agenda
Jorzik, P., Klein, S. P., Kanbach, D. K., & Kraus, S. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114764 · Cited by 290
Gupta, S., Ghardallou, W., Pandey, D. K., & Sahu, G. P. (2022) · Research in International Business and Finance · doi:10.1016/j.ribaf.2022.101757 · Cited by 181
Metadata
- Authors
- Eling, M., Nuessle, D., & Staubli, J.
- Affiliations
- University of St.Gallen (CH)
- Corresponding author
- Eling, Martin
- ORCID
- Eling, Martin
- Venue
- The Geneva Papers on Risk and Insurance Issues and Practice
- Published
- 2021-02-08
- Volume
- 47
- Issue
- 2
- Pages
- 205-241
- ISSN
- 1018-5895 (ISSN-L)
- 1468-0440
- OpenAlex
- W3126999983
- Cited by
- 156
- References
- 96
- Citations by year
- 2026: 25 · 2025: 38 · 2024: 38 · 2023: 30 · 2022: 17 · 2021: 7
- FWCI
- 17.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The Geneva Papers on Risk and Insurance Issues and Practice (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- Funders
- University of St.Gallen
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Insurance and Financial Risk Management
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, insurance value chain, insurance business models, loss prediction, loss prevention, information asymmetry, risk transformation, insurance products, cost efficiency, income sources
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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