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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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Mullins, J., & Iskandarova, S. (2024) · IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) · doi:10.1109/gcaiot63427.2024.10833574 · Cited by 2
“From the historical perspective, insurance companies have primarily focused on leveraging artificial narrow intelligence (weak AI) rather than attempting to replicate human intelligence (strong AI) [2] [3] [4].”
Alva, F. A. C., Fernández, G. I. L., Castillo, Á. L. L. F. M., Puscán, M. W. V., & Yupanqui-Garcia, A. B. (2023) · LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD) · doi:10.18687/leird2023.1.1.203 · Cited by 4
A LITERATURE REVIEW ON DIMENSIONS OF BRAND EQUITY IN THE INSURANCE INDUSTRY OF SAUDI ARABIA
Hassan, M., Hassan, M. U., & Uddin, I. (2022) · Jurnal Syariah · doi:10.22452/syariah.vol29no3.4 · Cited by 3
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“Incorporating ML into premium calculation procedures enables insurers to use predictive analytics to increase pricing schemes' precision, efficiency, and personalization [3].”
BackgroundInsurance Distribution Systems
Hilliard, J. I., & Tennyson, S. L. (2025) · Springer eBooks · doi:10.1007/978-3-031-69674-9_12 · Cited by 2
Insurance Discrimination and Fairness in the Age of Artificial Intelligence under Japanese Law
Totaro, D. L. (2023) · Hokengakuzasshi (JOURNAL of INSURANCE SCIENCE) · doi:10.5609/jsis.2023.662_69 · Cited by 1
Kumar, M., Kumar, S., & Ahmed, R. I. (2024) · Financial Mathematics and Fintech · doi:10.1007/978-3-031-47324-1_12 · Cited by 1
Big Data Based Solutions for the Digitalization of the Insurance Sector
Avcı, D. A., Şahin, G., Karagenç, Ş., Pak, B. K., Arkan, T. T., & Tekin, M. K. (2024) · Orclever Proceedings of Research and Development · doi:10.56038/oprd.v5i1.558 · Cited by 1
DIGITAL TRANSFORMATION AND THE USE OF ARTIFICIAL INTELLIGENCE IN SOCIAL INSURANCE AND INSURANCE
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The Effectiveness of Life Insurance Sales Force Training: Welcome “Me and AI”
Janowski, A. (2025) · Economies · doi:10.3390/economies13040101 · Cited by 6
“AI is already used in selected areas in the insurance sector (Eling et al., 2021), i.e., fraud detection (Wang & Xu, 2017), virtual chatbots (Weizenbaum, 1966; Riikkinen et al., 2018), or calculating insurance risk (Vassiljeva et al., 2017).”
Aldmour, R. (2025) · Journal of Lifestyle and SDGs Review · doi:10.47172/2965-730x.sdgsreview.v5.n05.pe06197 · Cited by 1
Transforming Insurance: The Impact of Digital Technologies on Operations and Processes
Musaigwa, M., & Netswera, F. (2025) · International Journal of Applied Research in Business and Management · doi:10.51137/wrp.ijarbm.2025.mmti.45818 · Cited by 1
The Future of AI in Cybersecurity Insurance
Alassaf, N., Manickam, S., Odeh, A., Laghari, S. U. A., & Anbar, M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1977-3.ch006 · Cited by 1
Government Support Driving Startup Growth [Insights from Assam]
Rroy, A. D., & Devi, D. S. (2025) · International Journal of Advanced Research in Commerce Management & Social Science · doi:10.62823/ijarcmss/8.3(i).7766 · Cited by 1
Automating Compliance Assessment of Insurance Contracts Using AI: Promises and Challenges
Jabłonowska, A., Lagioia, F., Liepiņa, R., Micklitz, H., & Giovanni Sartor, B. (2025) · AIDA Europe research series on insurance law and regulation · doi:10.1007/978-3-031-96496-1_7 · Cited by 1
Emerging AI Trends Shaping the Insurance Industry
Agrawal, S. S., Raheja, S., Goyal, N., & Syan, A. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2822-5.ch005 · Cited by 1
Srivastava, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2822-5.ch001 · Cited by 1
Atlasi, R., Rezayi, S., Mahdavi, A., Amanzadeh, M., & Naemi, R. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/8864251 · Cited by 1
“…of risk, improvement of sales, quick and efective recommendation of products matching the customer profle, automatic underwriting, automatic processing of claims, quick processing of claims, reducing customer attrition, and reducing insurers’ claims payments through improved fraud detection [112].”
InfluentialZhang, E., & Wang, D. P. (2025) · Scientific Reports · doi:10.1038/s41598-025-34208-8 · Cited by 2
Identifying Determinants for Generative AI-Enabled Cloud Platform Adoption in SMEs
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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
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