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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The application of AI by insurance companies might allow for a more accurate prediction of loss probabilities, thus reducing asymmetric information.
“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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Cited by
Sorted by citations
Artificial intelligence and machine learning in finance: A bibliometric review
Ahmed, S., Alshater, M. M., Ammari, A. E., & Hammami, H. (2022) · Research in International Business and Finance · doi:10.1016/j.ribaf.2022.101646 · Cited by 527 · see citing works
The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae191 · Cited by 371 · see citing works
“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 387 · see citing works
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 239 · see citing works
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 155 · see citing works
Explainable Artificial Intelligence (XAI) in Insurance
Owens, E., Sheehan, B., Mullins, M., Cunneen, M., Ressel, J., & Castignani, G. (2022) · Risks · doi:10.3390/risks10120230 · Cited by 119 · see citing works
Machine Learning - Algorithms, Models and Applications
Sen, J. (2021) · Artificial intelligence · doi:10.5772/intechopen.94615 · Cited by 81 · see citing works
Rane, N., Choudhary, S., & Rane, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4644059 · Cited by 81 · see citing works
Challenges in Achieving Artificial Intelligence in Agriculture
Atapattu, A. J., Perera, L., Nuwarapaksha, T. D., Udumann, S. S., & Dissanayaka, N. S. (2024) · Springer eBooks · doi:10.1007/978-981-97-5878-4_2 · Cited by 70 · see citing works
Rodriguez-Garcia, P., Li, Y., López-López, D., & Juan, A. A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100772 · Cited by 52 · see citing works
Pathak, A., & Bansal, V. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100094 · Cited by 40 · see citing works
Adeoye, O. B., Okoye, C. C., Ofodile, O. C., Odeyemi, O., Addy, W. A., & Ajayi-Nifise, A. O. (2024) · International Journal of Management & Entrepreneurship Research · doi:10.51594/ijmer.v6i3.840 · Cited by 38 · see citing works
Papadopoulos, C., Kollias, K.-F., & Fragulis, G. F. (2024) · Future Internet · doi:10.3390/fi16110415 · Cited by 35 · see citing works
AI revolution in insurance: bridging research and reality
Bhattacharya, S., Castignani, G., Masello, L., & Sheehan, B. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1568266 · Cited by 32 · see citing works
Jena, S. K., Kumar, B. P., Mohanty, B., Singhal, A., & Barik, R. C. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100411 · Cited by 32 · see citing works
Cybersecurity, cyber insurance and small-to-medium-sized enterprises: a systematic Review
Adriko, R., & Nurse, J. R. C. (2024) · Information and Computer Security · doi:10.1108/ics-01-2024-0025 · Cited by 29 · see citing works
Introductory Chapter: Machine Learning in Finance-Emerging Trends and Challenges
Sen, J., Sen, R., & Dutta, A. (2021) · Artificial intelligence · doi:10.5772/intechopen.101120 · Cited by 29 · see citing works
Kuppan, K., Acharya, D. B., & Divya, B. (2024) · IEEE Access · doi:10.1109/access.2024.3509918 · Cited by 38 · see citing works
A Rank Graduation Accuracy measure to mitigate Artificial Intelligence risks
Raffinetti, E. (2023) · Quality & Quantity · doi:10.1007/s11135-023-01613-y · Cited by 28 · see citing works
“…as AI includes a wide set of technologies and methods which are disrupting the insurance domain, the main challenge for insurers is resorting to suitable regulatory actions to mitigate operational, reputational and strategic risks (see e.g., Ceylan 2022; Eling et al. 2022; Mullins et al. 2021).”
BackgroundArtificial Intelligence and Blockchain Technology in Insurance Business
Ahmad, S., & Saxena, C. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0601-7_6 · Cited by 28 · see citing works
Research on InsurTech and the Technology Innovation Level of Insurance Enterprises
Liu, J., Ye, S., Zhang, Y., & Zhang, L. (2023) · Sustainability · doi:10.3390/su15118617 · Cited by 35 · see citing works
“…technologies such as mobile Internet, cloud computing, big data, and blockchain, insurance enterprises can have more unique and e ffi cient information collection and processing capabilities, which can e ff ectively alleviate the problem of information asymmetry between supply and demand [68,69].”
BackgroundAn Integrated Artificial Intelligence of Things Environment for River Flood Prevention
Boulouard, Z., Ouaissa, M., Ouaissa, M., Siddiqui, F. A., Almutiq, M., & Krichen, M. (2022) · Sensors · doi:10.3390/s22239485 · Cited by 26 · see citing works
“Artificial Intelligence (AI) is a set of algorithms which, based on the rapid growth of data, can efficiently provide computers and machines with the ability to sense their environment, learn from it, and react to it [30].”
BackgroundIndustrial robots, supply chain spillover effect and firm employment: Evidence from China
Mao, Q., & Guo, C. (2024) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2024.106858 · Cited by 24 · see citing works
Xu, R., & Song, F. M. (2025) · Economic Modelling · doi:10.1016/j.econmod.2025.107126 · Cited by 24 · see citing works
A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data
Sarker, K. U., Saqib, M., Hasan, R., Mahmood, S., Hussain, S., Abbas, A., & Deraman, A. (2022) · Computers · doi:10.3390/computers11110158 · Cited by 27 · see citing works
“AI chatbots provide instant routine services to its customers [64], financial organizations use machine learning applications to enjoy competitive advantages [65], fraud detection systems are used in financial organizations [66], risk assessment and mitigation recommendation machine learning applications reduce risk i…”
BackgroundGupta, A., & Venkataraman, S. (2024) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2023.101412 · Cited by 33 · see citing works
A new competitive edge: crafting a service climate that facilitates optimal human–AI collaboration
van Riel, A. C. R., Tabatabaei, F., Yang, X., Masłowska, E., Palanichamy, V., Clark, D., & Luongo, M. (2024) · Journal of service management · doi:10.1108/josm-05-2024-0220 · Cited by 27 · see citing works
The impact of generative artificial intelligence on socioeconomic inequalities and policy makingPreprint
Capraro, V., Lentsch, A., Acemoğlu, D., Akgün, S., Akhmedova, A., Bilancini, E., Bonnefon, J., Brañas‐Garza, P., Butera, L., Douglas, K. M., et al. (2023) · doi:10.31234/osf.io/6fd2y · Cited by 36 · see citing works
Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems
Shah, F., Liu, Y., Anwar, A., Shah, Y. A., Alroobaea, R., Hussain, S., & Ullah, S. S. (2022) · Security and Communication Networks · doi:10.1155/2022/7149902 · Cited by 20 · see citing works
RetractedFramework for open insurance strategy: insights from a European study
Standaert, W., & Muylle, S. (2022) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-022-00264-8 · Cited by 22 · see citing works
Fritzsche, A., & Bohnert, A. (2021) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-021-00244-4 · Cited by 19 · see citing works
Analyzing the Relationship between Agricultural AI Adoption and Government-Subsidized Insurance
Osorio, C. P., Leucci, F., & Porrini, D. (2024) · Agriculture · doi:10.3390/agriculture14101804 · Cited by 17 · see citing works
Blockchain Technology as an Emerging Technology in the Insurance Market
Trivedi, S., & Malik, R. (2022) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80262-605-620221006 · Cited by 17 · see citing works
Challenges, Issues, and Recommendations for Blockchain- and Cloud-Based Automotive Insurance Systems
Mateen, A., Khalid, A., Lee, S., & Nam, S. Y. (2023) · Applied Sciences · doi:10.3390/app13063561 · Cited by 15 · see citing works
Pathak, A., & Bansal, V. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-50192-0_5 · Cited by 21 · see citing works
Need-Based and Optimized Health Insurance Package Using Clustering Algorithm
Matloob, I., Khan, S. A., Hussain, F., Butt, W. H., Rukaiya, R., & Khalique, F. (2021) · Applied Sciences · doi:10.3390/app11188478 · Cited by 15 · see citing works
“The impact of artificial intelligence on the insurance industry is presented in [24].”
BackgroundHealthcare fraud detection using adaptive learning and deep learning techniques
Matloob, I., Khan, S. A., Rukaiya, R., Alfraihi, H., & Khan, J. A. (2025) · Evolving Systems · doi:10.1007/s12530-025-09698-6 · Cited by 14 · see citing works
“The existing insurance management systems are based on predefined rules (Eling et al. 2022).”
The Impact of Generative Artificial Intelligence on Socioeconomic Inequalities and Policy MakingPreprint
Capraro, V., Lentsch, A., Acemoğlu, D., Akgün, S., Akhmedova, A., Bilancini, E., Bonnefon, J., Brañas‐Garza, P., Butera, L., Douglas, K. M., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4666103 · Cited by 13 · see citing works
Critical Evaluation of the Future Role of Artificial Intelligence in Business and Society
Yahaya, M., Umagba, A., Obeta, S., & Maruyama, T. (2023) · Journal of Artificial Intelligence Machine Learning and Data Science · doi:10.51219/jaimld/moshood-yahaya/03 · Cited by 11 · see citing works
“Despite the extensive advancement in the application AI to our lives and industries, the full potential is still constrained by the lack of access to the adequate computing resources (Eling et al., 2021).”
BackgroundInfluentialArtificial Intelligence and Service Quality of Telecommunication Firms in Nigeria
BUSAYO, T., Igbekoyi, O. E., Oluwagbade, O. I., ADEWARA, Y., Dagunduro, M. E., & BOLUWAJI, Y. (2023) · Journal of Economics Finance and Accounting Studies · doi:10.32996/jefas.2023.5.3.16 · Cited by 11 · see citing works
“As chatbots take on roles in companies, telecoms will deploy human salespeople and support agents more effectively so policyholders get faster access to services and product Page | 206 information (Martin et al., 2021).”
Dependence modeling of multivariate longitudinal hybrid insurance data with dropout
Frees, E. W., Bolancé, C., Guillen, M., & Valdez, E. A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115552 · Cited by 10 · see citing works
Adamu, I. I., Okanlawon, T. T., Oyewobi, L. O., Shittu, A. A., & Jimoh, R. A. (2024) · International Journal of Building Pathology and Adaptation · doi:10.1108/ijbpa-02-2024-0050 · Cited by 10 · see citing works
Pathak, A., & Bansal, V. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2490672 · Cited by 11 · see citing works
The Role of Artificial Intelligence in the Insurance Industry of India
Sharma, V., & Sood, D. (2022) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80262-637-720221017 · Cited by 10 · see citing works
Abdallah-Ou-Moussa, S., Wynn, M. G., Kharbouch, O., & Rouaine, Z. (2024) · Administrative Sciences · doi:10.3390/admsci14110282 · Cited by 9 · see citing works
Lee, Y., & Yim, H. (2025) · Administrative Sciences · doi:10.3390/admsci15010025 · Cited by 9 · see citing works
“…al., 2016; Zhou et al., 2023; Juma’h & Li, 2023; Sangari & Mashatan, 2024; Kim & Song, 2018; Koh et al., 2019; Bharal & Shapiro, 2016; Turrin, 2018; Eling et al., 2022; Spender et al., 2019; Ahmad & Saxena, 2022; Catlin et al., 2018; Greineder et al., 2020; Cortis et al., 2019); second, studies on…”
Exploring the Potential of Cyber Manufacturing System in the Digital Age
Ahmed, U., Lin, J. C., & Srivastava, G. (2023) · ACM Transactions on Internet Technology · doi:10.1145/3596602 · Cited by 9 · see citing works
Determinants influencing the adoption of artificial intelligence technology in non-life insurers.
Nguyen, T. H. D., Nguyen, X. T., Le, T. H. T., & Bui, Q. A. (2024) · Corporate Governance and Organizational Behavior Review · doi:10.22495/cgobrv8i1p17 · Cited by 9 · see citing works
Prado, J. C. A., Hernández-Cenzano, C. G., Villalta-Herrera, C. D., & Barahona-Silva, E. W. (2024) · Administrative Sciences · doi:10.3390/admsci14090190 · Cited by 8 · see citing works
Value-Chain in Beach Tourism: The Consideration of Maslahah in Strategic Formulation
Thaha, F., Parakkasi, I., Sirajuddin, S., & Fathurrahman, R. A. (2022) · JURNAL MINDS Manajemen Ide dan Inspirasi · doi:10.24252/minds.v9i2.31584 · Cited by 7 · see citing works
Artificial intelligence innovation and financial report quality
Li, J. (2025) · International Review of Economics & Finance · doi:10.1016/j.iref.2025.104832 · Cited by 7 · see citing works
Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers
Siopi, E., Poufinas, T., Chen, J. M., & Agiropoulos, C. (2023) · International Advances in Economic Research · doi:10.1007/s11294-023-09867-w · Cited by 7 · see citing works
“These capabilities enable organizations to achieve and maintain a long-term competitive advantage (Paltrinieri et al., 2019; Sen & Mehtab, 2021; Lei et al., 2020; Dornadula & Geetha, 2019; Eling et al., 2022; Yu et al., 2021; Leo et al., 2019; Gu et al., 2020; Ye & Zhang, 2019; Zand et al., 2020).”
BackgroundComparative Analysis of Machine Learning Algorithms for Health Insurance Pricing
Bau, Y.-T., & Hanif, S. (2024) · JOIV International Journal on Informatics Visualization · doi:10.62527/joiv.8.1.2282 · Cited by 6 · see citing works
Insurtech strategies: a comparison of incumbent insurance firms with new entrants
Holland, C. P., & Kavuri, A. S. (2024) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-024-00341-0 · Cited by 6 · see citing works
“…the technical attributes of big data (Catlin et al. 2018), the emergence of data platforms and related value chain networks (Eling and Lehmann 2018; Eling et al. 2021) will have a significant competitive impact on the insurance market because they will define the control over the collection,…”
Solving AI ethics? Hybrid expertise and professional power in EU ethics governance
Gamerdinger, A., & Willers, J. O. (2025) · Journal of European Public Policy · doi:10.1080/13501763.2025.2499113 · Cited by 6 · see citing works
“On the one hand, AI integrations into insurance promise to produce significant efficiency gains alongside the entire value chain (Albrecher et al ., 2019; Eling et al ., 2022).”
Tavares, E. D. S., Leardini, M., & Pessôa, M. S. D. P. (2024) · Innovation & Management Review · doi:10.1108/inmr-03-2022-0042 · Cited by 6 · see citing works
Legal status of artificial intelligence-based health insurance services
Riyanti, R. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6n2.10600 · Cited by 7 · see citing works
“"Particularly for insurance, we trust that AI can change individuals' insight that protection is vital to safeguard us from undesirable things (Eling et al., 2022).”
BackgroundMachine Learning in Finance-Emerging Trends and ChallengesPreprint
Sen, J., Sen, R., & Dutta, A. (2021) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2110.11999 · Cited by 6 · see citing works
“Loan and insurance underwritings: Loans, credit, and insurance underwriting is also an area where the large-scale deployment of machine learning models can be made by financial institutions for achieving competitive advantage [5].”
Nwogugu, M. C. I. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2980133 · Cited by 5 · see citing works
INSURANCE OPPORTUNITIES AND CHALLENGES IN AN ARTIFICIAL INTELLIGENCE SOCIETY
Kajwang, B. (2022) · European Journal of Technology · doi:10.47672/ejt.1180 · Cited by 4 · see citing works
“Maximum benefits from AI requires well-organized structures, dedicated time, teamwork, patience, consistent training and guided leadership from industrial experts (Eling, Nuessle & Staubli, 2022).”
BackgroundNew technologies in the financial industry: case of Poland
Iwanicz‐Drozdowska, M., Cichowicz, E., Cicirko, M., Kawiński, M., & Nowak, A. K. (2023) · Economics and Business Review/The Poznań University of Economics Review · doi:10.18559/ebr.2023.3.926 · Cited by 7 · see citing works
Implementation Of Artificial Intelligence at The Insurance Company
Sundari, D., Melinasari, S., Suryani, E., & Fadilah, H. (2024) · Proceeding of The International Conference on Multidisciplinary Research for Sustainable Innovation. · doi:10.31098/icmrsi.v1i.805 · Cited by 4 · see citing works
Iqbal, M. N., & Jan, M. T. (2025) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-9954-5.ch006 · Cited by 4 · see citing works
Systemic Review of AI Reshaped Blockchain Applications
Shamsuddoha, M., Kashem, M. A., & Koul, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-28180-8_32 · Cited by 4 · see citing works
Digital Transformation with Artificial Intelligence in the Insurance Industry
GÜRSEV, S. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-981-99-6062-0_30 · Cited by 3 · see citing works
Pego, A., & Lourenço, J. (2024) · Springer proceedings in earth and environmental sciences · doi:10.1007/978-3-031-57650-8_5 · Cited by 3 · see citing works
Li, X. (2023) · Academic Journal of Computing & Information Science · doi:10.25236/ajcis.2023.060416 · Cited by 3 · see citing works
On the Transposition of FAIR Data Principles to Financial Services: An Adapted FAAIR Guideline
Owens, E., Sheehan, B., Mullins, M., Cunneen, M., & O’Connell, B. (2025) · Accounting Finance & Governance Review/Accounting finance & governance review · doi:10.52399/001c.138720 · Cited by 3 · see citing works
DIGITAL TRANSFORMATION OF THE FINANCIAL MARKET: RISKS AND OPPORTUNITIES
Bulyga, E., Vasyukova, L., Grigoryeva, V. I., & Dimov, R. (2022) · Krasnoyarsk Science · doi:10.12731/2070-7568-2022-11-2-49-67 · Cited by 3 · see citing works
The Role of Artificial Intelligence for the Improvement and Management of Global Value Chains
Marino, D., & Stilo, P. (2023) · Studies in systems, decision and control · doi:10.1007/978-3-031-33461-0_4 · Cited by 2 · see citing works
Governing AI Output in Autonomous Driving: Scalable Privacy Infrastructure for Societal Acceptance
Fujii, Y. (2025) · Future Transportation · doi:10.3390/futuretransp5030116 · Cited by 2 · see citing works
“Other potential recipients include universities and research institutions [49] for academic use, insurance companies for accident response, infrastructure managers for road maintenance, advertising agencies for commercial use, and smart city entities [50] for urban function optimization.”
McDonnell, K., Sheehan, B., & Murphy, F. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124418 · Cited by 2 · see citing works
Conversational AI in customer support: Revolution or risk?
Sarwar, B., & Hashmi, J. (2025) · AI and Ethics · doi:10.1007/s43681-025-00893-4 · Cited by 3 · see citing works
“Eling et al. [35] demonstrates AI applications in insurance which advance analytics enables insurance businesses to shift from reactive compensation to proactive risk-based management as described by Hentzen et al. [ 20] in their financial service customer AI analysis.”
张文波, Feng, J., Hu, B., & Xie, F. (2026) · Sustainability · doi:10.3390/su18041908 · Cited by 2 · see citing works
“Meanwhile, rapid advances in digital technologies, such as artificial intelligence and big data, have created new opportunities to reshape agricultural insurance operations, giving rise to a new paradigm known as agricultural insurance technology (AIT) [4].”
Transformação digital e seguro: uma revisão sistemática da literatura
Barcelos, V., & dos Santos, A. M. (2023) · Revista de Gestão e Secretariado (Management and Administrative Professional Review) · doi:10.7769/gesec.v14i6.2269 · Cited by 1 · see citing works
Forecasting Transitions in Digital Society: From Social Norms to AI Applications
Ullrich, D., & Diefenbach, S. (2023) · doi:10.3390/engproc2023039088 · Cited by 2 · see citing works
“Artificial intelligence is already being used to support complex decisions, for example in the fields of insurance [38,39], medicine (for example, diagnostics and pattern recognition in image processing mentioned by Kermany et al.”
MethodIvanson, H., Altenhofen, B., Cannesson, M. P., & Canales, C. (2023) · Current anesthesiology reports · doi:10.1007/s40140-023-00572-2 · Cited by 2 · see citing works
Synergistic advantages of deep learning and reinforcement learning in economic forecasting
Zhang, G. (2023) · International Journal of Global Economics and Management · doi:10.62051/ijgem.v1n1.13 · Cited by 2 · see citing works
“These innovative methods provide more and more timely information for economists and decision makers, and provide strong support for formulating effective economic policies and business strategies(Eling et al., 2021).”
MethodYapay Zekâ ve Makine Öğreniminin Sigorta Sektörüne Etkisi
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Mudholkar, M. (2024) · Journal of Informatics Education and Research · doi:10.52783/jier.v4i1.606 · Cited by 2 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
-, R. E. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i03.20511 · Cited by 2 · see citing works
“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 · see citing works
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 · see citing works
Kumar, M., Kumar, S., & Ahmed, R. I. (2024) · Financial Mathematics and Fintech · doi:10.1007/978-3-031-47324-1_12 · Cited by 1 · see citing works
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 · see citing works
DIGITAL TRANSFORMATION AND THE USE OF ARTIFICIAL INTELLIGENCE IN SOCIAL INSURANCE AND INSURANCE
Pletneva, E., & Zagorska, D. M. (2024) · Social Economics · doi:10.26565/2524-2547-2024-67-14 · Cited by 1 · see citing works
The Effectiveness of Life Insurance Sales Force Training: Welcome “Me and AI”
Janowski, A. (2025) · Economies · doi:10.3390/economies13040101 · Cited by 4 · see citing works
“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 · see citing works
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 · see citing works
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“…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].”
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- 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 · 2016: 1
- 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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