wu2025.2.6

Data dodania: 31 lipca 2025

Digital Transformation and Innovation in the Insurance Sector: Processes, Technologies, and Challenges

Adam Śliwiński, Łukasz Kuryłowicz, Renata Pajewska-Kwaśny

DOI: https://doi.org/10.33995/wu2025.2.6

Abstract

The digital transformation of the insurance sector improves risk assessment, pricing, distribution, and claims management. Key technologies driving these changes include artificial intelligence (AI), machine learning (ML), big data analytics, and automation. These advancements primarily drive process innovation, aligning with Barras’s reverse innovation cycle rather than traditional product innovation.
The authors show that key technologies such as telematics, the Internet ofThings, predictive analytics, and robotic process automation streamline operations and improve customer experience. However, challenges remain in ensuring AI transparency and interpretability. While digital transformation is still in its early stages, its continued development will shape the industry’s efficiency, competitiveness, and regulatory landscape.

Keywords

Digitalisation of insurance, process innovations, AI in insurance, Barras’s reverse innovation cycle, big data in insurance

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Bibliography

Accenture, Insurance telematics: A game-changing opportunity for the industry, 2014, http://www.accenture.com/Microsites/insights/Documents/pdfs/Accenture-Telematics-Low-ResVersion-Final.pdf

Allianz, Ubezpieczenie Allianz Podatnik, https://www.allianz.pl/pl_PL/dla-firm/dla-przedsiebiorstw/ ochrona-prawno-podatkowa/allianz-podatnik.html (Dec 7, 2024).

Armano D., COVID-19 will be remembered as the ‘Great Accelerator’ of digital transformation, ‘Forbes’, 09 September 2020.

Assa H., Mostafa P. and Abdolrahim B., Sound deposit insurance pricing using a machine learning approach, ‘Risks’, 2019 vol. 7, pp. 1–18.

Barras R., Interactive innovation in financial and business services: the vanguard of the service revolution, ‘Research Policy’, 1990 vol. 19, pp. 215–37.

Barras R., Towards a Theory of Innovation in Services, ‘Research Policy’, 1986 vol. 15, pp. 161–173.

Baudry M. and Robert Ch.Y., A machine learning approach for individual claims reserving in insurance, ‘Applied Stochastic Models in Business and Industry’, 2019 vol. 35, pp. 1127–55.

Bennett R., The top 10 most innovative and disruptive Insurtech companies, https://www.information-age.com/the-top-10-most-innovative-insurtech-companies-15788/ (Jan 31, 2025).

Bischofberger St.M., In-sample hazard forecasting based on survival models with operational time, ‘Risks’, 2020 vol. 8, pp. 1–17.

Board of Innovation, 10 Innovative Startups in the Insurance Industry, https://www.boardofinnovation.com/blog/10-innovative-startups-in-the-insurance-industry/ (Dec 7, 2024).

Boodhun N. and Jayabalan M., Risk Prediction in Life Insurance Industry Using Supervised Learning Algorithms, ‘Complex and Intelligent Systems’, 2018 no. 4(2), pp. 145–54.

Borges M., Chesbrough H. and Moedas C., Open innovation: Research, practices, and policies, ‘California management review’, 2018 vol. 60, issue 2, pp. 5–16.

Brynjolfsson E. and Collins A., How Should We Measure the Digital Economy?, ‘Harvard Business Review’, November–December 2019, pp. 140–148.

Chmilowski A., Insurtech–zmiany tradycyjnych ubezpieczeń w kontekście ekonomii współpracy, ‘Kwartalnik Naukowy Uczelni Vistula’, 2019 no. 4(62), pp. 54–69.

Cortis D., Debattista J., Debono J. and Farrell M., InsurTech in: Disrupting finance: FinTech and strategy in the 21st century, eds. T. Lynn, J.G. Mooney, P. Rosati and M. Cummins, Palgrave Pivot, Cham 2019, pp. 74–77.

Cranfield A. and White D., The rise of the robo-insurer, Ninety consulting paper, 2016, pp. 1–18.

Dhieb N., Ghazzai H., Besbes H. and Massoud Y., A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement, ‘IEEE Access’, 2020 vol. 8, pp. 58546–58558.

Díaz Z., Segovia M.J. and Fernández J., Machine learning and statistical techniques. An application to the prediction of insolvency in Spanish non-life insurance companies, ‘The International Journal of Digital Accounting Research’, 2005 vol. 5, pp. 1–45.

Dubey A., Parida T., Birajdar A., Prajapati A.K. and Rane S., Smart Underwriting System: An Intelligent Decision Support System For Insurance Approval & Risk Assessment, ‘Proceedings of the Third International Conference for Convergence in Technology’, Tamil Nadu, India 2018.

Elpidorou V., Margraf C., Martínez-Miranda M.D. and Nielsen B., A likelihood approach to Bornhuetter– Ferguson analysis, ‘Risks’, 2019 vol. 7, pp. 1–20.

Furr N. and Shipilov A., Digital doesn’t have to be disruptive: the best results can come from adaptation rather than reinvention, ‘Harvard Business Review’, 2019 vol. 97, issue 4, pp. 94–103.

Gabrielli A., Richman R. and Wüthrich M., Neural network embedding of the over-dispersed Poisson reserving model, ‘Scandinavian Actuarial Journal’, 2020 vol. 2020, pp. 1–29.

Gallagher Re, Global InsurTech Report, 2023, p. 47.

Gan G., 2013, Application of data clustering and machine learning in variable annuity valuation, ‘Insurance: Mathematics and Economics’, 2013 vol. 53, pp. 795–801.

Grize Y.-L., Fischer W. and Lützelschwab Ch., Machine learning applications in nonlife insurance, ‘Applied Stochastic Models in Business and Industry’, 2020 vol. 36, pp. 523–37.

Hanush H. and Pyka A., Elgar Companion to Neo-Schumpeterian Economics, Edward Elgar Publishing, Cheltenham, UK, Northampton, MA 2007.

Janasz W. and Kozioł-Nadolna K., Determinanty działalności innowacyjnej przedsiębiorstw, PWE, Warszawa 2007.

Kaushik K., Bhardwaj A., Dwivedi A.D. and Singh R., Machine Learning-Based Regression Framework to Predict Health Insurance Premiums, ‘International Journal of Environmental Research and Public Health’, 2022 vol. 19(13), issue 7898, pp. 1–15.

Koprivica M., Insurtech: challenges and opportunities for the insurance sector, ‘Proceedings of 2nd International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA’, Graz, Austria 2018, pp. 622–623.

Krah A.-S., Nikolić Z. and Korn R., Machine learning in least-squares Monte Carlo proxy modeling of life insurance companies, ‘Risks’, 2020 vol. 8, pp. 1–79.

Kumar N., Srivastava J.D. and Bisht H., Artificial Intelligence in Insurance Sector, ‘Journal of the Gujarat Research society’, 2019 no. 21(7), pp. 79–91.

Kuryłowicz Ł., Telematyka ubezpieczeniowa i jej wpływ na równowagę rynku ubezpieczeń komunikacyjnych, Oficyna Wydawnicza SGH, Warszawa 2021.

Laeven L., Levine R. and Michalopoulos S., Financial innovation and endogenous growth, ‘Journal of Financial Intermediation’, 2015 vol. 24, pp. 1–24.

Lamparelli N., On‐Demand Insurance: Ultimately a Bust?, https://www.insurancethoughtleadership.com/on-demand-insurance-ultimately-a-bust/ (Aug 14, 2024).

Melville G., The unit-linked approach to life insurance, ‘Journal of the Institute of Actuaries’, 1970 vol. 96, issue 3, pp. 311–367

Niedzielski P. and Rychlik K., Innowacje i kreatywność, Wydawnictwo Naukowe Uniwersytetu Szczecińskiego, Szczecin 2006.

Pearson R., Towards an Historical Model of Services Innovation: The Case of the Insurance Industry, 1700–1914, ‘Economic History Review’, 1997 vol. 50, no. 2, pp. 235–256.

PIU and Accenture, Cyfryzacja Sektora Ubezpieczeń w Polsce, Warszawa 2024, p. 76.

Porter M.E. and Stern S., The New Challenge to America’s Prosperity: Findings from the Innovation Index, Council on Competitiveness, Washington, DC 1999.

Raverkar A.K., Avramakis E. and Fitzgerald C., Why insurers need to transform digital distribution and how to do it in the digital age, https://www.swissre.com/institute/research/topics-and- -risk-dialogues/digital-business-model-and-cyber-risk/why-insurers-need-to-transform-digital- -distribution.html (Jan 22, 2025).

Schumpeter J.A., Business Cycles: A Theoretical, Historical and Statistical Analysis of the Capitalist Process, Vol. 2, McGraw-Hill, New York 1939.

Schumpeter J.A., Capitalism, Socialism and Democracy, 5th edition, George Allen and Unwin, London 1976.

Schumpeter J.A., The theory of economic development: an inquiry into profits, capital, credit, interest and the business cycle, Harvard University Press, Cambridge 1934.

Schumpeter J.A., Grzywicka J. and Górski J., Teoria rozwoju gospodarczego, PWN, Warszawa 1960.

Silber W.L., The Process of Financial Innovation, ‘American Economic Review’, 1983 vol. 73, issue 2, pp. 89–95.

Śliwiński A., Karmańska A., Michalski T., European Insurance Markets in Face of Financial Crisis: Application of Learning Curve Concept as a Tool of Insurance Products Innovation – Discussion, ‘Journal of Reviews on Global Economics’, 2017 vol. 9, pp. 404–419.

Stroiński E., Ubezpieczenie na życie, Wyższa Szkoła Ubezpieczeń i Bankowości: ‘Lam’, Warszawa 1996.

The handbook of data mining, ed. N. Ye, CRC Press, Boca Raton 2003.

Ubezpieczenia cyfrowe. Możliwości, oczekiwania, wyzwania, eds. J. Monkiewicz, L. Gąsiorkiewicz, P. Gołąb, M. Monkiewicz, PWN, Warszawa 2022.

Wycinka E., Uniwersalność zastosowań modeli skoringowych, http://media.statsoft.nazwa.pl/_ old_dnn/downloads/uniwersalnosc_zastosowan_modeli_skoringowych.pdf (Dec 12, 2024).