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Assessing the influence of digitalisation on systemic risk in the insurance sectors of European Union countries

Abstract

Objective: The article aims to study how much digitalisation influences the systemic risk (SR) in the insurance sector of European Union (EU) countries.

Research Design & Methods: In this research, we introduce an innovative, quantitative method for exploring the impact of digitalisation and assessing the similarities and interconnections among all European Union countries’ insurance sectors from 2004 to 2018 within the framework of Industry 4.0. The study integrates statistical and econometric tools with network modelling techniques, focusing on the topological indicators of minimum spanning trees (MST) derived from multidimensional dynamic time warping (DTW) distances. We analysed two datasets. The first one comprises exclusively data describing insurance sectors, while the second incorporates data detailing both insurance sectors and the digitalisation processes of individual EU countries. We assessed the similarity of the sectors’ dynamics over the analysed period, examining network behaviour during subprime crises, periods of excessive public debt, and immigration-related crises in Europe.

Findings: The proposed tools made it possible to determine how digitalisation contributes to the increase in systemic risk in the EU insurance sector over the periods examined and effectively measure similarity levels, and outline indirect connections between insurance sectors.

Implications & Recommendations: Because similarity can be a potential indirect channel for systemic risk contagion, countries with comparable insurance sectors and shared digitalisation-related behaviours may undergo similar repercussions during global financial downturns. Research endeavours in the insurance sector must consider digitalisation indicators that encompass technological advancements and consumer behaviour.

Contribution & Value Added: We developed a new method to examine the similarity of the insurance sectors of the European Union countries and to assess the dynamics of changes in this similarity in the era of Insurance 4.0. Such an analysis allows for a long-term assessment of the possibility of spreading threats in the insurance sector throughout the European Union.

Keywords

digitalisation, dynamic time warping, minimum spanning trees, systemic risk, insurance sector

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Author Biography

Anna Denkowska

Assistant Professor in the Mathematics Department of the Krakow University of Economics (Poland). MSc in mathematics (Jagiellonian University), PhD in mathematics (Jagiellonian University). Her research interests include functional analysis, algebraic topology, PDE’s, statistics, risk modelling, and network theory.

Stanisław Wanat

Associate Professor in the Mathematics Department at the Krakow University of Economics (Poland), MSc in mathematics (Jagiellonian University), PhD in economics (Krakow University of Economics), habilitation in economics (Krakow University of Economics). His research interests include statistics, actuarial methods, risk modelling, and forecasting.

Joao Paulo Vieito

Associate Professor at the Polytechnic Institute of Viana do Castelo (Portugal), MSc in Finance (Catholic University of Porto), PhD in Management Sciences, Finance specialisation, Faculty of Economics (University of Porto), Managing Director at the School of Business Sciences at Polytechnic Institute of Viana do Castelo, Main chairman of the World Finance Conference. His research interests include business administration, gender, gender studies, gender equality, gender differences, neuroeconomics, neurofinance, behavioural finance, gender and development, capital Structure, stocks, sociology, markets, economy, finance, mergers, investment, banking, and financial risk management.


References

  1. Acharya, V., Pedersen, L., Philippon, T., & Richardson, M.P. (2017). Measuring systemic risk. The Review of Financial Studies, 30(1), 2-47. https://doi.org/10.1093/rfs/hhw088
  2. Adrian, T., & Brunnermeier, M.K. (2016). CoVaR. American Economic Review, 106(7), 1705-1741. https://doi.org/10.1257/aer.20120555
  3. Albrecher, H., Bommier, A., Filipović, D., Koch-Medina, P., Loisel, S., & Schmeiser, H. (2019). Insurance: Models, digitalization, and data science. European Actuarial Journal, 9, 349-360. https://doi.org/10.1007/s13385-019-00211-6
  4. Albrecher, H., Bauer, D., Embrechts, P., Filipović, D., Koch-Medina, P., Korn, R., Loisel, S., Pelsser, A., Schiller, F., Schmeiser, H., & Wagner, J. (2018). Asset-liability management for long-term insurance business. European Actuarial Journal, 8, 9-25. https://doi.org/10.1007/s13385-017-0165-2
  5. Albrecher, H., Embrechts, P., Filipović, D., Harrison, G., Koch, P., Loisel, S., Vanini, P., & Wagner, J. (2016). Old-age provision: Past, present and future. European Actuarial Journal, 6, 287-306. https://doi.org/10.1007/s13385-016-0133-3
  6. Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47-97. https://doi.org/10.1103/RevModPhys.74.47
  7. Andrzejak, J., Chmielewski, L. J., Landmesser-Rusek, J., & Orłowski, A. (2024). The impact of the measure used to calculate the distance between exchange rate time series on the topological structure of the currency network. Entropy, 26(4), 279. https://doi.org/10.3390/e26040279MDPI
  8. Bellman, R., & Kalaba, R. (1959). On adaptive control processes. IRE Transactions on Automatic Control, 4(2), 1-9. https://doi.org/10.1109/TAC.1959.1104822
  9. Bekiros, S., Nguyen, D.K., Junior, L., & Uddin, G.S. (2017). Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets. European Journal of Operational Research, 256(3), 945-961. https://doi.org/10.1016/j.ejor.2016.07.032
  10. Billio, M., Getmansky, M., Lo, A.W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535-559. https://doi.org/10.1016/j.jfineco.2011.12.010
  11. Bisias, D., Flood, M., Lo, A.W., & Valavanis, S. (2012). A survey of systemic risk analytics. Annual Review of Financial Economics, 4, 255-296. https://doi.org/10.1146/annurev-financial-110311-101754Annual Reviews
  12. Boccara, N. (2003). Modeling complex systems. Springer. https://doi.org/10.1007/b97378SpringerLink
  13. Brownlees, C.T., & Engle, R.F. (2017). SRISK: A conditional capital shortfall measure of systemic risk. The Review of Financial Studies, 30(1), 48-79. https://doi.org/10.1093/rfs/hhw060
  14. Cedras, C., & Shah, M. (1995). Motion-based recognition: A survey. Image and Vision Computing, 13(2), 129-155. https://doi.org/10.1016/0262-8856(95)99722-5
  15. Chan-Lau, J. (2010). Regulatory capital charges for too-connected-to-fail institutions: A practical proposal. IMF Working Paper 10/98. International Monetary Fund. Retrieved from https://www.imf.org/external/pubs/ft/wp/2010/wp1098.pdf on May 2, 2024.
  16. Denkowska, A., & Wanat, S. (2021). A Dynamic MST-delta CoVaR Model of Systemic Risk in the European Insurance Sector. Statistics in Transition: New Series, 22(2), 173-188. https://doi.org/10.21307/stattrans-2021-017
  17. Denkowska, A., & Wanat, S. (2020a). Dependencies and systemic risk in the European insurance sector: New evidence based on Copula-DCC-GARCH, model and selected clustering methods. Entrepreneurial Business and Economics Review, 8(4), 7-27. https://doi.org/10.15678/EBER.2020.080401
  18. Denkowska, A., & Wanat, S. (2020b). A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector. Risks, 8(2), 39, 1-22. https://doi.org/10.3390/risks8020039
  19. Denkowska, A., & Wanat, S. (2021). Dynamic Time Warping Algorithm in Modeling Systemic Risk in European Insurance Sector. Entropy, 23(8), 1-18. https://doi.org/10.3390/e23081039
  20. Denkowska, A., & Wanat, S. (2022). Linkages and systemic risk in the European insurance sector: Some new evidence based on dynamic spanning trees. Risk Management, 24(2), 123-136. https://doi.org/10.1080/13669877.2021.1956014
  21. Denkowska, A., Wanat, S., & Vieito, J.P. (2023). The Effect of the 2004 EU Enlargement on the Development and Similarity of the Insurance Sectors in the EU. European Review, 2023, 1-24. https://doi.org/10.1017/S1062798723000131
  22. Diebold, F.X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134. https://doi.org/10.1016/j.jeconom.2014.04.011
  23. EIOPA. (2017). Investment behavior report. EIOPA-BoS-17/230, 16 November 2017. Retrieved from https://www.eiopa.europa.eu/ on November 16, 2023.
  24. EIOPA (2024). EIOPA’s Report on the Digitalisation of the European Insurance Sector. Retrieved from https://www.eiopa.europa.eu/ on November 16, 2023.
  25. Eling, M., & Lehmann, M. (2018). The Impact of Digitalization on the Insurance Value Chain and the Insurability of Risks. The Geneva Papers on Risk and Insurance – Issues and Practice, 43, 359-396. https://doi.org/10.1057/s41288-018-0097-2
  26. Franses, P.H., & Wiemann, T. (2020). Intertemporal similarity of economic time series: An application of Dynamic Time Warping. Computational Economics, 56(1), 59-75. https://doi.org/10.1007/s10614-019-09839-x
  27. Geiger, D., Gupta, A., Costa, L.A., & Vlontzos, J. (1995). Dynamic programming for detecting, tracking, and matching deformable contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(3), 294-302. https://doi.org/10.1109/34.391786
  28. Gray, D., & Jobst, A. (2013). Systemic Contingent Claim Analysis – Estimating Market-Implied Systemic Risk. IMF Working Paper, No. 13/54. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2020/03/01/Systemic-Contingent-Claim-Analysis-Estimation-Market-Implied-Systemic-Risk-406651 on May 2, 2024.
  29. Harrington, S.E. (2009). The financial crisis, systemic risk, and the future of insurance regulation. Journal of Risk and Insurance, 76(4), 785-819. https://doi.org/10.1111/j.1539-6975.2009.01314.x
  30. Hilbert, M. (2020). Digital technology and social change: the digital transformation of society from a historical perspective. Dialogues in Clinical Neuroscience, 22(2), 189. https://doi.org/10.31887/DCNS.2020.22.2/mhilbert
  31. IMF. (2016). The insurance sector – Trends and Systemic Risk implications. IMF – Global Financial Stability Report 2016, Chapter 3. Retrieved from https://www.imf.org/en/Publications/GFSR/Issues/2016/05/06/Global-Financial-Stability-Report-May-2016 on May 2, 2024.
  32. IMF. (2020). Cyber Risk and Financial Stability. IMF – Global Financial Stability Report 2020. Retrieved from https://www.imf.org/en/Publications/GFSR/Issues/2020/10/15/global-financial-stability-report-october-2020 on May 2, 2024.
  33. Jajuga, K., Karaś M., Kuziak, K., & Szczepaniak, W. (2017). Ryzyko systemu finansowego. Metody oceny i ich weryfikacja w wybranych krajach. Materiały i Studia, 329, Narodowy Bank Polski. Retrieved from https://www.nbp.pl on May 2, 2024.
  34. Jajuga, K. (2023). Data Analysis for Risk Management – Economics, Finance and Business: New Developments and Challenges. Risks, 11(4), 70. https://doi.org/10.3390/risks11040070
  35. Jobst, A.A. (2014). Systemic risk in the insurance sector: a review of current assessment approaches. Geneva Papers on Risk and Insurance, 39(3), 440-470. https://doi.org/10.1057/gpp.2014.5
  36. Kaur, P., & Singh, M. (2023). Exploring the impact of InsurTech adoption in Indian life insurance industry: a customer satisfaction perspective. The TQM Journal, forthcoming. https://doi.org/10.1108/TQM-06-2023-0301
  37. Keogh, E.J., & Pazzani, M.J. (2000). Scaling up dynamic time warping for data mining applications. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 285-289. https://doi.org/10.1145/347090.347161
  38. Kruskal, J. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society, 7(1), 48-50. https://doi.org/10.1090/S0002-9939-1956-0079333-3
  39. Kwon, W.J., & Wolfrom, L. (2017). Analytical tools for the insurance market and macro-prudential surveillance. OECD Journal: Financial Market Trends, 2016(1), 1-47. https://doi.org/10.1787/fmt-2016-5jrs7d94kv1d
  40. Li, W., Hommel, U., & Paterlini, S. (2018). Network topology and Systemic Risk: Evidence from the Euro Stoxx market. Finance Research Letters, 27, 105-112. https://doi.org/10.1016/j.frl.2018.03.007
  41. Lyons, D. (2019). Insurance 4.0: Six Winning Strategies in the Fourth Industrial Revolution, Digital Insurance, RGA Retrieved from https://www.rgare.com/knowledge-center/article/insurance-4.0-six-winning-strategies-in-the-fourth-industrial-revolution on May 2, 2024.
  42. Masiello, M.P. (2020). Insurance Agency 4.0: Prepare Your Agency for the Future; Develop Your Road Map for Digitalization; Increase Profit, Scalability and Time. Autor Academy Elite, ISBN 1647465222, 9781647465223
  43. Mantegna, R.N. (1999). Hierarchical structure in financial markets. European Physical Journal B – Condensed Matter and Complex Systems, 11(1), 193-197. https://doi.org/10.1140/epjb/e20010
  44. Nicoletti, B. (2020). Insurance 4.0: Benefits and Challenges of Digital Transformation. Palgrave Macmillan Cham Insurance 4.0: Benefits and Challenges of Digital Transformation, Palgrave Macmillan (2020). https://doi.org/10.1007/978-3-030-48874-1
  45. Newman, M. (2002). Assortative mixing in networks. Physical Review Letters, 89, 11. https://doi.org/10.1103/PhysRevLett.89.218701
  46. OECD (2017). Enhancing the Role of Insurance in Cyber Risk Management, OECD Publishing, Paris. Enhancing the Role of Insurance in Cyber Risk Management, OECD Publishing, Paris (2017). https://doi.org/10.1787/9789264282148-en
  47. Petitjean, F., Ketterlin, A., & Gançarski, P. (2011). A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognition, 44(3), 678-693. https://doi.org/10.1016/j.patcog.2010.09.010
  48. Rak, R., & Rak, E. (2020). The Fractional Preferential Attachment Scale-Free Network Model. Entropy, 22, 509. https://doi.org/10.3390/e22050509
  49. Ronken, L. (2018). Industry 4.0 – Implications for the Insurance Industry, Insurance Issues, General Reinsurance AG, Cologne. Industry 4.0 – Implications for the Insurance Industry, Insurance Issues, General Reinsurance AG
  50. Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. https://doi.org/10.1109/TASSP.1978.1163057
  51. Shokoohi-Yekta, M., Hu, B., Jin, H., Wang, J., & Keogh, E.J. (2017). Generalizing Dynamic Time Warping to the Multidimensional Case Requires an Adaptive Approach. Data Mining and Knowledge Discovery, 31(1), 1-31. https://doi.org/10.1007/s10618-016-0483-7
  52. Schwab, K., & Davis, N. (2018). Shaping the Future of the Fourth Industrial Revolution. World Economic Forum
  53. Torri, G., Giacometti, R., & Paterlini, S. (2018). Robust and Sparse Banking Network Estimation. European Journal of Operational Research, 270(1), 51-65. https://doi.org/10.1016/j.ejor.2017.11.031
  54. Waliszewski, K., Cichowicz, E., Gębski, Łukasz, Kliber, F., Kubiczek, J., Niedziółka, P., Solarz, M., & Warchlewska, A. (2024). Digital loans and buy now pay later from LendTech versus bank loans in the era of ‘black swans’: Complementarity in the area of consumer financing. Equilibrium. Quarterly Journal of Economics and Economic Policy, 19(1), 241-278. https://doi.org/10.24136/eq.2982
  55. Wang, G.-J., Xie, C., Han, F., & Sun, B. (2012). Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree. Physica A: Statistical Mechanics and Its Applications, 391(16), 4136-4146. https://doi.org/10.1016/j.physa.2012.02.014
  56. Wang, G.J., Xie, Ch., Zhang, P., Han, F., & Chen, S. (2014). Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach. Hindawi Publishing Corporation Discrete Dynamics in Nature and Society, 2014, Article ID 170921. https://doi.org/10.1155/2014/170921
  57. Zhao, J., Xi, Z., Itti, L. (2016). MetricDTW: local distance metric learning in Dynamic Time Warping. Retrieved from https://arxiv.org/abs/1606.03628 on May 2, 2024.

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