Volume 26 , Issue 4 , PP: 94-112, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Jamil J. Jaber 1 * , Asma S. Alzwi 2 , Abdullah A. K. Alkhawaldeh 3 , Sami Nasser Mohammad Alhjahja 4 , Yasar Shatnawi 5
Doi: https://doi.org/10.54216/IJNS.260410
This study evaluates the influence of technology risks on insurance company performance through Insurtech innovation, focusing on the roles of Data Privacy (DP), Skill Gaps (SG), and Financial Risks (FR) in predicting Insurance Performance (IP). Employing a questionnaire survey approach, the research extended historical empirical studies, capturing demographic profiles and study variables measured on a 5-point Likert scale. A pilot study refined the questionnaire, achieving an 80% response rate, and minor adjustments were made to enhance clarity. The dataset included 243 responses from employees of Jordanian insurance companies, with 37 excluded due to incomplete data. Validity and reliability were assessed using Average Variance Extracted (AVE), Composite Reliability (CR), and Cronbach's Alpha, confirming the robustness of the measurement model. Multicollinearity was tested using correlation, Tolerance, and Variance Inflation Factor (VIF), with no significant issues detected. ANOVA tests were conducted to examine the impact of experience and technology level on DP, SG, FR, and IP, revealing significant differences across groups. A multiple regression model demonstrated that DP and FR positively affect IP, while SG has a negative effect. To further predict IP, the dataset was split into 80-20% and 60-40% training-test sets, and a Multilayer Perceptron (MLP) model was employed. The MLP neural network model, using the Rprop method, highlights the importance of DP, SG, and FR in predicting IP, achieving an accuracy of up to 72%. These findings highlight the importance of addressing technology risks and leveraging Insurtech innovations to enhance insurance company performance, providing valuable insights for industry stakeholders and policymakers.
MLP model , Insurtech , Performance , Financial Risk
[1] I. Rahmani, B. Barati, V. Majazi Dalfard, and L. Hatami-Shirkouhi, "Nonparametric frontier analysis models for efficiency evaluation in insurance industry: a case study of Iranian insurance market," Neural Computing Applications, vol. 24, pp. 1153-1161, 2014.
[2] R. Al Omari, R. S. Alkhawaldeh, and J. J. Jaber, "Artificial neural network for classifying financial performance in Jordanian insurance sector," Economies, vol. 11, no. 4, p. 106, 2023.
[3] JIF, "History insurance regulations," 2025. [Online]. Available: https://www.joif.org/en/history-insurance-regulations-and-development-regulations-jordan.
[4] D. Lanfranchi and L. Grassi, "Examining insurance companies’ use of technology for innovation," The Geneva Papers on Risk Insurance: Issues and Practice, vol. 47, no. 3, p. 520, 2021.
[5] S. Volosovych, I. Zelenitsa, D. Kondratenko, W. Szymla, and R. Mamchur, "Transformation of insurance technologies in the context of a pandemic," Insurance Markets Companies, vol. 12, no. 1, pp. 1-13, 2021.
[6] I. N. Khatatbeh, H. Alshurafat, M. O. Al Shbail, and F. Jamaani, "Factors affecting employees' use and acceptance of remote working during the COVID-19 pandemic: evidence from the Jordanian insurance sector," Sage Open, vol. 13, no. 2, p. 21582440231181390, 2023.
[7] A. Paminto, I. A. Lahaya, Y. A. Yudaruddin, and R. Yudaruddin, "COVID-19 pandemic and firm performance in the insurance industry in developed and emerging markets," Insurance Markets Companies, vol. 14, no. 1, p. 85, 2023.
[8] Q. L. Kweh, W.-M. Lu, and W.-K. Wang, "Dynamic efficiency: intellectual capital in the Chinese non-life insurance firms," Journal of Knowledge Management, vol. 18, no. 5, pp. 937-951, 2014.
[9] O. M. Olarewaju and T. S. Msomi, "Intellectual capital and financial performance of South African development community’s general insurance companies," Heliyon, vol. 7, no. 4, 2021.
[10] M. Eling, D. Nuessle, and J. Staubli, "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk Insurance: Issues and Practice, vol. 47, no. 2, pp. 205-241, 2022.
[11] E. Pletneva and D. Zagorska, "Digital transformation and the use of artificial intelligence in social insurance and insurance," Social Economics, no. 67, pp. 146-155, 2024.
[12] M. Samara, A. Al-Gasaymeh, J. Al-Gasawneh, A. A. Alsmadi, and M. Al-Okaily, "Determinants of economic performance in emerging countries: evidence from generalized method of moments," in Artificial Intelligence and Economic Sustainability in the Era of Industrial Revolution 5.0, Springer, 2024, pp. 863-876.
[13] A. A. Alsmadi, A. Shuhaiber, M. Al-Okaily, A. Al-Gasaymeh, and N. Alrawashdeh, "Big data analytics and innovation in e-commerce: current insights and future directions," Journal of Financial Services Marketing, p. 1, 2023.
[14] A. A. Alsmadi, N. Alrawashdeh, A. Al-Gasaymeh, H. Al-Malahmeh, and A. Moh’d Al_hazimeh, "Impact of business enablers on banking performance: a moderating role of Fintech," Banks and Bank Systems, vol. 18, no. 1, p. 14, 2023.
[15] C. Huang, S. Chen, Y. Zhang, W. Zhou, J. J. Rodrigues, and V. H. C. de Albuquerque, "A robust approach for privacy data protection: IoT security assurance using generative adversarial imitation learning," IEEE Internet of Things Journal, vol. 9, no. 18, pp. 17089-17097, 2021.
[16] K. Fan, W. Jiang, H. Li, and Y. Yang, "Lightweight RFID protocol for medical privacy protection in IoT," IEEE Transactions on Industrial Informatics, vol. 14, no. 4, pp. 1656-1665, 2018.
[17] C. Chen, C. B. Frey, and G. Presidente, "Privacy regulation and firm performance: estimating the GDPR effect globally," The Oxford Martin Working Paper Series on Technological Economic Change, no. -1, 2022.
[18] C. B. Frey and G. Presidente, "Privacy regulation and firm performance: estimating the GDPR effect globally," Economic Inquiry, 2024.
[19] K. D. Martin, A. Borah, and R. W. Palmatier, "Data privacy: effects on customer and firm performance," Journal of Marketing, vol. 81, no. 1, pp. 36-58, 2017.
[20] Y. Zhang, C. Zhang, and Y. Xu, "Effect of data privacy and security investment on the value of big data firms," Decision Support Systems, vol. 146, p. 113543, 2021.
[21] J. Forth and G. Mason, "Information and communication technology (ICT) skill gaps and company-level performance: evidence from the ICT professionals survey 2000-01," Citeseer, 2004.
[22] R. Iskandar and N. D. Jayanto, "Analisis pengaruh kemampuan dalam mengoperasikan dan memanfaatkan teknologi terhadap kinerja karyawan," Jurnal Ilmiah Manajemen, Ekonomi Dan Akuntansi, vol. 2, no. 1, pp. 46-54, 2022.
[23] M. R. F. Adhim and E. Suherman, "Analisis kompetensi karyawan terhadap kinerja karyawan di era digital," OPTIMAL Jurnal Ekonomi Dan Manajemen, vol. 4, no. 3, pp. 95-108, 2024.
[24] V. Thota, "Role of management and technology in bridging the skill gap of an employee," Indian Journal of Applied Research, vol. 4, no. 2, pp. 8-10, 2014.
[25] X. Cheng et al., "Combating emerging financial risks in the big data era: a perspective review," Fundamental Research, vol. 1, no. 5, pp. 595-606, 2021.
[26] Š. Slávik and V. Bednárová, "Factors and circumstances affecting the business performance of firms based on new technologies," Problems Perspectives in Management, vol. 22, no. 3, p. 458, 2024.
[27] A. Bohnert, A. Fritzsche, and S. Gregor, "Digital agendas in the insurance industry: the importance of comprehensive approaches," The Geneva Papers on Risk Insurance: Issues and Practice, vol. 44, pp. 1-19, 2019.
[28] A. A. Alsmadi, N. Aalrawashdeh, A. Al-Gasaymeh, A. Moh'd Al_hazimeh, and L. Alhawamdeh, "Adoption of Islamic Fintech in lending services through prediction of behavioural intention," Kybernetes, vol. 53, no. 6, pp. 1921-1938, 2023.
[29] M. Al-Okaily, A. A. Alsmadi, N. Alrawashdeh, A. Al-Okaily, Y. Oroud, and A. S. Al-Gasaymeh, "The role of digital accounting transformation in the banking industry sector: an integrated model," Journal of Financial Reporting Accounting, vol. 22, no. 2, pp. 308-326, 2023.
[30] M. A. Almarashdah, A. Al-Gasaymeh, and A. A. Alsmadi, "Exploring the role of technology in transforming the startup companies' management: an empirical study," Quality-Access to Success, vol. 25, no. 200, 2024.
[31] A. Al-Gasaymeh, A. A. Alsmadi, N. Alrawashdeh, H. M. Alzoubi, and M. Alshurideh, "Dynamic model in estimating the impact of competition on banking efficiency: evidence from MENA countries," Calitatea, vol. 24, no. 193, pp. 385-394, 2023.
[32] H. A. Al-Shari and M. Lokhande, "The relationship between the risks of adopting FinTech in banks and their impact on the performance," Cogent Business Management, vol. 10, no. 1, p. 2174242, 2023.
[33] W. S. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," The Bulletin of Mathematical Biophysics, vol. 5, pp. 115-133, 1943.
[34] F. Rosenblatt, "The perceptron: a probabilistic model for information storage and organization in the brain," Psychological Review, vol. 65, no. 6, p. 386, 1958.
[35] F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Cornell Aeronautical Lab Inc, Buffalo, NY, 1961.
[36] M. Minsky and S. Papert, An Introduction to Computational Geometry, Cambridge Tiass, HIT, vol. 479, no. 480, 1969.
[37] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, no. 6088, pp. 533-536, 1986.
[38] K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, no. 5, pp. 359-366, 1989.
[39] V. Nair and G. E. Hinton, "Rectified linear units improve restricted Boltzmann machines," in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010, pp. 807-814.
[40] N. Srivastava et al., "Dropout: a simple way to prevent neural networks from overfitting," The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
[41] S. Ioffe, "Batch normalization: accelerating deep network training by reducing internal covariate shift," arXiv preprint arXiv:1502.03167, 2015.
[42] M. Riedmiller and H. Braun, "A direct adaptive method for faster backpropagation learning: the RPROP algorithm," in IEEE International Conference on Neural Networks, 1993, pp. 586-591.
[43] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 8th ed. Pearson Higher Ed, 2019.
[44] S. A. Raza, W. Qazi, K. A. Khan, and J. Salam, "Social isolation and acceptance of the learning management system (LMS) in the time of COVID-19 pandemic: an expansion of the UTAUT model," Journal of Educational Computing Research, vol. 59, no. 2, pp. 183-208, 2021.
[45] R. A. Fisher, Statistical Methods for Research Workers, 5th ed. Oliver and Boyd, Edinburgh and London, 1934.
