Statistical analysis of significant variables of contribution margin and claims: case of an insurance company
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Published:
Dec 24, 2025
Keywords:
Insurance company, liquidity, sinister, statistical analysis, contribution.
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Abstract
Introduction: This study examines the relevant factors influencing the contribution margin and losses in a vehicle insurance company in Ecuador, using data collected during the years between 2015 and 2024. Objectives: The main objective is to identify and measure the elements that have the greatest impact on profitability and the frequency of claims, to provide ideas for improving risk management and financial strategy. Methodology: To determine the relationship between independent and dependent variables, a statistical analysis of the data collected from the Superintendence of Companies is used in this methodology. Techniques such as regression and multivariate analysis are applied for this purpose. Results: The findings indicate that the contribution margin is significantly affected by macroeconomic and sector-specific variables, such as the loss rate and insurance rates. Conclusions: It can be concluded from the research that efficiently managing these variables allows for a considerable improvement in the profitability of insurance companies, which indicates the importance of adopting more flexible and strategic policies when setting prices and assessing risks. This research offers a detailed analysis of the elements that influence monetary success in the insurance industry in Ecuador, serving as a solid foundation for research and strategic decisions in the future. General area of study: Administration. Specific area of study: Business Management. Type of item: original.
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Sánchez Sánchez, R. M. (2025). Statistical analysis of significant variables of contribution margin and claims: case of an insurance company. Visionario Digital, 9(4), 52-84. https://doi.org/10.33262/visionariodigital.v9i4.3570
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References
Abel, S., & Marire, J. (2021). Competition in the insurance sector - an application of Boone indicator. Cogent Economics and Finance, 9(1). https://doi.org/10.1080/23322039.2021.1974154
Adelmann, M., Fernandez-Arjona, L., Mayer, J., & Schmedders, K. (2021). A large-scale optimization model for replicating portfolios in the life insurance industry. Operations Research, 69(4), 1134-1157. https://doi.org/10.1287/opre.2020.2098
Agarwal, D., & Tripathi, K. (2022). A framework for structural damage detection system in automobiles for flexible insurance claim using IOT and machine learning. International Mobile and Embedded Technology Conference -MECON, 5-8. https://doi.org/10.1109/MECON53876.2022.9751889
Alwis, S., & Jinasena, T. M. K. K. (2022). A blockchain-based decentralized insurance platform. International Research Conference on Smart Computing and Systems Engineering - SCSE, 5, 137-142. https://doi.org/10.1109/SCSE56529.2022.9905219
Arabmaldar, A., Sahoo, B. K., & Ghiyasi, M. (2023). A generalized robust data envelopment analysis model based on directional distance function. European Journal of Operational Research, 311(2), 617-632. https://doi.org/10.1016/j.ejor.2023.05.005
Armenia, S., Angelini, M., Nonino, F., Palombi, G., & Schlitzer, M. F. (2021). A dynamic simulation approach to support the evaluation of cyber risks and security investments in SMEs. Decision Support Systems, 147(113580). https://doi.org/10.1016/j.dss.2021.113580
Dash, S., Panigrahi, B. S., Sanikommu, V. V. B. R., Madhavi, B. K., & Sahoo, S. K. (2024). A comparative analysis of different machine learning techniques for medical insurance premium prediction [1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU), 1–6. IEEE]. https://doi.org/10.1109/IC-CGU58078.2024.10530731
Denkowska, A., & Wanat, S. (2021). A dynamic MST-deltaCoVaR model of systemic risk in the European insurance sector. Statistics in Transition New Series, 22(2), 173-188. https://doi.org/10.21307/STATTRANS-2021-022
Dutta, H., & Bhaumik, P. (2023). A blockchain based sustainable digital insurance business parametric solution architecture to protect Realtime QSR business interruption losses [7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings]. https://doi.org/10.1109/CSITSS60515.2023.10334071
Eckert, J., Graf, S., & Kling, A. (2021). A measure to analyze the interaction of contracts in a heterogeneous life insurance portfolio. European Actuarial Journal, 11(1), 87-112. https://doi.org/10.1007/s13385-020-00225-2
Farhat, D., & Awan, M. S. (2021). A brief survey on ransomware with the perspective of internet security threat reports [9th International Symposium on Digital Forensics and Security (ISDFS), 1–6]. https://doi.org/10.1109/ISDFS52919.2021.9486348
Garmdareh, M. S., Neysiani, B. S., Nogorani, M. Z., & Bahramizadegan, M. (2023). A machine learning-based approach for medical insurance anomaly detection by predicting indirect outpatients’ claim price [9th International Conference on Web Research - ICWR]. https://doi.org/10.1109/ICWR57742.2023.10139290
Guerar, M., Merlo, A., Migliardi, M., Palmieri, F., & Verderame, L. (2020). A fraud-resilient blockchain-based solution for invoice financing. IEEE Transactions on Engineering Management, 67(4), 1086-1098. https://doi.org/10.1109/TEM.2020.2971865
Herrmann, H., & Masawi, B. (2022). Three and a half decades of artificial intelligence in banking, financial services, and insurance: a systematic evolutionary review. Strategic Change, 31(6), 549-569. https://doi.org/10.1002/jsc.2525
Hogan, K. M. (2020). A global comparison of corporate value adjustments to news of cyber-attacks. Journal of Governance and Regulation, 9(2), 34-44. https://doi.org/10.22495/jgrv9i2art2
Huang, Y.-K., & Chuang, F.-C. (2020). A brief on risk management and application models of unmanned aerial vehicles [3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII)]. https://doi.org/10.1109/ICKII50300.2020.9318985
Jang, J., Qu, Y., Zhao, H., & Dassios, A. (2023). A Cox model for gradually disappearing events. Probability in the Engineering and Informational Sciences, 37(1), 214-231. https://doi.org/10.1017/S0269964821000553
Jarraya, B. (2021). A new optimization approach to assess optimal asset allocation in European non-life insurance companies. International Journal of Applied Decision Sciences, 14(4), 461-476. https://doi.org/10.1504/IJADS.2021.116004
Kansal, R., & Aggarwal, N. (2023). An empirical analysis in analysing the critical factors influencing the health insurance business in achieving sustainable development using structural equation model [IEEE IAS Global Conference on Emerging Technologies (GlobConET)]. https://doi.org/10.1109/GlobConET56651.2023.10150027
Levantesi, S., Menzietti, M., & Nyegaard, A. K. (2024). De-risking in multi-state life and health insurance. Annals of Actuarial Science, 18(2), 423-441. https://doi.org/10.1017/S1748499524000083
Lozano, S., & Khezri, S. (2023). A new interval efficiency measure in data envelopment analysis based on efficiency potential. IMA Journal of Management Mathematics, 34(1), 123-142. https://doi.org/10.1093/imaman/dpab040
Meskini, F. Z., & Aboulaich, R. (2020). A new cooperative insurance based on blockchain technology: six simulations to evaluate the model [International Conference on Intelligent Systems and Computer Vision (ISCV)]. https://doi.org/10.1109/ISCV49265.2020.9204170
Mitra, A., Gochhait, S., Obaid, A. J., & Alkhafaji, M. A. (2023). A strategic data protection plan for the healthcare industry - A review [8th International Conference on Communication and Electronics Systems (ICCES), 1784–1788]. IEEE. https://doi.org/10.1109/ICCES57224.2023.10192830
Molnár, B., Pisoni, G., Kherbouche, M., & Zghal, Y. (2023). Blockchain-based Business Process Management (BPM) for finance: the case of credit and claim requests. Smart Cities, 6(3), 1254-1278. https://doi.org/10.3390/smartcities6030061
Nai, W., Yang, Z., Wei, Y., Sang, J., Wang, J., Wang, Z., & Mo, P. (2022). A comprehensive review of driving style evaluation approaches and product designs applied to vehicle usage-based insurance. Sustainability, 14(13). https://doi.org/10.3390/su14137705
Nelson, H. (2024). Writing and reading new markets: insurance in Quebec, 1931-1960. Enterprise and Society, 25(2), 432-453. https://doi.org/10.1017/eso.2022.48
Papayiannis, G. I. (2023). A framework for treating model uncertainty in the asset liability management problem. Journal of Industrial and Management Optimization, 19(11), 7811-7825. https://doi.org/10.3934/jimo.2023021
Peng, S., Zhou, M., Yang, M., Mi, H., Cao, S., Wen, Z., Xu, T., Wang, H., & Liu, L. (2021). A dialogue-based information extraction system for medical insurance assessment. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 654-663. https://aclanthology.org/2021.findings-acl.58/
Pettersson, D., & Lillieskold, J. (2022). A literature review of frameworks for evaluating potentially disruptive innovation [Portland International Conference on Management of Engineering and Technology (PICMET)]. https://doi.org/10.23919/PICMET53225.2022.9882651
Ruiz López, H. R., Zambrano Farías, F. J., Sánchez Pacheco, M. E., & Correa Soto, S. R. (2023). Relación entre la antigüedad de la microempresa y su rentabilidad financiera: un análisis por conglomerados. Suma de Negocios, 14(31), 136-143. https://doi.org/10.14349/sumneg/2023.V14.N31.A5
Ryali, N., Manimegalai, V., Nikkhel, A., Nadh, P. G., Bandaru, Y. C., & Sai, P. V. (2024). A business strategy for insurance companies using deep learning for risk assessment and mitigation [2nd International Conference on Disruptive Technologies (ICDT), 66–70]. https://doi.org/10.1109/ICDT61202.2024.10489296
Sandner, K., Sieber, S., Tellermann, M., & Walthes, F. (2020). A lean six sigma framework for the insurance industry: insights and lessons learned from a case study. Journal of Business Economics, 90, 845-878. https://doi.org/10.1007/s11573-020-00989-9
Shieh, H.-S., Li, Y., Hu, J.-L., & Ang, Y.-Z. (2022). A comparison of efficiency of life insurance companies in mainland China and Taiwan using bootstrapped truncated regression approach. Cogent Economics and Finance, 10(1). https://doi.org/10.1080/23322039.2022.2043571
Sultana, N., Rahman, K. S., Zahidul Islam, K. M., & Ahmad, S. (2023). An exploration of bancassurance: standpoints of banks, insurance companies and customers in Bangladesh [4th International Conference for Emerging Technology (INCET), 1–7]. https://doi.org/10.1109/INCET57972.2023.10169976
Superintendencia de Compañías, Valores y Seguros. (2025). Ranking. https://appscvsmovil.supercias.gob.ec/ranking/reporte.html
Ullah, R., Asghar, I., Griffiths, M. G., Ballard-Smith, S., & Alonso, N. (2023). A data-driven approach for customized pay-as-you-drive insurance premiums [International Conference on Innovation, Knowledge, and Management (ICIKM), 26-30. IEEE]. https://doi.org/10.1109/ICIKM59709.2023.00014
Adelmann, M., Fernandez-Arjona, L., Mayer, J., & Schmedders, K. (2021). A large-scale optimization model for replicating portfolios in the life insurance industry. Operations Research, 69(4), 1134-1157. https://doi.org/10.1287/opre.2020.2098
Agarwal, D., & Tripathi, K. (2022). A framework for structural damage detection system in automobiles for flexible insurance claim using IOT and machine learning. International Mobile and Embedded Technology Conference -MECON, 5-8. https://doi.org/10.1109/MECON53876.2022.9751889
Alwis, S., & Jinasena, T. M. K. K. (2022). A blockchain-based decentralized insurance platform. International Research Conference on Smart Computing and Systems Engineering - SCSE, 5, 137-142. https://doi.org/10.1109/SCSE56529.2022.9905219
Arabmaldar, A., Sahoo, B. K., & Ghiyasi, M. (2023). A generalized robust data envelopment analysis model based on directional distance function. European Journal of Operational Research, 311(2), 617-632. https://doi.org/10.1016/j.ejor.2023.05.005
Armenia, S., Angelini, M., Nonino, F., Palombi, G., & Schlitzer, M. F. (2021). A dynamic simulation approach to support the evaluation of cyber risks and security investments in SMEs. Decision Support Systems, 147(113580). https://doi.org/10.1016/j.dss.2021.113580
Dash, S., Panigrahi, B. S., Sanikommu, V. V. B. R., Madhavi, B. K., & Sahoo, S. K. (2024). A comparative analysis of different machine learning techniques for medical insurance premium prediction [1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU), 1–6. IEEE]. https://doi.org/10.1109/IC-CGU58078.2024.10530731
Denkowska, A., & Wanat, S. (2021). A dynamic MST-deltaCoVaR model of systemic risk in the European insurance sector. Statistics in Transition New Series, 22(2), 173-188. https://doi.org/10.21307/STATTRANS-2021-022
Dutta, H., & Bhaumik, P. (2023). A blockchain based sustainable digital insurance business parametric solution architecture to protect Realtime QSR business interruption losses [7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings]. https://doi.org/10.1109/CSITSS60515.2023.10334071
Eckert, J., Graf, S., & Kling, A. (2021). A measure to analyze the interaction of contracts in a heterogeneous life insurance portfolio. European Actuarial Journal, 11(1), 87-112. https://doi.org/10.1007/s13385-020-00225-2
Farhat, D., & Awan, M. S. (2021). A brief survey on ransomware with the perspective of internet security threat reports [9th International Symposium on Digital Forensics and Security (ISDFS), 1–6]. https://doi.org/10.1109/ISDFS52919.2021.9486348
Garmdareh, M. S., Neysiani, B. S., Nogorani, M. Z., & Bahramizadegan, M. (2023). A machine learning-based approach for medical insurance anomaly detection by predicting indirect outpatients’ claim price [9th International Conference on Web Research - ICWR]. https://doi.org/10.1109/ICWR57742.2023.10139290
Guerar, M., Merlo, A., Migliardi, M., Palmieri, F., & Verderame, L. (2020). A fraud-resilient blockchain-based solution for invoice financing. IEEE Transactions on Engineering Management, 67(4), 1086-1098. https://doi.org/10.1109/TEM.2020.2971865
Herrmann, H., & Masawi, B. (2022). Three and a half decades of artificial intelligence in banking, financial services, and insurance: a systematic evolutionary review. Strategic Change, 31(6), 549-569. https://doi.org/10.1002/jsc.2525
Hogan, K. M. (2020). A global comparison of corporate value adjustments to news of cyber-attacks. Journal of Governance and Regulation, 9(2), 34-44. https://doi.org/10.22495/jgrv9i2art2
Huang, Y.-K., & Chuang, F.-C. (2020). A brief on risk management and application models of unmanned aerial vehicles [3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII)]. https://doi.org/10.1109/ICKII50300.2020.9318985
Jang, J., Qu, Y., Zhao, H., & Dassios, A. (2023). A Cox model for gradually disappearing events. Probability in the Engineering and Informational Sciences, 37(1), 214-231. https://doi.org/10.1017/S0269964821000553
Jarraya, B. (2021). A new optimization approach to assess optimal asset allocation in European non-life insurance companies. International Journal of Applied Decision Sciences, 14(4), 461-476. https://doi.org/10.1504/IJADS.2021.116004
Kansal, R., & Aggarwal, N. (2023). An empirical analysis in analysing the critical factors influencing the health insurance business in achieving sustainable development using structural equation model [IEEE IAS Global Conference on Emerging Technologies (GlobConET)]. https://doi.org/10.1109/GlobConET56651.2023.10150027
Levantesi, S., Menzietti, M., & Nyegaard, A. K. (2024). De-risking in multi-state life and health insurance. Annals of Actuarial Science, 18(2), 423-441. https://doi.org/10.1017/S1748499524000083
Lozano, S., & Khezri, S. (2023). A new interval efficiency measure in data envelopment analysis based on efficiency potential. IMA Journal of Management Mathematics, 34(1), 123-142. https://doi.org/10.1093/imaman/dpab040
Meskini, F. Z., & Aboulaich, R. (2020). A new cooperative insurance based on blockchain technology: six simulations to evaluate the model [International Conference on Intelligent Systems and Computer Vision (ISCV)]. https://doi.org/10.1109/ISCV49265.2020.9204170
Mitra, A., Gochhait, S., Obaid, A. J., & Alkhafaji, M. A. (2023). A strategic data protection plan for the healthcare industry - A review [8th International Conference on Communication and Electronics Systems (ICCES), 1784–1788]. IEEE. https://doi.org/10.1109/ICCES57224.2023.10192830
Molnár, B., Pisoni, G., Kherbouche, M., & Zghal, Y. (2023). Blockchain-based Business Process Management (BPM) for finance: the case of credit and claim requests. Smart Cities, 6(3), 1254-1278. https://doi.org/10.3390/smartcities6030061
Nai, W., Yang, Z., Wei, Y., Sang, J., Wang, J., Wang, Z., & Mo, P. (2022). A comprehensive review of driving style evaluation approaches and product designs applied to vehicle usage-based insurance. Sustainability, 14(13). https://doi.org/10.3390/su14137705
Nelson, H. (2024). Writing and reading new markets: insurance in Quebec, 1931-1960. Enterprise and Society, 25(2), 432-453. https://doi.org/10.1017/eso.2022.48
Papayiannis, G. I. (2023). A framework for treating model uncertainty in the asset liability management problem. Journal of Industrial and Management Optimization, 19(11), 7811-7825. https://doi.org/10.3934/jimo.2023021
Peng, S., Zhou, M., Yang, M., Mi, H., Cao, S., Wen, Z., Xu, T., Wang, H., & Liu, L. (2021). A dialogue-based information extraction system for medical insurance assessment. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 654-663. https://aclanthology.org/2021.findings-acl.58/
Pettersson, D., & Lillieskold, J. (2022). A literature review of frameworks for evaluating potentially disruptive innovation [Portland International Conference on Management of Engineering and Technology (PICMET)]. https://doi.org/10.23919/PICMET53225.2022.9882651
Ruiz López, H. R., Zambrano Farías, F. J., Sánchez Pacheco, M. E., & Correa Soto, S. R. (2023). Relación entre la antigüedad de la microempresa y su rentabilidad financiera: un análisis por conglomerados. Suma de Negocios, 14(31), 136-143. https://doi.org/10.14349/sumneg/2023.V14.N31.A5
Ryali, N., Manimegalai, V., Nikkhel, A., Nadh, P. G., Bandaru, Y. C., & Sai, P. V. (2024). A business strategy for insurance companies using deep learning for risk assessment and mitigation [2nd International Conference on Disruptive Technologies (ICDT), 66–70]. https://doi.org/10.1109/ICDT61202.2024.10489296
Sandner, K., Sieber, S., Tellermann, M., & Walthes, F. (2020). A lean six sigma framework for the insurance industry: insights and lessons learned from a case study. Journal of Business Economics, 90, 845-878. https://doi.org/10.1007/s11573-020-00989-9
Shieh, H.-S., Li, Y., Hu, J.-L., & Ang, Y.-Z. (2022). A comparison of efficiency of life insurance companies in mainland China and Taiwan using bootstrapped truncated regression approach. Cogent Economics and Finance, 10(1). https://doi.org/10.1080/23322039.2022.2043571
Sultana, N., Rahman, K. S., Zahidul Islam, K. M., & Ahmad, S. (2023). An exploration of bancassurance: standpoints of banks, insurance companies and customers in Bangladesh [4th International Conference for Emerging Technology (INCET), 1–7]. https://doi.org/10.1109/INCET57972.2023.10169976
Superintendencia de Compañías, Valores y Seguros. (2025). Ranking. https://appscvsmovil.supercias.gob.ec/ranking/reporte.html
Ullah, R., Asghar, I., Griffiths, M. G., Ballard-Smith, S., & Alonso, N. (2023). A data-driven approach for customized pay-as-you-drive insurance premiums [International Conference on Innovation, Knowledge, and Management (ICIKM), 26-30. IEEE]. https://doi.org/10.1109/ICIKM59709.2023.00014