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Submitted
Oct 19, 2023
Accepted
Aug 5, 2024
Published
Sep 4, 2024
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Abstract

Ruin theory is commonly used to predict the likelihood of bankruptcy for an insurance company and relates to the rate of surplus of the insurance company for the insurance policy portfolio. Considering the change in the insurance fund from time to time, the timing of the occurrence of a number of claims is highly taken into account. Ruin theory is necessary so that companies can anticipate and detect bankruptcy early. One way to help insurance companies minimize their bankruptcy chances is through reinsurance. In this paper, will discuss about application of ruin theory in computing two methods of reinsurance treaty, that is Quota Share Reinsurance and Excess of Loss Reinsurance to decide more effective method to minimize probability of ruin. Results show that Excess of Loss Reinsurance method more effective than Quota Share Reinsurance method to minimize ruin probability of insurance company.

Keywords

Ruin theory, Quota Share Reinsurance, Excess of Loss Reinsurance

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
1.
Dwi Susanti, Riaman, Badrulfallah, Dimas Apriliyanto. Quota Share Reinsurance and Excess of Loss Reinsurance Calculations Using Ruin’s Theory. EKSAKTA [Internet]. 2024Sep.4 [cited 2024Oct.12];25(03):289-301. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/468

References

  1. Babuna, P., Yang, X., Gyilbag, A., Awudi, D. A., Ngmenbelle, D., & Bian, D. (2020). The impact of Covid-19 on the insurance industry. International journal of environmental research and public health, 17(16), 5766.
  2. Dickson, D. C. (2016). Insurance risk and ruin. Cambridge University Press.
  3. Ntwali, A., Kituyi, A., & Kengere, A. O. (2020). Claims Management and Financial Performance of Insurance Companies in Rwanda: A Case of SONARWA General Insurance Company Ltd. Journal of Financial Risk Management, 9(03), 190.
  4. Garayeta, A., De la Peña, J. I., & Trigo, E. (2022). Towards a global solvency model in the insurance market: a qualitative analysis. Sustainability, 14(11), 6465.
  5. Delsing, G. (2022). Ruin theory for portfolio risk modeling in banking and insurance. GA Delsing.
  6. Francis, S. I. (2022). Testing applicability of Altman Z model in predicting financial distress of non-financial firms listed at the Nairobi security exchange (Doctoral dissertation, University of Nairobi).
  7. Manzoor, A., Kim, K., Pandey, S. R., Kazmi, S. A., Tran, N. H., Saad, W., & Hong, C. S. (2021). Ruin theory for energy-efficient resource allocation in UAV-assisted cellular networks. IEEE Transactions on Communications, 69(6), 3943-3956.
  8. Pavlova, K. P. (2005). Some Aspects of Discrete Ruin Theory. Library and Archives Canada= Bibliothèque et Archives Canada, Ottawa.
  9. Lakdawalla, D., & Zanjani, G. (2012). Catastrophe bonds, reinsurance, and the optimal collateralization of risk transfer. Journal of Risk and Insurance, 79(2), 449-476.
  10. Zhao, Y., Lee, J. P., & Yu, M. T. (2021). Catastrophe risk, reinsurance and securitized risk-transfer solutions: A review. China Finance Review International, 11(4), 449-473.
  11. Cummins, J. D., Dionne, G., Gagné, R., & Nouira, A. (2021). The costs and benefits of reinsurance. The Geneva Papers on Risk and Insurance-Issues and Practice, 46, 177-199.
  12. Essadic, A., & Mouhssine, Y. (2018). Comparative study between the proportional approach and the non-proportional approach in retakaful. Вестник Белгородского университета кооперации, экономики и права, (4), 202-220.
  13. Wehrhahn, R. (2009). Introduction to reinsurance. primer series on insurance, 2.
  14. Hürlimann, W. (1999). Non-optimality of a linear combination of proportional and non-proportional reinsurance. Insurance: Mathematics and Economics, 24(3), 219-227.
  15. Le Courtois*, O., & Randrianarivony**, R. (2013). On the bankruptcy risk of insurance companies. Finance, 34(1), 43-72.
  16. Constantin, D., & Clipici, E. (2016). A new model for estimating the risk of bankruptcy of the insurance companies based on the artificial neural networks. International Multidisciplinary Scientific GeoConference: SGEM, 1, 3-10.
  17. Constantinescu, C., & Lo, J. (2013). Ruin Theory Starter Kit. Proceeding at GIRO, 1-7.
  18. Manzoor, A., Kim, K., Pandey, S. R., Kazmi, S. A., Tran, N. H., Saad, W., & Hong, C. S. (2021). Ruin theory for energy-efficient resource allocation in UAV-assisted cellular networks. IEEE Transactions on Communications, 69(6), 3943-3956.
  19. Zhang, J. (2017). Study of ruin probability in double Poisson risk model. In 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017) (pp. 1539-1543). Atlantis Press.
  20. You, H., Guo, J., & Jiang, J. (2020). Interval estimation of the ruin probability in the classical compound Poisson risk model. Computational Statistics & Data Analysis, 144, 106890.
  21. Rao, N. V., Atmanathan, G., Shankar, M., & Ramesh, S. (2013). Analysis of bankruptcy prediction models and their effectiveness: An Indian perspective. Great Lakes Herald, 7(2), 3-17.
  22. Jaki, A., & Ćwięk, W. (2020). Bankruptcy prediction models based on value measures. Journal of Risk and Financial Management, 14(1), 6.
  23. Bryan, D., Janes, T., & Tiras, S. L. (2014). The role that fraud has on bankruptcy and bankruptcy emergence. Journal of Forensic & Investigative Accounting, 6(2), 126-156.
  24. Saleh, M. M. A., Aladwan, M., Alsinglawi, O., & Salem, M. O. (2021). Predicting fraudulent financial statements using fraud detection models. Academy of Strategic Management Journal, suppl. Special, 20(3), 1-17.
  25. Meraou, M. A., Al-Kandari, N. M., Raqab, M. Z., & Kundu, D. (2022). Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims. Journal of Statistical Computation and Simulation, 92(5), 928-956.
  26. Madhira, S., & Deshmukh, S. (2023). Poisson Process. In Introduction to Stochastic Processes Using R (pp. 389-440). Singapore: Springer Nature Singapore.
  27. Lui, J. (2002). Estimating the probability of bankruptcy: A statistical approach. Leonard N. Stern School of Business, New York University.
  28. Štefko, R., Horváthová, J., & Mokrišová, M. (2021). The application of graphic methods and the DEA in predicting the risk of bankruptcy. Journal of Risk and Financial Management, 14(5), 220.
  29. Dorohan-Pysarenko, L., Rębilas, R., Yehorova, O., Yasnolob, I., & Kononenko, Z. (2021). Methodological peculiarities of probability estimation of bankruptcy of agrarian enterprises in Ukraine. Agricultural and Resource Economics: International Scientific E-Journal, 7(2), 20-39.
  30. Jaki, A., & Ćwięk, W. (2020). Bankruptcy prediction models based on value measures. Journal of Risk and Financial Management, 14(1), 6.