APPLICATION OF EXPECTED CREDIT LOSS MODEL AND MARKOV CHAIN TO CALCULATE NET SINGLE PREMIUM OF UNSECURED CREDIT INSURANCE

  • Hansen Juni Lieus Department of Business Mathematics, Faculty of STEM, Prasetiya Mulya University, Indonesia
  • Devin Tedja Department of Business Mathematics, Faculty of STEM, Prasetiya Mulya University, Indonesia
  • Vanessa Joewita Department of Business Mathematics, Faculty of STEM, Prasetiya Mulya University, Indonesia
  • Agus Sofian Eka Hidayat Department Actuarial Science, Faculty Business, President University, Indonesia
  • Alexander R. J Silalahi Department of Business Mathematics, Faculty of STEM, Prasetiya Mulya University, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss4pp2161-2170
Keywords: Cohort, Expected Credit Loss, Markov Chain, Probability of Disbursed Claim, Unsecured Credit Insurance

Abstract

Transferring credit risk to an insurance company is a way to mitigate risk. Premiums should be calculated accurately to attain economic value for both the lender and the guarantor. The aim of this study was to determine the net single premium (NSP) values for an unsecured credit insurance product using the expected credit loss (ECL) method from IFRS 9. This study used data generated through simulation of insurance policies issued in 2015 or 2016. Their state classifications were monthly observed from 2016 to 2020. The probability of disbursed claim (PDC) parameter replaced the probability of default parameter on the ECL model, whereas the PDC model was constructed based on the components of a state-transition probability matrix, obtained with the Markov chain approach using the cohort method:  = 0.999181,  = 0.000130, and  = 0.000689. The PDC model validation showed relatively decent results, whereas MSE = 2.457% and zs = 0.608 with a = 5%. These results indicated that the PDC model was a good fit to calculate ECL. 5,000 iterations were done as part of the cash flow simulation process, whereas debtors’ loan amounts were randomly generated during each iteration, and the average NPV of these iterations was -Rp564.419.305. Based on model sensitivity analysis, cash flow values were most sensitive to the variable used to construct the PDC model (). Thus, the 5,000-iteration process was repeated with the newly adjusted PDC value, which were  = 0.998924 and  = 0.000946. The new average NPV of these iterations was Rp409,877,840, indicating that the constructed ECL model was a good fit to calculate NSP values for unsecured credit insurance products.

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Published
2023-12-19
How to Cite
[1]
H. Lieus, D. Tedja, V. Joewita, A. Hidayat, and A. Silalahi, “APPLICATION OF EXPECTED CREDIT LOSS MODEL AND MARKOV CHAIN TO CALCULATE NET SINGLE PREMIUM OF UNSECURED CREDIT INSURANCE”, BAREKENG: J. Math. & App., vol. 17, no. 4, pp. 2161-2170, Dec. 2023.
Issue
Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Hansen Juni Lieus, Devin Tedja, Vanessa Joewita, Agus Sofian Eka Hidayat, Alexander R. J Silalahi

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