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Net Single Premium Estimation for Credit Life Insurance under Floating Interest Rates Using the Cox-Ingersoll Ross (CIR) Stochastic Model and Amortization Method Azzah Nailah Salsabila; Muhammad Hanif Faridy
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i1.1238

Abstract

Credit life insurance is designed to protect lenders against the risk of loan default arising from the death of borrowers during the loan period. In practice, premium determination for credit life insurance often assumes constant interest rates and does not fully account for demographic risk factors, which may lead to inaccurate pricing. This study aims to estimate the net single premium of credit life insurance by incorporating both borrower-specific mortality characteristics and floating interest rate dynamics under a stochastic framework. The loan interest rate is assumed to follow a floating structure linked to the BI 7-Day Reverse Repo Rate, which is modeled using the Cox–Ingersoll–Ross stochastic interest rate model to capture mean-reverting behavior and ensure non-negative interest rates. Loan repayment is structured through a monthly amortization scheme, resulting in a decreasing insurance benefit equal to the outstanding loan balance at the time of death. Mortality risk is evaluated using the Indonesian Mortality Table IV, with monthly death probabilities derived under the Uniform Distribution of Death assumption to accommodate fractional-age valuation. The actuarial present value of insurance benefits is computed by discounting the outstanding loan balance for each month and weighting it by the corresponding probability of death. The expected value of this random present value yields the net single premium. Numerical illustrations demonstrate that premiums increase with borrower age and are higher for male borrowers than for female borrowers of the same age, reflecting underlying mortality differences. Furthermore, the use of floating interest rates leads to annual adjustments in loan installments, which directly influence the evolution of insured benefits and premium values. Overall, the results indicate that integrating stochastic interest rate modeling with demographic mortality structure produces a more accurate and risk-reflective estimation of credit life insurance premiums, particularly in environments where floating interest rates are applied.