<![CDATA[Abstract. The growth of credit insurance in Indonesia has increased insurers’ exposure to default risk, particularly defaults arising from debtor mortality. In practice, mortality-related default risk is commonly assessed using static life tables or deterministic assumptions, which fail to capture stochastic mortality dynamics and heterogeneity among debtors. This limitation often leads to inaccuracies in estimating default probabilities and expected claims, potentially compromising pricing and reserving decisions. This study addresses this gap by developing an integrated framework that combines stochastic mortality estimation with credit default risk modeling. The primary contribution of this research is the synthesis of the PLAT stochastic mortality model with a Generalized Non-Linear Model (GNLM). The PLAT model is employed to capture age, period, and cohort effects, while the GNLM integrates these estimated mortality probabilities with loan-specific characteristics, such as loan amount, tenor, and underwriting year. Furthermore, mortality rates are estimated within a Quasi-Poisson regression framework to account for overdispersion in claim data. Our results indicate that default probabilities significantly increase with age and vary across underwriting cohorts, demonstrating that loan characteristics materially influence the magnitude of mortality-driven risk. This proposed framework provides a more robust actuarial basis for pricing, reserving, and risk management in credit insurance.]]>
