A Comparative Analysis of Stochastic Approaches for Claims Reserving in Private Health Insurance

To guarantee the fulfillment of all claims, an insurance company must allocate enough funds to cover both current and future claims for active policies. The application of stochastic models has found extensive use in various domains of insurance and finance. However, their application in the context of private health insurance has been somewhat limited. To address this gap in existing knowledge, this paper aims to explore the application of stochastic methods to disease portfolios. The study involves dividing the developmental periods into semi-annual intervals and determining the most appropriate model for forecasting claim reserves. The research objectives include evaluating the effectiveness of the Mack, Clark LDF, and Clark Cape Cod methods in predicting claim reserves within a disease portfolio. Moreover, the study intends to compare these models to identify the most appropriate method for claim reserve calculations. The data source employed for this study comprises private health insurance claims data covering the period from 2018 to 2022. When compared to alternative methods, The Clark LDF method with Weibull distribution is far more accurate in calculating the reserve of claims compared with other similar methods. From the pool of models analyzed, the Mack model and Clark LDF model with Weibull distribution show the lowest reserve claims and standard errors. Based on these result analyses, the insurance company is recommended to explore other strategies for its fund distribution as opposed to maintaining only one substantial claim reserve.