| Abstract [eng] |
Every business is associated with long-term uncertainty and the risk of incurring unacceptable losses. The life insurance industry is no exception. Here, the primary risk arises from the variability of mortality rates over time, as calculations of insurance company reserves and pricing are based on customers mortality data. Therefore, periodic mortality analysis is crucial for actuaries. Key steps in mortality analysis include choosing the data time frame, accounting for randomness in the data, selecting and graduating the data with an appropriate mortality law, using the standard mortality table interpolation method, and establishing secure confidence intervals for mortality norms. These steps are essential for a critical evaluation of mortality norms, ensuring a reliable and profitable operation in the life insurance sector. Given the absence of a universally perfect methodology to address these challenges, this work explores a specifically chosen algorithm designed to reliably assess clients' mortality indicators based on their age. |
