Mortality simulations for insured and general populations

TL;DR

This paper introduces a novel framework using IPF and Monte Carlo simulations to generate detailed mortality tables for insured and general populations, addressing data scarcity issues.

Contribution

It develops a new methodology combining IPF and Monte Carlo techniques to produce high-resolution, demographic-specific mortality data for public health and actuarial use.

Findings

Generated refined mortality tables incorporating multiple demographic factors

Enhanced life expectancy projections for targeted populations

Provided a scalable approach for mortality data simulation

Abstract

This study presents a framework for high-resolution mortality simulations tailored to insured and general populations. Due to the scarcity of detailed demographic-specific mortality data, we leverage Iterative Proportional Fitting (IPF) and Monte Carlo simulations to generate refined mortality tables that incorporate age, gender, smoker status, and regional distributions. This methodology enhances public health planning and actuarial analysis by providing enriched datasets for improved life expectancy projections and insurance product development.