Distribution and Pattern of an Insurance Health Claim System: A Time Series Approach

Abstract

There is a continuous increase in health costs, thereby increasing pressure on individuals and consequently making the amounts claimed by the insured to be on the increase. In this study, data was collected from a large local insurance company in Zimbabwe for the period from January 2012 to December 2016. The aim of this study was to analyse the distribution and future pattern of insurance health claim system using time series approach. Akaike information criterion and Schwarz Bayesian criterion were used to select the adequate model through maximum likelihood estimation methods. ARIMA (0, 0, 0) (1, 0, 1) [12] is the model that was chosen to forecast claim amounts. The use of ARIMA models proves to be an excellent instrument for predicting and capturing the cost trend of health claims which can help in decision making to insurance companies.
 Keywords: ARIMA; Box-Jenkins; health insurance; time series.

Modelling and Forecasting Claim Payments of Tanzania National Health Insurance Fund

“…Globally, there is a problem of high costs for health care services (Jiying et al 2019). Health insurance bills have been dramatically increasing in recent years (Bertsimas et al 2008, Mashasha et al 2022. Most countries have reported a significant rise in health care expenses whose growth outpaces their gross domestic product (GDP) growth (Glassman andZoloa 2014, Jahanmehr et al 2022).…”

“…The predictions from the models showed a deficit would start happening in 2024. Mashasha et al (2022) analysed health insurance claim payment patterns in Zimbabwe using a seasonal ARIMA model based on monthly records from January 2012 to December 2016. The study used Seasonal ARIMA with monthly data but did not show to what extent the results could be trusted.…”