Forecasting Life Insurance Loss Reserves Using Time Series Analysis.

Purpose: To determine an accurate predictive model that captures trends and seasonality in life insurance claims. Methodology: Time Series Analysis is used to forecast Secondary data on life insurance claims. The researcher aims at using a non-actuarial and accurate predictive model that captures trends and seasonality in life insurance claims over time. The dataset, comprising monthly claim amounts from May 2018 to March 2024 obtained from an insurance company. Findings: Life insurance claims increases with time hence the insurance companies are to set up an appropriate reserve to cater for future occurrence. It is realized that ARIMA (2,1,1) is appropriate for modelling the life insurance claim amounts with a least Log Likelihood value of -766.99, AIC value of 1521.97, AICc value of 1522.6, and BIC value of 1530.91. An ACF plot and Ljung Box test on the residuals shows the residuals are free from autocorrelation and free from heteroscedasticity respectively and hence the model is a white noise and adequate for further analysis. The result of the twelve months forecast indicates an increase in the life insurance claims. Unique contribution to theory, practice and policy: Managers in the insurance companies should focus on risk management and reserve allocation of fund in order to meet short term and long term claims settlement.