Pension risk management and its effect on the financial performance of pension firms in Uganda
Abstract
The objective of this study was to evaluate the impact of risk management on the financial performance of pension firms in Uganda. Therefore, it aims to find out whether risk management practices (risk identification, risk assessment, risk mitigation and risk management implementation and monitoring) have a significant relationship on the financial performance of pension firms in Uganda. The study used both primary and secondary data where primary data was collected using a questionnaire and 15 questionnaires were sent out to 15 different pension firms. Secondary data was obtained from Annual Retirement Benefits Sector Report published by URBRA and a Cost-Income Ratio was analysed for the years 2017 to 2019. The analysis of both primary and secondary data was done using StataSE 15 where a frequency distribution, oneway ANOVA tests and logistic regression. The results were presented using tables and chats. The study established that most of the pension firms in Uganda had adopted risk management practices in their operation and this had a strong impact on their financial performance. Risk mitigation was found to be the highest contributing to financial performance followed by, risk assessment, risk management implementation and identification respectively. The study concludes that there is a positive relationship between the adoption of risk management practices and the performance of pension firms in Uganda. The study recommends that pension companies in Uganda should ensure cost-effective measures for timely risk identification and mitigation, increase risk assessment measures in order to see if they can still operate under occurrence of events and continuity after occurrence, increase risk mitigation measures to reduce exposure to and impact of risks on pension firms. Finally, pension firms in Uganda should adopt Enterprise Risk Management (ERM), a current international leading practice which incorporates other risk quantification models.