Published by Nick Griggs on
The first step for the students was to identify the major risks faced by the University and to assess the possible sources of data available to allow these risks to be quantified.
Following an introductory talk from two Barnett Waddingham actuaries, Lauren Allan and Lewys Curteis, the first step for the students was to identify the major risks faced by the University and to assess the possible sources of data available to allow these risks to be quantified.
Based on the results of this analysis, it was decided that the students would concentrate on two risks: financial risk and reputational risk. The students divided into three separate groups, with one group looking at financial risk and the other two groups looking at reputational risk.
The results of the project were presented by the students at Barnett Waddingham’s Liverpool office, with prizes awarded for the best contributors.
A summary of each of the group’s work is provided below:
Tuition fees form one of the main sources of the University’s income, and the volatility of this income is a major risk for the University. The first group therefore decided to assess how tuition fee income is affected by changes in the macroeconomic environment.
Tuition fee income is related to the number of students studying at the University, so the group decided to use a regression model to identify the macroeconomic factors that, historically, appeared to have the greatest effect on the number of students. This included GDPs, unemployment rates, inflation and exchange rates.
Using these macroeconomic factors, the group developed a model to forecast student numbers (and hence tuition fee income) under various scenarios, with particular focus on the impact that the Brexit decision could have on the University’s financial income.
The second group decided to consider the University’s position in the national league tables, which could be considered as a quantifiable measure of the University’s reputation. The group’s aim was to try to measure the risk of a fall in league table position for the University.
Based on a historical analysis of the correlation between a university’s league table ranking and the factors contributing to the ranking, the group identified three factors that they considered to be the most significant in influencing a university’s ranking.
Using these factors, the group developed a discrete-time Markov chain model to model how the University’s ranking might develop in future years, and identified the actions that the University could take to mitigate the risk of a fall in league table position.
As a significant risk for universities, the third group also decided to consider the impact of reputational risk. To do this, the group compared the University’s actual tuition fee income to the total expected tuition fee income (based on a regression model).
The group determined three reputational factors that could result in expected income being less than actual income, and calculated the amount of capital that the University could hold to mitigate the impact of these factors.
The group then forecast how this capital value might develop in the future years using a Vasicek model.
Given more time, the students would have liked to consider further risks faced by the University, the interdependencies between these risks, and possible ways in which the University could mitigate these risks. The projects showed the potential for universities and companies generally to quantify some of the risk they face. This information can then be used to better understand and therefore manage the risks they face at an operational and strategic level.