Using Technology to Encourage Critical Thinking and Optimal Decision Making in Risk Management Education
Garvey, John, Buckley, Patrick, Risk Management and Insurance Review
This article draws a link between the risk management failures in the financial services industry and the educational philosophy and teaching constraints at business schools. An innovative application of prediction market technology within business education is proposed as a method that can be used to encourage students to think about risk in an open and flexible way. This article explains how prediction markets also provide students with the necessary experience to critically evaluate and stress-test quantitative risk modeling techniques later in their academic and professional careers.
The financial and economic crisis that we continue to endure presents a serious challenge to the teaching and learning strategies employed in universities. Business graduates are expected to have a deep knowledge of the theory that forms the bedrock of the financial system as well as the mathematical competence necessary to apply asset pricing and risk management methodologies. However, the techniques and models used to control and manage risk are often taught in an environment that does not provide sufficient space and time for rigorous debate and critical analysis.
Students are often presented with subject knowledge in a way that the content has already been carefully selected and sequenced by their lecturer. The education literature already notes that this method of providing teaching materials prevents an active learning dynamic (Kinchin, Chadha, and Kokotailo, 2008). In the early stages of university business programs, the often large class sizes limit the opportunity for students to engage in realistic decision-making scenarios. The project described in this article is founded on providing students with an early testing ground for the application of risk management theory. The creation of a closed market populated by other class members is a departure from the traditional approach where students learn about the use of statistical measures of risk such as standard deviation and correlation and become familiar with their practical relevance to industry standards such as beta or value-at-risk through lectures and formulaic practice. The application by students of statistical methods in a real-time insurance market demonstrates the relevance of human behavior and expectations in driving market dynamics.
Beyond the confines of the university campus we can observe increasing pressure on the insurance system to underwrite risks previously considered uninsurable. The insurance system is absorbing potential claims associated with catastrophic risks posed by natural hazards such as earthquakes and windstorms and in some cases man-made hazards associated with technologies as nuclear, biological, and chemical engineering. This trend is occurring at a time when the industry is beset by narrower profits as large volumes of capital compete for a limited range of risks. There is now a large category of insured risks that are being priced and underwritten using techniques that do not apply the age-old mathematical comforts of the law of large numbers and the central limit theorem.
This article describes an innovative teaching mechanism that has been applied to a large group of undergraduate students at the Kemmy Business School, University of Limerick. We document how the teaching and learning environment has been dramatically changed through the introduction of a prediction market where students estimate and transfer insurance risks. The market structure encourages students to think about risk outside the confines of the lecture theatre. The competitive nature of the market and the sparse historical information that is made available require students to explore the strengths and limitations of traditional risk management techniques. Importantly, the students' participation in this dynamic and complex environment coincides with their introduction to formal ways of thinking about risk management. …