A general framework for the identification and categorization of risks: an application to the context of financial markets
Risk represents an important part of human interaction and must be taken into account in decision-making processes in various fields of activity and research. Additionally, failure to consider or be unaware of certain risks can lead to inappropriate decision-making processes and inadequate risk management practices that can negatively impact business performance. This article is, to our knowledge, the first to develop an algorithm-based and generally applicable framework that generates a complete and integrated identification and categorization scheme of certain risks using text mining and machine learning approaches. Automatique. To demonstrate the applicability of our framework, we apply our approach to the financial market context, identify 193 financial market risks and classify them into five categories using common machine learning techniques. To assess general applicability, we further apply our derived framework to the information systems context. Finally, we obtain strong indications of the robustness and superiority of our derived framework by comparing it to more manual risk identification techniques and other clustering approaches.