Enterprise risk analytics: Automatic analysis of risk factors from textual feedbacks

Abstract

There has been a growing need to automatically identify, extract and analyze risk related statements from textual data. In this paper, we have exploited natural language processing research to develop a risk analytics framework that processes human-reported risk statements to analyzes the enterprise risk description texts to classify them into valid and invalid risk categories, and perform analytics to extract information from the text pertaining to the different categories of risks and their possible cause and impacts. A manual annotation study from management experts using risk descriptions collected for a specific organization was conducted to evaluate the framework. The evaluation showed promising results for automated risk analysis and identification.