Risk Forecasting in the Light of Big Data

Abstract

Life in modern society is increasingly connected by networks that link the world around us and create numerous exciting opportunities, new services and advantages for humanity. Yet concurrently, these underpinning networks have provided routes by which potentially dangerous and harmful incidents can propagate quickly and worldwide. This complexity poses a considerable challenge for risk analysis and forecasting. Conventional methods of risk analysis tend to underestimate the probability and impact of risks (e.g. pandemics, financial collapses, terrorist attacks), as sometimes the existence of independent observations is wrongly assumed and cascading errors that can occur in complex systems are not considered. The purpose of this article is to assess critically the potential of big data to profoundly change the current capability for risk forecasting in diverse areas and the assertion that big data leads to better risk predictions. In particular, the focus is on big data implications for risk forecasting in the areas of economic and financial risks, environmental and sustainable development risks, and public and national security risks. The article concludes that big data and predictive analytics offer substantial opportunities for improving risk forecasting but may not replace the significance of appropriate assumptions, adequate data quality and continuous validation.

Keywords

• Big data
• risk forecasting
• systemic risk
• predictive analytics
• machine learning
• security risk