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 many new opportunities, services and benefits for humanity. But at the same time, the underlying networks have created pathways through which potentially hazardous and damaging incidents can spread 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, this article focuses 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.

Keywords: Big data, risk forecasting, systemic risk, predictive analytics, machine learning, public security risk, surveillance, predictive policing, political instability, national security risk, conflict prediction, sustainable development risk, SDG indicator, environmental risk, climate change, financial

JEL Classification: C53, C55, F52, F47, F37, G32, H56