Machine Learning Approaches to Predictive Risk Management in Digital Ecosystems
Annotatsiya
Digital ecosystems are becoming more and more governed by continuous connectivity, high data velocity and tightly coupled services where risk spreads at a faster pace as compared to traditional enterprise risk management processes. With these kinds of environments, disruptions in operation, cyber attacks, failures of compliance and financial instability are not often present in isolation, revealing the constraints of risk management practices that are reactive and fragmented. The fundamental weakness of current AI-based risk management methods is that they are domain specific and model based in their optimization that is in most cases unable to accommodate cross-domain relations, temporal volatility, and decision level relevancy. This paper hypothesizes a combined machine learning-predicted risk management system which considers risk as a system-level phenomenon. The framework combines the heterogeneous risk signals, adaptive domain weighting and combines predictive output to a composite risk index intended to be used to make continuous and governance responsive decisions.