The last financial crisis triggered a large research effort in hope to find out why otherwise robust Risk Management systems failed to alert the enterprises of upcoming serious financial consequences. One of the reasons was sheer amount of data that couldn't be handled effectively. A detailed Operational Risk Registry contains anywhere from few hundred up to few thousand identified risks. Each risk seldom has only one trigger, raising the number of monitoring points to quantities which overwhelm the scope of regular risk audits and render the whole system inefficient. If a new risk threatening development is emerging, an automated system would be needed to react in time. But such a system is challenged by the fact that the monitoring ratios using financial data are tainted by time distortion as financial data lags behind the actual event. Nonfinancial trigger attributes lack common denominator to aggregate the status of the trigger. This paper proposes a ratio based trigger monitoring model where each trigger may be based on different unit of measure and data collected from both financial and operational nonfinancial segments, rendering automated data collection plausible. The model further introduces trigger's time behaviour into monitoring process, where linear progress is only a special case. Multiple triggers are easily aggregated as ratios are the common denominator. Finally, this model is integrated into the model of Multidimensional Pre-emptive Coordination, which propagates the risk status change alert both horizontally and vertically across all of the enterprise, focusing on interested shareholders.