| Risk management stage | Contribution of AI |
| Risk identification when working with natural gas | • Cleaner risk identification and obtaining results in real time. It is implemented on the basis of the information received from the monitoring network in the gas infrastructure and refers to location and technical data. • Data obtained from various early warning systems for natural disasters, such as GIS (geographic information system) [13]. • Scanning and filtering relevant external information of third parties and linking it to existing assets. |
| Risk analysis and assessment | • Increasing the transparency in the correlation of the risks and the total risk exposure; • Risk assessment with a wider data set; • Real-time visibility of changing total gas transmission and storage exposure. • Constant validation of the model by collecting new data and detecting anomalies in data points and correlations. |
| Impact on risk | • Faster response time to risk reduction measures; • Better conditions for risk transfer, thanks to the large amount of available data and the machine learning algorithm. |
| Risk monitoring and control | • Improves risk protection; • Avoid accidents and better protect assets; • Faster response time to relevant risk events; • Greater resilience. |