Introduction to Risk Modelling Challenges
Risk modelling in the renewable energy sector can be complex and challenging due to inconsistent and poor quality data. Issues such as unstructured reports, incomplete information, duplicate records, and varying data sources can impede the accuracy of insurance risk models. This leads to a lack of efficiency in incorporating data into the analysis, making it difficult to assess project-specific risks and develop effective mitigation strategies.
Clir's Solution using Artificial Intelligence and Data Enrichment
Clir offers a robust solution by leveraging artificial intelligence and anonymized claims and operations data to enhance risk modelling in the renewable energy sector. By applying machine learning techniques to label and enrich risk data, Clir can improve data quality and provide a deeper understanding of risks associated with wind and solar projects. This enhanced data analysis allows for the development of more robust mitigation strategies, leading to better risk management practices.
Data Sources and Risk Modelling Insights
Clir's risk modelling data integrates various sources, including insurance claims and policies, SCADA and event data, publicly available environmental data, and Clir's own knowledge base of component technologies. By analyzing these diverse data sets, Clir can offer insights into the claims history of assets, quantify business interruption losses, model the impact of downtime, and provide natural catastrophe risk assessments. This comprehensive approach provides a holistic view of risk factors affecting renewable energy projects.
Benefits of Deeper Understanding and Accurate Risk Submissions
Through collaboration with customers and industry partners, Clir has developed a leading methodology for understanding wind and solar claims. This methodology covers common causes, severity assessments, fault identifications, mitigation measures, and predictions of future claims. By enhancing risk submissions with factors like age, technology, OEM, service contracts, and contractual structure, Clir enables owners and insurers to better understand site-specific attritional risk rates. This leads to more accurate risk submissions tailored to each project's unique characteristics.
Improving Natural Catastrophe Modelling and Risk Severity Predictions
Clir's risk modelling specifically focuses on improving natural catastrophe modelling for wind and solar assets. By considering project-specific information, project location, technology, and mitigation practices, Clir enhances risk assessments to provide better insights into site-specific natural catastrophe risks. Additionally, Clir's risk severity predictions are strengthened by analyzing site-specific O&M contracts, technology inspection reports, and service agreements, enabling a more accurate prediction of risk severities on projects.
Empowering Renewable Energy Projects with Clir's Risk Modelling
By leveraging Clir's advanced risk modelling capabilities, owners and insurers in the renewable energy sector can make more informed decisions about risk management and mitigation strategies. Clir's comprehensive and data-driven approach ensures that projects are better protected against potential risks, leading to improved operational efficiency and reduced financial exposures. Contact Clir today to learn how their risk modelling solutions can enhance your renewable energy projects.