Risk analytics for financiers
De-risk renewable energy investment with Renew Risk’s science-driven analytics.
Quantify and manage the financial implications of natural disasters
Catastrophe risk models are underutilised in the financial markets. Historically viewed as a tool for insurers, their popularity has grown in recent years due to the increasing demands of regulatory compliance and climate stress testing.
For investors and lenders with prospective or existing renewable energy infrastructure within their portfolio, the benefits of utilising risk models are numerous.
Renew Risk’s advanced risk analytics enables financiers to optimise their existing renewable infrastructure investments through risk-based capital allocation and the creation of resilient strategies based on science-based loss projections.
With portfolio diversification key to avoiding a concentration of risk, those looking to increase their presence in renewables can benefit from risk exposure insights that highlight where existing risk exists within their portfolio, future-proofing investment decisions against the damage of a single or aggregated natural disaster.
Armed with our data, investors and lenders can speed up credit decisions and accelerate the energy transition.
Explore our product portfolio
Navigate portfolio vulnerability with science-driven risk insights
Tailormade for renewables
Renew Risk is the only organisation focused on risk modelling and analytics for renewable energy infrastructure.
Customisable
Our products capture additional per-asset costs of repair and reinstatement for tailored model loss estimates.
Comprehensive analysis
In-depth risk insights for all assets including substations, cables and foundations, with modifiers including soil type and asset age.
Science driven
Underpinned by vulnerability calculations by world-leading experts.
Regionally calibrated
Built using at-site calibrations.
Industry validated
Our models undergo extensive validation, including comprehensive testing by market-leading early adopters who provide invaluable feedback on the model’s functionality and performance.
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