For more than two decades, Risk Frontiers has delivered a host of catastrophe solutions to empower disaster risk decisions. Risk Frontiers’ local and global experience, expertise and science-driven research have been utilised by the global insurance industry and critical infrastructure providers as well as all levels of government and large organisations.
Risk Frontiers specialises in qualitative and quantitative research with communities, emergency management practitioners, professionals and policy makers. We cover the social, economic and psychological aspects of risk reduction and climate change adaptation.
Risk Frontiers has a great deal of experience in delivering a wide diversity of consulting projects for commercial, infrastructure and government clients relating to hazard analysis, social research, risk management, resilience planning, policy development and risk assessment.
Risk Frontiers has been at the forefront of the data, scientific and analytical revolution that has swept through the insurance sector over the past 20 years.
Risk Frontiers has a core team of GIS specialists able to advise on the best approach to solving any risk problem and the most effective way of expressing and automating that risk profile.
Post-event reconnaissance, statistical analysis of event records and numerical simulation underpin our suite of models and allows for the development of hazard and exposure specific fragility functions.
Risk Frontiers is dedicated to world-class hazard and risk research and mentoring of postgraduate students. Our research strengths include meteorology, seismology, volcanology, numerical hazard simulation, geo-spatial analysis and social science.
Disruption is a key risk that can negatively affect an organisation’s bottom line or a community’s functioning. Resilient organisations and communities are better able to cope and adapt to disruption, ultimately enhancing sustainability and prosperity.
Machine learning (ML) methods are famously disrupting every field they get applied to, and many believe we are just at the beginning of a major revolution that will spare no industry. At the heart of the concept is the assumption that, if presented with enough relevant examples, ML algorithms can learn to predict a given outcome without any need for a modeller to explicitly specify any rule.