This report is a compilation of achievements and results to date, from the World Bank Crisis & Disaster Risk Finance Analytics program, on the integration of earth observation, big data, machine learning and artificial intelligence for disaster risk finance. This report presents a selection of projects from the Crisis Risk Finance Analytics program (CRFA), as part of the World Bank Crisis and Disaster Risk Finance work to apply innovative technology for risk finance applications. It presents the successes and challenges faced by the program to date, highlighting the potential of satellite imagery, Big Data, and advanced analytics techniques for disaster risk finance, as well as the implications of various governance and partnership models with the private and public sectors to bring some of these applications to scale.
This report has highlighted state-of-the-art applications of innovative data sources and analytical methods for DRF. The projects presented bring objective and practical benefits for policy-making and implementation on the ground, which contribute to increased financial resilience in the face of worsening and compounding climate shocks. These technologies were shown to:
- Increase data availability: Risk models are better equipped to understand and monitor risks using comprehensive coverage obtained through BD and EO, thereby providing critical risk information globally, in high-resolution, and in near-real-time.
- Enhance risk modeling: The analytical methods used to leverage these data sources improve the understanding of hazard estimates and risks, including systemic risks, allowing for better planning and forecasting.
- Support risk-informed policies and DRF solutions: As these technologies provide objective and near-real-time measures, financial response mechanisms can be triggered more quickly, improving tar-geted financial support for those most in need.