Managing coastal risk at the regional scale requires a prioritization of resources along the shoreline. A transparent and rigorous risk assessment should inform managers and stakeholders in their choices. This requires advances in modelling assessment (e.g., consideration of source and pathway conditions to define the probability of occurrence, nonlinear dynamics of the physical processes, better recognition of systemic impacts and non-economic losses) and open-source tools facilitating stakeholders' engagement in the process.This paper discusses how the Coastal Risk Assessment Framework (CRAF) has been developed as part of the Resilience Increasing Strategies for Coasts Toolkit (RISC-KIT). The framework provides two levels of analysis. A coastal index approach is first recommended to narrow down the risk analysis to a reduced number of sectors which are subsequently geographically grouped into potential hotspots. For the second level of analysis an integrated modelling approach improves the regional risk assessment of the identified hotspots by increasing the spatial resolution of the hazard modelling by using innovative process-based multi-hazard models, by including generic vulnerability indicators in the impact assessment, and by calculating regional systemic impact indicators. A multi-criteria analysis of these indicators is performed to rank the hotspots and support the stakeholders in their selection.The CRAF has been applied and validated on ten European case studies with only small deviation to areas already recognised as high risk. The flexibility of the framework is essential to adapt the assessment to the specific region characteristics. The involvement of stakeholders is crucial not only to select the hotpots and validate the results, but also to support the collection of information and the valuation of assets at risk. As such, the CRAF permits a comprehensive and systemic risk analysis of the regional coast in order to identify and to select higher risk areas. Yet efforts still need to be amplified in the data collection process, in particular for socio-economic and environmental impacts.

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