In areas such as engineering, economics, and insurance, real-world systems are becoming increasingly complex to analyse due to their global scale as well as to the uncertainty and dynamic conditions that characterises realistic scenarios. This increasing complexity makes risk analysis and analytic (RA&A) methods more important than ever, since being able to design, develop, and operate real-live systems while assessing and reducing their risk of malfunctions or inefficiencies constitutes one of the most relevant challenges in our current society. RA&A methods and techniques have rapidly evolved over the last years. One factor that explains this development is outstanding and continuous improvement in software and computing power, which facilitates the use of hybrid algorithms combining risk/reliability principles with modern optimisation and simulation frameworks. Another factor is the increasing use of problem solving approaches that benefit from the so-called "big data" phenomenon. However, despite these significant advances in this scientific arena, there seems to be an important gap between theory and practice; most industrial sectors (including engineering, economics, and insurance) are only starting to employ the full potential of state-of-the-art scientific advances in RA&A.