Systemic risk has become a widely observed and thoroughly researched topic in the years following the 2008 global financial crisis. A number of international and European financial stability bodies have been established in its aftermath with objectives of safeguarding financial stability and monitoring systemic risk. However, both practitioners and academics still struggle to produce a uniform definition of systemic risk itself. Indeed, even though this topic has been widely researched thus far, there are still many open challenges when it comes to identifying, assessing and monitoring systemic risk. For instance, many indicators of systemic risk are shown not to be effective in predicting the upcoming financial crises. In order to address such gaps, both researchers and practitioners increasingly use new information and digital technologies to tackle existing discrepancies and generate more accurate analyses. The prerequisites for this are big data, proper IT infrastructure and highly skilled personnel. The utilization of business intelligence tools and advanced analytics enable the creation of new systemic risk measures, more effective systemic risk monitoring and the automation of data collection and risk processing. This paper discusses the prerequisites for the implementation of BI and AA solutions, its limitations and potential benefits for systemic risk supervision. An example from the Croatian banking and non-banking financial sector serves to display a project-based example of systemic risk supervision using interactive dashboards and innovative visualization techniques. Additionally, the possible applications of advanced analytics are discussed, including machine learning and artificial intelligence for systemic risk supervision.