Systemic risk is recognised as a crucial risk component in financial markets which both regulators and market players are interested in quantifying. The number of financial transactions happening every second is ever-increasing, occurring on a variety of marketplaces, performed by different market players, and related to more and more interdependent complex instruments.  Ensuring financial stability and monitoring systemic risk require making sense of multiple data structures and data sources, describing our societies and economies, not only our markets. Stochastic modeling remains a fundamental benchmark, but complex market behaviours require data-driven modeling hardly captured by standard theory.  Network theory is a powerful framework for modeling dynamic multi-layered relationships between financial institutions and for training non-linear multivariate models -including machine learning ones- needed to capture the complex interdependencies of financial variables and to provide timely and informative indicators about the financial system.