Systemic risk identification, modelling, analysis, and monitoring: An integrated approach

Over the last fifteen years, computational intelligence applications have proliferated solutions to financial engineering problems, bringing a new fertile area of collaboration between professional engineering and financial communities. Computational systems based on machine learning techniques have become indispensable in virtually all financial applications, from portfolio selection to proprietary trading and risk management. The new challenges for the financial community involve working with the abundance of data from a variety of types that were not previously available and could now help solve financial problems proven hard to solve earlier. The bar has been raised with new regulatory, and compliance and risk management requirements. As the field is intensively evolving, we need a new set of tools and methods to face the challenges posed.