Risk and valuation models in the financial sector are becoming increasingly computationally intensive due to growing data volumes, complex portfolios, and rising regulatory requirements. In particular, classical Monte Carlo methods can require substantial computation time for high-dimensional and nonlinear scenarios. At the same time, results need to be transparent, auditable, and available in a timely manner. The research project Q-Fin – Quantum-Enhanced Financial Risk Modelling investigates how quantum computing can support and accelerate key risk and valuation processes in the financial sector. The project focuses, among other areas, on calculating risk metrics such as Value-at-Risk and Expected Shortfall, generating complex market and stress scenarios, and optimizing portfolios. To this end, quantum-based methods are combined with classical high-performance computing and Monte Carlo approaches in hybrid workflows. The aim is to develop a modular quantum-classical software stack that can be integrated into existing financial processes and builds on the QC-Next reference architecture. In addition to quantum-based algorithms, Q-Fin develops components for the intelligent orchestration of classical and quantum computing resources, as well as methods for explainability and auditability. This is intended to enable quantum-computing-based analyses to be used transparently and traceably in regulated financial applications. The developed methods are evaluated together with the application partner Deka using real or realistic portfolio, ESG, and stress scenarios and are compared with classical approaches. The results are intended to identify the conditions under which hybrid Quantum-HPC approaches provide practical added value, while also contributing reusable components and best practices to the QC-Next ecosystem.
Partners
Forschungszentrum Jülich GmbH Deka Investment GmbH
