Under this research topic, an unprecedented collection of innovative data quality methodologies and data augmentation techniques will be applied to a wide variety of industry-relevant datasets. Overcoming the obstacles of data quality and availability (through novel or extended large language models, deep generation of data, and anomaly- and dependence detection models baked on network concepts) will contribute to the literature and further provide a valuable tool for the European Finance industry to enhance product offerings, reduce financial market risks, and work toward a European financial data space. With quality data available and the ability to detect dependencies in high dimensions, high frequency, and high veracity financial data, DIGITAL is in a position to move towards a European financial data space and make use of substantially more data sources than today.