Financial Data is significantly constrained by customer privacy regulations such as GDPR, which hampers the possibility of collaboration between different stakeholders in financial problems such as optimising Anti-Money Laundering (AML) tools and reducing financial crime. Solutions based on Machine Learning (ML) are on the way, but unfortunately, the quality data required to train the models is unavailable. The three most significant drawbacks of using ML for AML are lack of 'labelled data', the 'imbalance class' of misbehaviour financial activity and finally, the evolving threat of finCrime that makes 'training datasets obsolete'. These drawbacks are derived from the unknown ‘hidden crime’ problem. We address this problem by creating digital synthetic twins of financial data significantly enriched for advanced solutions based on machine learning. We add to our models the known crime dynamics specially tailored to match undesirable scenarios in our financial ins