This research project called GRAMMAF, (GR)aph (A)nti-(M)oney laundering and (M)arket (A)buse (F)ramework, will aim to provide a scalable graph theoretical framework for AML/CTF (Anti-Money Laundering/Counter Terrorism Financing) and MAR (Market Abuse Regulations) analytics for financial transactions. The project will address two specific use cases. The first use-case is given by challenging customer datasets from our partner (LOGOS/iDETECT), involving several millions of transactions and users. The second case is related to modern FinTech and distributed ledgers, and targets the specific case of the Ripple network. These objectives are highly relevant for the financial industry. Compliance and regulations require financial actors to implement efficient AML/CTF and MAR controls, while the recent emergence of crypto-currencies and distributed ledger based value transfers have no suitable AML/CTF and MAR solutions available.