Graph Mining for Detection of a Large Class of Financial Crimes

Financial crime perpetrators use many different and sophisticated types of schemes, techniques and transactions to accomplish their goals. However, for a large class of financial crimes, such as doing harm to a company, they cannot escape a powerful principle: illegal proceeds have to return to or be under control of managers to achieve a personal gain. This circular flow of transaction attributes is characteristic of another type of financial crimes: such as a VAT carousel or a Polish fuel mafia scheme. In this work we propose a minimal model of descriptions of a doing harm to a company crime, combined with money laundering. Such a model uses sufficient ontology to build evidence and assign legal qualifications to criminal activities and nothing more. The scheme can be described by using 8 layers of concepts and relations that follow in logical order of uncovering a crime. For example, on the first level that describes money transfers there are only 6 parameters necessary assuming that certain operations can be grouped. Using conceptual graphs with subsumption and negation operations, one can reason on people involvement in a crime and choose between strategies of building a case. The model captures over 90% of relevant information for a typical use case of issuing fictitious invoices, the so called Hydra case and is able effectively reason over relevant facts, which means that legal qualification for this case is basically correct. To what extent the model can be generalized to more complex schemes will be a subject of a further study.