Fuzzy historical graph pattern matching a NoSQL graph database approach for fraud ring resolution

Abstract

Graphs have been studied and used for many years as they allow to represent in an efficient manner real data such as biological or social data. Graph databases have recently emerged within the NoSQL framework and are implemented in systems like Neo4J, OrientDB, etc. Recent works have shown that the management of history is crucial in such systems. In this paper, we show how such historical graph databases can be queried in order to retrieve fraud rings, also known as fraud cycles. Frauds are indeed often based on sophisticated chains of successive transactions (money, communications, etc.). We thus claim that the indirect link between fraudsters can be retrieved by considering historical NoSQL graph databases. We study how the model of historical NoSQL databases can be extended for better address this goal and we propose the associated queries that have been tested on a synthetical database.