Investigation of graph databases suitable for work with big data while detecting money laundering and terrorism financing cases

Abstract

This paper discusses the currently popular graph database management systems (DBMSs) working with Big Data and can be used to store information obtained while dealing with money laundering and terrorist financing criminal (ML/FT) cases. The aim of this study is to choose a secure graph DBMS suitable for working with Big Data for such financial investigations. The authors consider the existing graph DBMSs, analyze and compare them with each other with special emphasis on the information security protection methods of stored data. The advantages and disadvantages of software products are studied and a comparison with the help of selected parameters characterizing system's ability to keep information secure is made. The results of the comparison are followed by detailed comments. On its basis the most convenient, flexible and up-to-date DBMS was chosen for usage while searching ML/FT cases. It was found that graph DBMSs are suitable for Big data tasks and as a final result JanusGraph was selected as a foreground DBMS in this project according to selected parameters.

Keywords

• money laundering, terrorism financing, ML/FT, information security, typology, Big Data, Database Management System (DBMS).