Visual querying and analysis of temporal fiscal networks

Abstract

We present TeFNet, a new system for the visual analysis of temporal networks in the fiscal domain, aimed to contrast tax evasion, fiscal frauds, and money laundering. The design of TeFNet has been driven by domain experts (tax officers) and the system is currently used by the Italian revenue agency, Agenzia delle Entrate. TeFNet is based on a powerful visual query language that allows users to easily define suspicious patterns involving temporal constraints. To efficiently execute graph pattern matching algorithms on large networks, TeFNet exploits modern graph database technologies. Besides its visual query language, TeFNet is equipped with graph visualization techniques to quickly convey time-varying information during an interactive exploration of the subgraphs that match a desired pattern. Both the query language and the visualization techniques rely on a suitable timeline approach, which maps the time dimension to the space dimension. The system has been tested in a real working environment. We demonstrate its effectiveness by discussing use cases on real suspicious patterns and the results of experiments conducted with expert tax officers on real data. Furthermore, we performed a qualitative evaluation to get additional insights about advantages and limits of our system.