Detecting suspicious entities in Offshore Leaks networks

Abstract

The ICIJ Offshore Leaks Database represents a large set of relationships between people, companies, and organizations involved in the creation of offshore companies in tax-heaven territories, mainly for hiding their assets. This data are organized into four networks of entities and their interactions: Panama Papers, Paradise Papers, Offshore Leaks, and Bahamas Leaks. For instance, the entities involved in the Panama Papers networks are people or companies that had affairs with the Panamanian offshore law firm Mossack Fonseca, often with the purpose of laundering money. In this paper, we address the problem of searching the ICIJ Offshore Leaks Database for people and companies that may be involved in illegal acts. We use a collection of international blacklists of sanctioned people and organizations as ground truth for bad entities. We propose a new ranking algorithm, named Suspiciousness Rank Back and Forth (SRBF), that, given one of the networks in the ICIJ Offshore Leaks Database, leverages the network structure and the blacklist ground truth to assign a degree of suspiciousness to each entity in the network. We experimentally show that our algorithm outperforms existing techniques for node classification achieving area under the ROC curve ranging from 0.69 to 0.85 and an area under the recall curve ranging from 0.70 to 0.84 on three of the four considered networks. Moreover, our algorithm retrieves bad entities earlier in the rank than competitors. Further, we show the effectiveness of SRBF on a case study on the Panama Papers network.