Validating Bad Entity Ranking in the Panama Papers via Open-Source Intelligence
The Panama Papers network maintained by the International Consortium of Investigative Journalists (ICIJ) represents a large set of relationships between people, companies, and organizations involved in the creation of offshore companies in tax-haven territories, mainly for hiding their assets. The Panama Papers network includes people or companies that had affairs with the Panamanian offshore law firm Mossack Fonseca, often with the purpose of laundering money. In our previous work, we proposed a ranking algorithm, namely the Suspiciousness Rank Back and Forth (SRBF) algorithm, that, given the Panama Papers network, leverages a blacklist of known bad entities to assign a degree of suspiciousness to each entity in the network. This algorithm proved to be efficient in detecting known bad entities in the Panama Papers, but we were not able to verify the accuracy of the produced entity ranking for non-blacklisted entities.
In this paper, we propose to use the open-source intelligence (OSINT) methodology as a modern derivative of classical ethnographic and archaeological research methods that help us in validating with external open source data the ranking result of the Suspiciousness Rank Back and Forth algorithm. More specifically, we conduct a parallel, but independent, investigation using OSINT to assess the claims of SRBF algorithm. We identify positive outcomes from this study, describe current gaps in our process, and propose solutions to the gaps in order to better integrate the OSINT methodology with the SRBF ranking approach.
Winiecki, Donald; Kappelman, Katherine; Hay, Bryant; Joaristi, Mikel; Serra, Edoardo; and Spezzano, Francesca. (2020). "Validating Bad Entity Ranking in the Panama Papers via Open-Source Intelligence". 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 752-759. https://doi.org/10.1109/ASONAM49781.2020.9381389